Strategies

The following are the MOSERS strategies, listed by category. Information on each strategy, including the equations used, is included.

1.1 Transit System/Service Expansion and Replacement

Increase ridership by providing new rail system services and/or expanding bus services. Reduce emissions by replacing fleet with newer buses

Description

Expansion of a transit system or service can include the addition of rail services through increased frequency or route extension. Bus or paratransit services can be expanded with new vehicles and/or route extensions.

Application

Large cities or communities with enough population density to support reasonably frequent transit service.

Emissions Equations

Daily Emission Reduction (grams/day)=A+B−C−DDaily \space Emission \space Reduction\space (grams/day) = A + B – C – D A=VTR,P×TEFAUTO+VTR,OP×TEFAUTOA = VT_{R, P} \times TEF_{AUTO} + VT_{R, OP} \times TEF_{AUTO} Equation with Text

Reduction in auto start emissions from trips reduced

B=VMTR,P×EFAUTO,P+VMTR,OP×EFAUTO,OPB = VMT_{R, P} \times EF_{AUTO, P} + VMT_{R, OP} \times EF_{AUTO, OP} Equation with Text

Reduction in auto running exhaust emissions from VMT reductions

C=VTBUS,A×TEFBUS,A−VTBUS,B×TEFBUS,BC = VT_{BUS, A} \times TEF_{BUS, A} – VT_{BUS, B} \times TEF_{BUS,B} Equation with Text

Increase in emissions from additional bus starts

D=VMTBUS,P,A×EFBUS,P,A+VMTBUS,OP,A×EFBUS,OP,A−VMTBUS,P,B×EFBUS,P,B−VMTBUS,OP,B×EFBUS,OP,BD = VMT_{BUS, P, A} \times EF_{BUS, P, A} + VMT_{BUS, OP, A} \times EF_{BUS, OP, A} – VMT_{BUS, P, B} \times EF_{BUS, P, B} – VMT_{BUS, OP, B} \times EF_{BUS, OP, B} Equation with Text

Increase in emissions from additional bus running exhaust emissions

Variable
Unit
Definition
EFAUTO,PEF_{AUTO, P}

(grams/mile)

Speed-based running exhaust emission factor for affected roadway before implementation (NOx, VOC, PM, or CO) during peak hours

EFAUTO,OPEF_{AUTO, OP}

(grams/mile)

Speed-based running exhaust emission factor for affected roadway before implementation (NOx, VOC, PM, or CO) during off-peak hours

EFBUS,P,AEF_{BUS, P, A}

(grams/mile)

Speed-based running exhaust emission factor for transit vehicle (NOx, VOC, PM, or CO) during peak hours – after

EFBUS,P,BEF_{BUS, P, B}

(grams/mile)

Speed-based running exhaust emission factor for transit vehicle (NOx, VOC, PM, or CO) during peak hours – before (Applicable only for transit replacement)

EFBUS,OP,AEF_{BUS, OP, A}

(grams/mile)

Speed-based running exhaust emission factor for transit vehicle (NOx, VOC, PM, or CO) during off-peak hours – after

EFBUS,OP,BEF_{BUS, OP, B}

(grams/mile)

Speed-based running exhaust emission factor for transit vehicle (NOx, VOC, PM, or CO) during off-peak hours – before (Applicable only for transit replacement)

TEFAUTOTEF_{AUTO}

(grams/trip)

Auto trip-end emission factor (NOx, VOC, PM, or CO)

TEFBUS,ATEF_{BUS, A}

(grams/trip)

Bus (or other transit vehicle) trip-end emission factor (NOx, VOC, PM, or CO) – after

TEFBUS,BTEF_{BUS, B}

(grams/trip)

Bus (or other transit vehicle) trip-end emission factor (NOx, VOC, PM, or CO) – before (Applicable only for transit replacement)

VMTBUS,P,AVMT_{BUS, P, A}

VMT by transit vehicle during peak hours – after

VMTBUS,P,BVMT_{BUS, P, B}

VMT by transit vehicle during peak hours – before (Applicable only for transit replacement)

VMTBUS,OP,AVMT_{BUS, OP, A}

VMT by transit vehicle during off-peak hours

VMTBUS,OP,BVMT_{BUS, OP, B}

VMT by transit vehicle during off-peak hours – before (Applicable only for transit replacement)

VMTR,PVMT_{R, P}

Reduction in automobile VMT during peak hours

VMTR,OPVMT_{R, OP}

Reduction in automobile VMT during off-peak hours

VTBUS,AVT_{BUS, A}

(trips)

Vehicle trips by bus or other transit vehicle – after

VTBUS,BVT_{BUS, B}

(trips)

Vehicle trips by bus or other transit vehicle – before

VTR,PVT_{R, P}

(trips)

Reduction in number of automobile vehicle trips during peak hours

VTR,OPVT_{R, OP}

(trips)

Reduction in number of daily automobile vehicle trips during off-peak hours

Source
Texas A&M Transportation Institute

Activity Methodologies

For transit trips and transit VMT

VTBUS,A=VTBUS,P,A+VTBUS,OP,AVT_{BUS,A}=VT_{BUS,P,A}+VT_{BUS,OP,A} VTBUS,B=VTBUS,P,B+VTBUS,OP,BVT_{BUS,B}=VT_{BUS,P,B}+VT_{BUS,OP,B} VTBUS,P,A=1HBUS,P,A×hP,AVT_{BUS,P,A}=\frac{1}{H_{BUS,P,A}}\times h_{P,A} VTBUS,OP,A=1HBUS,OP,A×hOP,AVT_{BUS,OP,A}=\frac{1}{H_{BUS,OP,A}}\times h_{OP,A} VTBUS,P,B=1HBUS,P,B×hP,BVT_{BUS,P,B}=\frac{1}{H_{BUS,P,B}}\times h_{P,B} VTBUS,OP,B=1HBUS,OP,B×hOP,BVT_{BUS,OP,B}=\frac{1}{H_{BUS,OP,B}}\times h_{OP,B} VMTBUS,P,A=VTBUS,P,A×LBUS,AVMT_{BUS,P,A}=VT_{BUS,P,A}\times L_{BUS,A} VMTBUS,OP,A=VTBUS,OP,A×LBUS,AVMT_{BUS,OP,A}=VT_{BUS,OP,A}\times L_{BUS,A} VMTBUS,P,B=VTBUS,P,B×LBUS,BVMT_{BUS,P,B}=VT_{BUS,P,B}\times L_{BUS,B} VMTBUS,OP,B=VTBUS,OP,B×LBUS,BVMT_{BUS,OP,B}=VT_{BUS,OP,B}\times L_{BUS,B}

For auto trips and auto VMT

VTR=VTR,P+VTR,OPVT_{R}=VT_{R,P}+VT_{R,OP} VMTR=VMTR,P+VTR,OPVMT_{R}=VMT_{R,P}+VT_{R,OP} R=RP+ROPR=R_{P}+R_{OP} RP=R×FP×hPR_{P}=R\times F_{P} \times h_{P} ROP=R×FOP×hOPR_{OP}=R\times F_{OP} \times h_{OP} VTR,P=RP×rR,A×(1−PC)OA+RP×rR,A×PCOCVT_{R,P}=\frac{R_{P}\times r_{R,A} \times (1-P_{C})}{O_{A}}+\frac{R_{P} \times r_{R,A} \times P_{C}}{O_{C}} VTR,OP=ROP×rR,A×(1−PC)OA+ROP×rR,A×PCOCVT_{R,OP}=\frac{R_{OP}\times r_{R,A} \times (1-P_{C})}{O_{A}}+\frac{R_{OP} \times r_{R,A} \times P_{C}}{O_{C}} VMTR,P=VTR,P×LAVMT_{R,P}=VT_{R,P} \times L_{A} VMTR,OP=VTR,OP×LAVMT_{R,OP}=VT_{R,OP} \times L_{A}

Methodology and Assumptions
The calculator is designed to evaluate the benefits of providing corridor-level new transit service, and the area-wide or system-wide improvements can be estimated by summing the individual benefits of each corridor together. It estimates the daily activity benefits including the difference between services adapting to peak-hour and off-peak hour demand.

The calculator requires the basic information of proposed new transit/transit replacement service such as the headways, corridor length, service hours, average speed along the corridor, and estimated daily ridership which could be obtained from local transit agency. Calculator also requires information of auto trips within the covered area of new transit service such as average auto trip length, and average speed of these auto trips. The calculator also asks for the model year distribution of the transit services in the before and after case. The before values can be zero if it is new service. The sum of all distributions must add up to 1. The calculator assumes that both the area-wide average transit speeds and average auto speeds remain the same before and after applying the strategies. The default auto occupancy is at the national average level and default carpool occupancy is based on Katy freeway data collection. It is assumed that the 50 percent of transit riders who would travel by auto vehicles carpooled. In addition, it uses the DART peak-hour ridership factor as default value. The values of default parameters are suitable for most of the situations, but it is still recommended to use local specific data if they are available.

This methodology first calculates the additional peak-hour transit trips using the user input for peak-hour transit headway and the number of peak hours for both before and after case. The additional off-peak-hour transit trips are calculated using the user input for off-peak-hour transit headway and the number of off-peak hours. The transit VMT in peak hours or off-peak hours of transit trips during the period and the length of transit corridor. The peak-hour ridership and off-peak hour ridership are estimated based on the input of daily transit ridership and default peak-hour ridership factor. The reduction of auto trips come from the number of transit riders who were previously single occupancy auto drivers or carpooled. The calculator requires the ridership and estimated percentage of these transit riders to estimate the reduction of auto trips in both peak hours and off-peak hours. The reduced auto VMT is the product of the number of these auto trips and the average trip length.

Input Variable
Unit
Definition
HBUS,P,AH_{BUS, P, A}

(minutes/vehicle)

Proposed Average Headway during Peak Hours.
Input the estimated average transit headway during local peak hours.

HBUS,OP,AH_{BUS, OP, A}

(minutes/vehicle)

Proposed Average Headway during Off-Peak Hours.
Input the estimated average transit headway during local off-peak hours.

HBUS,P,BH_{BUS, P, B}

(minutes/vehicle)

Existing Average Headway during Peak Hours.
Input the estimated average transit headway during local peak hours.

HBUS,OP,BH_{BUS, OP, B}

(minutes/vehicle)

Existing Average Headway during Off-Peak Hours.
Input the estimated average transit headway during local off-peak hours.

LBUSL_{BUS}

(mile)

Proposed Transit Corridor Length.
Input the total length of the transit corridor.

LAL_{A}

(mile)

Average Auto Trip Length within the Buffer Distance of New Transit.
Input the length of the auto trips in transit covered area.

hp,Ah_{p,A}

(hour)

Proposed Service Hours during Peak Hours.
Input the transit service hours during local peak hours.

hOP,Ah_{OP,A}

(hour)

Proposed Service Hours during Off-Peak Hours.
Input the transit service hours during local off-peak hours.

hp,Bh_{p,B}

(hour)

Existing Service Hours during Peak Hours.
Input the transit service hours during local peak hours.

hOP,Bh_{OP,B}

(hour)

Existing Service Hours during Off-Peak Hours.
Input the transit service hours during local off-peak hours.

RR

(riders/day)

Estimated Typical Daily Transit Ridership.
Input the estimated daily transit ridership.

rR,Ar_{R,A}

(percent)

Percentage of Transit Riders Would be Auto Drivers.
Input the percentage of transit passengers who were auto drivers.

vB,Pv_{B,P}

(mph)

Estimated Transit Speed along the Corridor during Peak Hours.
Input the estimated transit speed along the corridor during local peak hours.

vA,Pv_{A,P}

(mph)

Current Auto Average Speed along the Corridor during Peak Hours.
Input the current auto speed on parallel arterial roads during local peak hours.

vB,OPv_{B,OP}

(mph)

Estimated Transit Speed along the Corridor during Off-Peak Hours.
Input the estimated transit speed along the corridor during local off-peak hours.

vA,OPv_{A,OP}

(mph)

Current Auto Average Speed along the Corridor during Off-Peak Hours.
Input the current auto speed on parallel arterial roads during local off-peak hours.

OAO_{A}

(persons/vehicle)

Average Auto Occupancy
Default value: 1.13%
Using the default auto occupancy or input local auto occupancy if data is available.

OCO_{C}

(persons/vehicle)

Carpool Occupancy
Default value: 2.31%
Using the default carpool occupancy or input local carpool occupancy if data is available.

PCP_{C}

(percent)

Percentage of Transit Riders Who Would be Auto Drivers were Carpooled.
Default value: 50%
Using the default or input local auto occupancy if data is available. The default data is presumed.

FPF_{P}

Peak Hour Ridership Factor
Using the default or input local auto occupancy if data is available.

FopF_{op}

Off-Peak Hour Ridership Factor
Using the default or input local auto occupancy if data is available.

Variable
Unit
Definition
VTBUS,P,AVT_{BUS,P,A}

Increased Number of Trips of Transit Vehicle during Peak Hours – after

VTBUS,OP,AVT_{BUS,OP,A}

Increased Number of Trips of Transit Vehicle during Off-Peak Hours – after

VTBUS,AVT_{BUS,A}

Proposed number of Daily Transit Vehicle Trips

VTBUS,P,BVT_{BUS,P,B}

Increased Number of Trips of Transit Vehicle during Peak Hours – (Applicable only for transit replacement)

VTBUS,OP,BVT_{BUS,OP,B}

Increased Number of Trips of Transit Vehicle during Off-Peak Hours – before (Applicable only for transit replacement)

VTBUS,BVT_{BUS,B}

Existing number of Daily Transit Vehicle Trips (Applicable only for transit replacement

VMTBUS,P,AVMT_{BUS,P,A}

Increased Number of Transit VMT during Peak Hours – after

VMTBUS,OP,AVMT_{BUS,OP,A}

Increased Number of Transit VMT during Off-Peak Hours – after

VMTBUS,P,BVMT_{BUS,P,B}

Increased Number of Transit VMT during Peak Hours – before (Applicable only for transit replacement)

VMTBUS,OP,BVMT_{BUS,OP,B}

Increased Number of Transit VMT during Off-Peak Hours – before (Applicable only for transit replacement)

VTR,PVT_{R,P}

Reduction in Number of Auto Vehicle Trips during Peak Hours

VTR,OPVT_{R,OP}

Reduction in Number of Auto Vehicle Trips during Off-Peak Hours

VTRVT_{R}

Reduction in Number of Daily Auto Vehicle Trips

VMTR,PVMT_{R,P}

Reduction in Number of Auto VMT during Peak Hours

VMTR,OPVMT_{R,OP}

Reduction in Number of Auto VMT during Off-Peak Hours

VMTRVMT_{R}

Reduction in Number of Daily Auto VMT

1.2 System/Service Operational Improvements

Increase ridership on existing transit systems.

Description

Operational improvements focus on enhancing the efficiency of a transit system and providing more effective service. These improvements are intended to attract new riders and reduce the number of vehicle trips. Improvements can be made, among others, in scheduling, routes, fleet maintenance programs, geographic coverage, improved mode transfer procedures, and monitoring operations.

Application

Cities and/or corridors with existing transit systems, new land development, limited parking, and heavy or increasing congestion.

Emissions Equations

Daily Emission Reduction (grams/day)=A+B−C−DDaily \space Emission \space Reduction \space (grams/day) = A + B – C – D
A=VTR,P×TEFAUTO+VTR,OP×TEFAUTOA = VT_{R, P} \times TEF_{AUTO} + VT_{R, OP} \times TEF_{AUTO} Equation with Text

Reduction in auto start emissions from trips reduced

B=VMTR,P×EFAUTO,P+VMTR,OP×EFAUTO,OPB = VMT_{R, P} \times EF_{AUTO, P} + VMT_{R, OP} \times EF_{AUTO, OP} Equation with Text

Reduction in auto running exhaust emissions from VMT reductions

C=VTBUS,P×TEFBUS+VTBUS,OP×TEFBUSC = VT_{BUS, P} \times TEF_{BUS} + VT_{BUS, OP} \times TEF_{BUS} Equation with Text

Increase in emissions from additional bus starts

D=VMTBUS×EFBUS,P+VMTBUS,OP×EFBUS,OPD = VMT_{BUS} \times EF_{BUS, P} + VMT_{BUS, OP} \times EF_{BUS, OP} Equation with Text

Increase in emissions from additional bus running exhaust emissions

Variable
Unit
Definition
EFAUTO,PEF_{AUTO, P}

(grams/mile)

Speed-based running exhaust emission factor for affected roadway before implementation (NOx, VOC, PM, or CO) during peak hours

EFAUTO,OPEF_{AUTO, OP}

(grams/mile)

Speed-based running exhaust emission factor for affected roadway before implementation (NOx, VOC, PM, or CO) during off-peak hours

EFBUS,PEF_{BUS, P}

(grams/mile)

Speed-based running exhaust emission factor for transit vehicle (NOx, VOC, PM, or CO) during peak hours

EFBUS,OPEF_{BUS, OP}

(grams/mile)

Speed-based running exhaust emission factor for transit vehicle (NOx, VOC, PM, or CO) during off-peak hours

TEFAUTOTEF_{AUTO}

(grams/trip)

Auto trip-end emission factor (NOx, VOC, PM, or CO)

TEFBUSTEF_{BUS}

(grams/trip)

Bus (or other transit vehicle) trip-end emission factor (NOx, VOC, PM, or CO)

Source
Texas A&M Transportation Institute

Advisory Meeting on Thursday, January 4, 2024 (tentative)

The next Advisory meeting will be on Thursday, January 4, 2024, from 10:00 AM to 11:00 AM Central.

TWG Meeting on Thursday, December 7, 2023 (tentative)

The next TWG meeting will be on Thursday, December 7, 2023, from 10:00 AM to 12:00 PM Central.

3.2 Bicycle and Pedestrian Programs – Option 1

Reduce vehicle trips, VMT, and emissions through provision of bicycle and pedestrian support facilities and programs.

Description

Employers provide support facilities and/or services to encourage employees to bicycle or walk to work. The programs include credits to be used toward purchases of bicycles; bonus days off; shower and locker facilities; free reflective vest, helmet, nightlight, and mirror; reduced-cost purchase program for bicycles; onsite bicycle repair shop with mechanics and pick-up service; and forgiveness for occasional tardiness. In a Washington, D.C., area program, employers must provide at least one bicycle for every 50 employees for midday employee business and personal use.

Bicycle and pedestrian programs can be classified in three different TCMs under the 1990 CAAA. In this instance, the program is employer based and is placed in this category. This is a clear example of the overlap found amid the various mobile source emission reduction strategies.

The calculator can be used to estimate the benefits of bike-ped programs for a corridor level, area-wide, or system-wide basis in populated areas. The calculator is designed to estimate the program’s impact where people choose to walk/bike over driving due to the improvement in the bike-ped program. Two optional methods are available to estimate daily activity and emission benefits of the program. Both options use the same emission equations but start with different input parameters. Option 1 uses a household-based estimation and Option 2 uses a facility need index-based estimation. Option 1 is described below. Option 2 is described on the corresponding strategy web page.

Option 1 – Household-based estimation

The methodology assumes that certain percentage of people are attracted to choose cycling or walking over driving vehicles when bike-ped facility is available. The bike-ped users are estimated from number of households, number of vehicles per households, and auto occupancy. Trips shifted to bike or walk reduces vehicle trips and associated VMT.

Application

Areas with existing bicycle and/or pedestrian paths that can serve businesses or business centers.

Emissions Equations

Daily Emission Reduction (grams/day)=A+BDaily \space Emission \space Reduction \space (grams/day) = A + B A=VTR×TEFAUTOA = VT_{R} \times TEF_{AUTO} Equation with Text

Reduction in auto start emissions from trips reduced

B=VMTR×EFBB = VMT_{R} \times EF_{B} Equation with Text

Reduction in auto running exhaust emissions from a reduction in vehicle miles traveled

Variable
Unit
Definition
EFBEF_{B}

(grams/mile)

Speed-based running exhaust emission factor for the average speed of participants trip before participating in the bike/pedestrian program (NOx, VOC, PM, or CO)

TEFAUTOTEF_{AUTO}

(grams/trip)

Auto trip-end emission factor (NOx, VOC, PM, or CO)

VMTRVMT_{R}

Reduction in daily auto vehicle miles traveled

VTRVT_{R}

trip

Reduction in number of daily auto vehicle trips

Source
CalTrans/CARB and FHWA Southern Resource Center (modified by Texas A&M Transportation Institute)

Activity Methodologies

VTP=NHH×nv×pp×npOautoVT_{P} = \frac {N_{HH} \times n_{v} \times p_{p} \times n_{p}}{O_{auto}}
VTOP=NHH×nv×pop×nopOautoVT_{OP} = \frac {N_{HH} \times n_{v} \times p_{op} \times n_{op}}{O_{auto}}
VMTP=VTP×LVVMT_{P} = VT_{P} \times L_{V}
VMTOP=VTOP×LVVMT_{OP} = VT_{OP} \times L_{V}
VTR=VTP+VTOPVT_{R} = VT_{P} + VT_{OP}
VMTR=VMTP+VMTOPVMT_{R} = VMT_{P} + VMT_{OP}

Methodology and Assumptions
Two optional methods are used to evaluate the benefits of bike-ped programs in populated area. The calculator could be applied to corridor level, area-wide or system-wide impact on trip mode choosing behaviors. The calculator is designed to estimate the program’s impact where people choose to walk/bike over driving due to the improvement in the bike-ped program. It estimates daily activity and emission benefits including the bike-ped program demand between peak-hours and off-peak hours.

The calculator requires the basic information such as the area type, area size and number of households or population of the service area where new bike-ped facility will be constructed. It also requires the trip information such as average trip length before having the bike-ped program, average number of trips during peak hours and off-peak hours. It also needs an estimated percentage of new program participants who previously were single occupancy drivers.

The approach assumes that the default auto occupancy is based on a national average level. The default average number of vehicles in households is based on Bureau of Transportation Statistic report from 2001. The values of default parameters are suitable for most situations, but it is still recommended to use local specific data if they are available.

The methodology assumes that certain percentage of people are attracted to choose cycling or walking over driving vehicles when bike-ped facility is available. The bike-ped users are estimated from number of households, number of vehicles per households, and auto occupancy. Trips shifted to bike or walk reduces vehicle trips and associated VMT.

Input Variable
Unit
Definition and Input Guidance
ppp_{p}

(percent)

Percentage of new cyclists/pedestrians who previously drove an SOV during peak hours
Input the percentage of program participants during peak hours

popp_{op}

(percent)

Percentage of new cyclists/pedestrians who previously drove an SOV during off-peak hours
Input the percentage of program participants during off-peak hours

NHHN_{HH}

(household)

Number of Households in Bike/Pedestrian Program Area
Input the number of households in the area of the bike and pedestrian program

npn_{p}

(trip)

Average Number of Trips per Participant during peak hours
Input the average number of trips per program participant during peak hours

nopn_{op}

(trip)

Average Number of Trips per Participant during off-peak hours
Input the average number of trips per program participant during off-peak hours

OautoO_{auto}

(persons/vehicle)

Average occupancy of auto
Default value: 1.13
Use the default or input local auto occupancy if data is available. User can make the default occupancy as 1 if there are only SOV vehicles

nvn_{v}

(vehicles/household)

Average Number of Vehicles per Household
Default value: 1.9
Use the default or input local data if available
The default value is based on Bureau of Transportation Statistics

Variable
Unit
Definition
VTPVT_{P}

(trip)

Peak Hour auto trip reduced

VTOPVT_{OP}

(trip)

Off-Peak Hours auto trip reduced

VMTPVMT_{P}

Peak Hours auto VMT reduced

VMTOPVMT_{OP}

Off-Peak Hours auto VMT reduced

VTRVT_{R}

(trip)

Reduction in number of daily vehicle trips

VMTRVMT_{R}

Reduction in daily auto vehicle miles traveled

LV,PL_{V,P}

(mile)

Average auto trip length of participants before participating in the bike/pedestrian program during peak hours

LV,OPL_{V,OP}

(mile)

Average auto trip length of participants before participating in the bike/pedestrian program during off-peak hours

3.2 Bicycle and Pedestrian Programs – Option 2

Reduce vehicle trips, VMT, and emissions through provision of bicycle and pedestrian support facilities and programs.

Description

Employers provide support facilities and/or services to encourage employees to bicycle or walk to work. The programs include credits to be used toward purchases of bicycles; bonus days off; shower and locker facilities; free reflective vest, helmet, nightlight, and mirror; reduced-cost purchase program for bicycles; onsite bicycle repair shop with mechanics and pick-up service; and forgiveness for occasional tardiness. In a Washington, D.C., area program, employers must provide at least one bicycle for every 50 employees for midday employee business and personal use.

Bicycle and pedestrian programs can be classified in three different TCMs under the 1990 CAAA. In this instance, the program is employer based and is placed in this category. This is a clear example of the overlap found amid the various mobile source emission reduction strategies.

The calculator can be used to estimate the benefits of bike-ped programs for a corridor level, area-wide, or system-wide basis in populated areas. The calculator is designed to estimate the program’s impact where people choose to walk/bike over driving due to the improvement in the bike-ped program. Two optional methods are available to estimate daily activity and emission benefits of the program. Both options use the same emission equations but start with different input parameters. Option 1 uses a household-based estimation and Option 2 uses a facility need index-based estimation. Option 1 is described on the corresponding strategy web page. Option 2 is described below.

Option 2 – Facility Needs Index Based Estimation

The method uses the facility needs index to show the percentage of people who would be attracted to choose cycling or walking over driving as their trip mode after having the bike-ped facility in the service zone. The bike-ped users are estimated from population, employment in the service zone, facility need index, geographic information, trip information, auto occupancy, and facility buffer distance. When trips are made by bike or walk, the vehicle trips and associated VMT are eliminated.

Application

Areas with existing bicycle and/or pedestrian paths that can serve businesses or business centers.

Emissions Equations

Daily Emission Reduction (grams/day)=A+BDaily \space Emission \space Reduction \space (grams/day) = A + B
A=VTR×TEFAUTOA = VT_{R} \times TEF_{AUTO} Equation with Text

Reduction in auto start emissions from trips reduced

B=VMTR×EFBB = VMT_{R} \times EF_{B} Equation with Text

Reduction in auto running exhaust emissions from a reduction in vehicle miles traveled

Variable
Unit
Definition
EFBEF_{B}

(grams/mile)

Speed-based running exhaust emission factor for the average speed of participants’ trip before participating in the bike/pedestrian program (NOx, VOC, PM, or CO)

TEFAUTOTEF_{AUTO}

(grams/trip)

Auto trip-end emission factor (NOx, VOC, PM, or CO)

VMTRVMT_{R}

Reduction in daily auto vehicle miles traveled

VTRVT_{R}

trip

Reduction in number of daily auto vehicle trips

Source
CalTrans/CARB and FHWA Southern Resource Center (modified by Texas A&M Transportation Institute)

Activity Methodologies

UB=(NP×IB+NE×IB)×LB×DBAU_{B}=(N_{P} \times I_{B} + N_{E} \times I_{B}) \times \frac{L_{B}\times D_{B}}{A}
UP=(NP×IP+NE×IP)×LP×DPAU_{P}=(N_{P} \times I_{P} + N_{E} \times I_{P}) \times \frac{L_{P}\times D_{P}}{A}
VTR=(UB+UP)×NOAVT_{R} =\frac{(U_{B} + U_{P}) \times N}{O_{A}}
VMTR=VTR×LVMT_{R}= VT_{R} \times L

Methodology and Assumptions
Two optional methods are used to evaluate the benefits of bike-ped programs in populated area. The calculator could be applied to corridor level, area-wide or system-wide impact on trip mode choosing behaviors. The calculator is designed to estimate the program’s impact where people choose to walk/bike over driving due to the improvement in the bike-ped program. It estimates daily activity and emission benefits including the bike-ped program demand between peak-hours and off-peak hours.

The calculator requires the basic information such as the area type, area size and number of households or population of the service area where new bike-ped facility will be constructed. It also requires the trip information such as average trip length before having the bike-ped program, average number of trips during peak hours and off-peak hours. It also needs an estimated percentage of new program participants who previously were single occupancy drivers.

The approach assumes that the default auto occupancy is based on a national average level. The default average number of vehicles in households is based on Bureau of Transportation Statistic report from 2001. The values of default parameters are suitable for most situations, but it is still recommended to use local specific data if they are available.

The method uses the facility needs index to show the percentage of people who would be attracted to choose cycling or walking over driving as their trip mode after having the bike-ped facility in the service zone. The bike-ped users are estimated from population, employment in the service zone, facility need index, geographic information, trip information, auto occupancy, and facility buffer distance. It must be noted that there must be no overlap between pedestrians and bicyclist when entering the values. The employment and population in the area are used as two exclusive contributions to the number of people participating in the program. When trips are made by bike or walk, the vehicle trips and associated VMT will be completely reduced.

Input Variable
Unit
Definition and Input Guidance
NPN_{P}

(person)

Estimated Population in the Service Zone
Input population in the service zone

NEN_{E}

(person)

Estimated Total Employment in the Service Zone
Input the employment in the service zone

IBI_{B}

Predicted Bicycle Needs Index (BNI) in the Service Zone
Input the estimated bicycle facility need index in the service zone

IPI_{P}

Predicted Pedestrian Needs Index (PNI) in the Service Zone
Input the estimated pedestrian facility need index in the service zone

AA

(square mile)

Area of the Service Zone
Input the area of the service zone

LBL_{B}

(mile)

Total Length of the Bicycle Facility in the Service Zone
Input the length of the bicycle facility in the service zone

LPL_{P}

(mile)

Total Length of the Pedestrian Facility in the Service Zone
Input the length of the pedestrian facility in the service zone

OAO_{A}

(person)

Auto Occupancy
Default value: 1.13
Use the default or input local auto occupancy if data is available. User can make default occupancy as 1 if there are only SOV vehicles.
The default value is based on Bureau of Transportation Statistics

DBD_{B}

(mile)

Bicycle Facility Buffer Distance
Default value: 2
Use the default or input local auto occupancy if data is available
The default value is based on NCTCOG bicycle/pedestrian equation

DPD_{P}

(mile)

Pedestrian Facility Buffer Distance
Default value: 0.5
Use the default or input local auto occupancy if data is available
The default value is based on NCTCOG bicycle/pedestrian equation

LL

(mile)

Average trip length in the service zone

NN

(trip/person/day)

Average number of trips per participant per day

Variable
Unit
Definition
UBU_{B}

(facility user)

Bicycle Facility Users in the Service Zone

UPU_{P}

(facility user)

Pedestrian Facility Users in the Service Zone

VTRVT_{R}

(trip)

Reduction in Number of Daily Vehicle Trips

VMTRVMT_{R}

Reduction in Daily Auto Vehicle Miles Traveled

2.1 Freeway High-Occupancy Vehicle (HOV) Facilities

Reduce emissions by decreasing VMT and increase average speeds on the lane.

Description

Separate lanes on controlled access highways are created for vehicles containing a specified minimum number of passengers. The lane may be concurrent flow, be barrier/buffer separated, or have a separate right-of-way.

HOV lanes reduce air pollution emissions by reducing running and trip-end emissions. Reductions in running emissions are derived by increasing average speeds in the HOV lanes. Conversion of HOV lanes to HOT lanes can also be modeled with the help of the percentage of travelers who are likely to shift to HOT lanes from General-purpose lanes.

Application

Highways in areas of traffic congestion with sufficient available right-of-way.

Emissions Equations

Daily Emission Reduction (grams/day)=A−B+CDaily \space Emission \space Reduction \space (grams/day) = A – B + C
A=VMTGP,B×EFGP,B+VMTH,B×EFH,BA = VMT_{GP, B} \times EF_{GP,B} + VMT_{H, B} \times EF_{H,B} Equation with Text

Running exhaust emissions from vehicles before implementation of HOV/HOT

B=VMTGP,A×EFGP,A+VMTH,A×EFH,AB = VMT_{GP, A} \times EF_{GP,A} + VMT_{H, A} \times EF_{H,A} Equation with Text

Running exhaust emissions from vehicles after implementation of HOV/HOT

C=VTR×TEF×2C = VT_{R} \times TEF \times 2 Equation with Text

Reduction in starts emissions due to trip reductions

Variable
Unit
Definition
EFGP,BEF_{GP,B}

(grams/mile)

Speed-based running exhaust emission factor on general purpose lane before implementation of HOV facility (NOx, VOC, PM, or CO)

EFGP,AEF_{GP, A}

(grams/mile)

Speed-based running exhaust emission factor on general purpose lanes after implementation of HOV facility (NOx, VOC, PM, or CO) (estimate)

EFH,BEF_{H, B}

(grams/mile)

Speed-based running exhaust emission factor on HOV facility before implementation of HOT facility (NOx, VOC, PM, or CO) (estimate)

EFH,AEF_{H, A}

(grams/mile)

Speed-based running exhaust emission factor on HOV facility after implementation of HOV/HOT facility (NOx, VOC, PM, or CO) (estimate)

TEFTEF

(grams/trip)

Trip-end emission factor for Auto

VMTGP,AVMT_{GP, A}

(vehicles/hour)

Vehicle miles traveled on general purpose lanes during peak hours after implementation of HOV/HOT facility

VMTGP,BVMT_{GP, B}

(vehicles/hour)

Vehicle miles traveled on general purpose lanes during peak hours before implementation of HOV/HOT facility

VMTH,BVMT_{H, B}

(vehicles/hour)

Vehicle miles traveled on HOV lanes during peak hours before implementation of HOT facility (Only for HOT conversion)

VMTH,AVMT_{H, A}

(vehicles/hour)

Vehicle miles traveled on HOV lanes during peak hours after implementation of HOV/HOT facility

VTRVT_{R}

(trips)

Number of vehicle trips reduced

Source
CalTrans (adapted by Texas A&M Transportation Institute)

Activity Methodologies

V/CGP,B=VGP,BCV/C_{GP,B} = \frac{V_{GP,B}}{C}
V/CGP,A=VGP,ACV/C_{GP,A} = \frac{V_{GP,A}}{C}
vGP,B based on speed−flow curvev_{GP,B} \space based \space on \space speed-flow \space curve
vGP,A based on speed−flow curvev_{GP,A} \space based \space on \space speed-flow \space curve
vH,B based on speed−flow curvev_{H,B} \space based \space on \space speed-flow \space curve
vH,A based on speed−flow curvev_{H,A} \space based \space on \space speed-flow \space curve
VMTGP,B=VGP,B×NPH×LVMT_{GP,B} = V_{GP,B} \times N_{PH} \times L
VMTGP,A=VGP,A×NPH×LVMT_{GP,A} = V_{GP,A} \times N_{PH} \times L
VMTH,B=VH,B×NPH×LVMT_{H,B} = V_{H,B} \times N_{PH} \times L
VMTH,A=VH,A×NPH×LVMT_{H,A} = V_{H,A} \times N_{PH} \times L
VTR=VH,B+VGP,B−VH,A−VGP,AVT_{R} = V_{H,B} + V_{GP,B} – V_{H,A} – V_{GP,A}

Methodology and Assumptions
The calculator is designed to evaluate the benefits of HOV/HOT facilities along the freeway corridor. It specifically calculates the corridor-level reductions in travel speed and vehicle miles traveled. The area-wide or system-wide improvements can be estimated by summing the individual benefits of each corridor. It estimates the activity and emission benefits in peak-hour since the HOV lanes are expected to open or beneficial during peak periods of the day.

The method requires the geographical information of the corridor, such as the area type, the corridor length, facility type, number of HOV lanes and the traffic information along the corridor, such as annual average daily traffic (AADT), peak-hour hourly traffic volume, and peak time of the day.

The calculator assumes there are three hours in morning peak period and three hours in evening peak period as the default data. The projected speeds are based on the speed-flow curves in the HCM for basic segments and managed lanes. The projected HOV lanes volume-capacity (V/C) ratio is presumed 0.9. The values of default parameters are suitable for most situations, but it is still recommended to use local specific data if available.

The module can be used for two purpose. First is new HOV facilities and second is conversion of HOV to HOT facility. The new facilities methodology first assumes that after introducing HOV lanes, some vehicles shift from general purpose lanes to HOV lanes. The numbers of vehicles shifting to HOV lanes are calculated based on projected V/C ratio and lane capacity. The calculator uses speed flow curves from HCM to calculate the speed on both the general purpose and the managed lanes before and after having HOV facility. In addition to corridor distance and free flow speed, the travel times along the corridor at free flow condition, before and after having HOV facility are computed. The calculator estimates vehicular travel time saving and vehicle miles traveled saving for peak hour period since the HOV lanes are expected to be beneficial only during peak periods of the day. For the HOT conversion, the percentage of travelers shifting to HOT lane from general purpose lanes is used to compute the additional volume on the HOV/HOT lane. The rest of the calculation remains the same.

Input Variable
Unit
Definition and Input Guidance
VGP,BV_{GP,B}

(vehicle/hour)

General Purpose Lane Vehicle Volume – Before

VGP,AV_{GP,A}

(vehicle/hour)

General Purpose Lane Vehicle Volume – After

VH,BV_{H,B}

(vehicle/hour)

HOV volume before implementation of HOT (NA for new HOV facilities)

VH,AV_{H,A}

(vehicle/hour)

Additional HOV Lane Volume after implementation of HOV/HOT facilities

LL

(mile)

Corridor Length
Input the length of corridor

NGPLN_{GPL}

Number of General Purpose Lanes
Input the numbers of general purpose lanes on roadway

AADTAADT

(vehicles/day)

Annual Average Daily Traffic along the facility
Input the annual average daily traffic during the day

VLaneV_{Lane}

Volume of Peak Hours
Input the traffic volume during local peak hours based on the AADT * Peak Hourly Factor / the number of lanes

NHOVN_{HOV}

Number of Additional HOV Lanes
Input the projected additional HOV lanes

NPHN_{PH}

(hour)

Peak Service Hours per Day
Using the default hour distribution or input local data if it is available (default: 3 hours for morning or evening, or 6 hours for morning and evening)

VPeakV_{Peak}

(vehicles/hour)

Peak-Hour Hourly Traffic Volume
Peak-hour hourly traffic volume based on the AADT * Peak Hourly Factor

CC

(vehicles/lane/hour)

Facility Capacity
Using the referenced default capacity or input local data if it is available
The value is based on the input of area type and facility type.

vFreeFlowv_{Free Flow}

(mph)

Facility Free Flow Speed
using the referenced default free flow speed or input local data if it is available
The value is based on the input of area type and facility type.

pHOV,GPp_{HOV,GP}

(percent)

Percent of high-occupancy vehicles along the facility
Default value: 20%
Input local data if available

pHOT,GPp_{HOT,GP}

(percent)

Percent of traveler shifting to HOT lane from General Purpose lanes
Default value: 20%
Input local data if available

V/CHV/C_{H}

Projected HOV Lanes Volume/Capacity Ratio
Default value: 0.90
Using the default projected volume/capacity ratio or input local data if it is available

Variable
Unit
Definition
V/CGP,BV/C_{GP,B}

General Purpose Lane V/C ratio – Before

V/CGP,AV/C_{GP,A}

General Purpose Lane V/C ratio – After

TTFreeFlowTT_{Free Flow}

(minute)

Travel Time Under Free-Flow Conditions

vGP,Av_{GP,A}

(mph)

General Purpose Lane Congested Speed – After

vGP,Bv_{GP,B}

(mph)

General Purpose Lane Congested Speed – Before

vH,Av_{H,A}

(mph)

HOV/HOT Lane Congested Speed – After

vH,Bv_{H,B}

(mph)

HOV Lane Congested Speed – Before (NA for new HOV facilities)

VMTGP,AVMT_{GP,A}

Vehicle Miles Traveled in GP lanes – After

VMTGP,BVMT_{GP,B}

Vehicle Miles Traveled in GP lanes – Before

VMTH,AVMT_{H,A}

Vehicle Miles Traveled in HOV Lane – After

VMTH,BVMT_{H,B}

Vehicle Miles Traveled in HOV Lane – Before (NA for new HOV facilities)

Resources

CMAQ Emissions Calculator Toolkit—Managed Lanes Tool
Federal Highway Administration (2020). CMAQ Toolkit—Managed Lanes Tool.

Determining level of service on freeways and multilane highways with higher speeds
Robertson, J., Fitzpatrick, K., Park, E.S., & Iragavarapu, V. (2014). Determining level of service on freeways and multilane highways with higher speeds. Transportation Research Record, 2461(1), 85-93.

Predicting High Occupancy Vehicle Lane Demand
Federal Highway Administration (1996), Description of methodology and micro-computer software model for quickly analyzing HOV lane demand and operations

CMAQ Performance Plan
HGAC (2018), Documentation on Congestion Mitigation and Air Quality (CMAQ) transportation funding for projects allocated in the Houston-Galveston region.

Progress North Texas 2019
NCTCOG (2019), Annual state of the region report for the NCTCOG region.

Evaluation of the Effectiveness of High Occupancy Vehicle Lanes
University of Utah (2004)

Simulated Travel Impacts of High-Occupancy Vehicle Lane Conversion Alternatives
Transportation Research Record (2001), A simulation model to analyze the effects of converting an existing high-occupancy vehicle (HOV) lane to either a high-occupancy toll (HOT) lane or a mixed-flow lane.

MOVES2014-Based Travel Demand Model Link Emissions Estimation Method
Texas A&M Transportation Institute (2020), Description of methodology and default values for delay calculation.

Determining Level of Service on Freeways and Multilane Highways with Higher Speeds
Texas A&M Transportation Institute (2014), Speed flow curves and breakpoint volumes for higher free flow speeds.

5.4 Intelligent Transportation Systems

Improve traffic speeds and reduce idling time through advanced traffic control systems and more efficient incident and corridor management.

Description

ITS combines the strengths of regional transportation planning models and traffic simulation models with overall transportation management strategies. It applies information technologies to the effective management of a traffic system and has received greater emphasis as a transportation planning concept since the Intermodal Surface Transportation Efficiency Act (ISTEA).

However, planners should be aware that some ITS methodologies require very detailed input data and complex computer models. Also, ITS entails potentially high costs to plan, implement, and utilize. Implementation of highway information management systems, from conceptual planning to the complete system, can require five to ten years.

Examples of ITS projects include transportation management centers. These centers contain closed-circuit monitors and many other data collection tools to observe traffic conditions. Cameras are placed along portions of freeways or arterials that commonly experience congestion difficulties during commute hours. These cameras enable personnel within the TMC to observe traffic and respond to situations in a timely manner, reducing the adverse effects on commuting traffic. TMCs serve as information and communication conduits between transportation personnel and law enforcement officials.

The Congestion Management System (CMS), a decision support tool, provides an integrated approach to planning by assessing information on all asset inventories, including condition and operational performance. Designed to assist decision makers in choosing cost-effective strategies and actions, CMS is a systematic approach to improving the efficiency of transportation assets. CMS is a tool for data management, analysis, and deficiency identification for all state highway assets, as well as local roadways. CMS uses historic, current, and forecasted attributes to support identification of current and future congested roadways. It also incorporates travel demand forecasting capabilities for urban and rural areas to assess transportation system performance and identify areas with unacceptable performance. Performance measures with localized thresholds allow CMS to address movement of people, vehicles, and goods based on goals and objectives of specific areas.

In areas where ITS solutions are being considered and evaluated, researchers have found at least one out of three conditions exists:

  • Cooperation and a partnership approach among all agencies involved in operating and enforcing laws on the transportation system.
  • Improved communication and coordination across geographic boundaries and between agencies. ITS is a metropolitan and regional solution and requires a high level of cooperation among entities to be effective. ITS cannot be achieved by a single agency.
  • Coordinated collection of data and use of information. ITS, especially TMCs, requires a larger amount of data collection, storage, and analysis than many agencies have previously amassed. Integration of the electronic systems that make up the different components is a key issue.

These conditions are considered preliminary but necessary steps that heighten awareness of the benefits of ITS solutions and allow for the consideration of ITS solutions. Without these conditions, planners should be cautious in considering ITS solutions as a MOSERS project in their area.

Application

Controlled-access highways and arterials.

Emissions Equations

Daily Emission Reduction (grams/day)=ADaily \space Emission \space Reduction \space (grams/day) = A
A=VMT×(EFB−EFA)A = VMT \times (EF_{B} – EF_{A})
Variable
Unit
Definition
VMTVMT

vehicles miles

Vehicle miles traveled along the corridor in a day

EFAEF_{A}

(grams/mile)

Speed-based running exhaust emission factor after implementation (NOx, VOC, PM, or CO)

EFBEF_{B}

(grams/mile)

Speed-based running exhaust emission factor before implementation (NOx, VOC, PM, or CO)

Source
Texas A&M Transportation Institute

Activity Methodologies

VMT=V×LVMT=V\times L

Methodology and Assumptions
The calculator is designed to evaluate the implementation of ITS strategies in a freeway.It specifically calculates the corridor-level reduction in emissions. The area-wide or system-wide improvements can be estimated by summing the individual benefits of each corridor.

The methodology requires user inputs such as the georgraphical properties of the roadway, the length of the corridor that will be affected by ITS, the annual average daily traffic in the corridor and the average speed of vehicles before and after implementation of the ITS strategies. The emissions benefits are calculated based on the speed difference between the pre-project and post-project average speed by multiplying the running emissions derived from these speeds with the VMT in the facility.The calculator is consistent with Section 5.1 in The Texas Guide to Accepted Mobile Source Emission Reduction Strategies (MOSERS) – Module 2.

Input Variable
Unit
Definition and Input Guidance
LL

miles

Input the corridor length

VV

vehicles/day

Input the annual average daily traffic along the facility

vBv_{B}

miles/hour

Input the pre-project speed along the facility

vAv_{A}

miles/hour

Input the post-project speed along the facility

Variable
Unit
Definition
VMTVMT

Vehicle miles traveled in the corridor affected by ITS

Resources

CMAQ Performance Plan
HGAC (2018), Documentation on Congestion Mitigation and Air Quality (CMAQ) transportation funding for projects allocated in the Houston-Galveston region.

MOVES2014 Statewide Non-Link On-Road Emissions Inventories for 2006, 2012, and 2018
Texas A&M Transportation Institute (2014), Documentation of methodology and default values for analyzing transportation infrastructure in Texas

5.1 Traffic Signalization – Signal Coordination

Reduce carbon monoxide (CO) and hydrocarbon (HC) emissions by decreasing vehicular stops and idling, which would in turn reduce travel times and traffic delays.

Description

Traffic signalization increases the efficiency of traffic flow at intersections by improving interconnection and coordination of signals, leading to reductions in travel times, delay, and stop-and-go driving. Traffic signalization can be as simple as updating equipment and/or software or improving the timing plan.

These projects are generally the most available tool for reducing congestion on local and arterial streets. Significant improvements in travel speed and/or time can be achieved.

Because signal improvements reduce travel times and stop-and-go driving conditions, they can measurably reduce CO and HC emissions as well as reduce fuel consumption. The effects on vehicular emissions, however, can be difficult to quantify. Although system-wide air quality benefits might be low, measurable benefits to local air quality and congestion relief are common in downtown areas and major activity sites or corridors.

Traffic signalization improvements may encourage additional traffic, increasing VMT. An increase in VMT along a roadway with improved traffic flow would offset some of the short-term air quality improvements generated by faster, more consistent travel speeds. Also, by reducing travel time on affected corridors, traffic signalization may attract additional vehicles and divert motorists from alternative modes of transportation.

The costs of a traffic signalization program will vary depending on the type of improvement and number of signals involved. Updating a signalized intersection requires a new traffic controller or traffic control software strategy. Timing plan improvements entail a labor-intensive data collection effort to determine new signal timings and subsequent re-timing of signals at each location. Signal coordination and interconnection require cable installation, as well as a series of controllers or a centralized computer-based master control system. To remove signals, a field survey must be performed to substantiate the elimination of the signals. Fieldwork is also necessary to remove the equipment.

Application

Major arterials or high-capacity roadways with uncoordinated traffic signals.

Emissions Equations

Daily Emission Reduction (grams/day)=A+BDaily \space Emission \space Reduction \space (grams/day) = A + B
A=VD,P×(EFB,P−EFA,P)×LA = V_{D, P} \times (EF_{B, P} – EF_{A, P}) \times L Equation with Text

Change in running exhaust emissions from improved traffic flow during the peak period

B=VD,OP×(EFB,OP−EFA,OP)×LB = V_{D, OP} \times (EF_{B, OP} – EF_{A, OP}) \times L Equation with Text

Change in running exhaust emissions from improved traffic flow during the off-peak period

Variable
Unit
Definition
EFA,OPEF_{A, OP}

(grams/mile)

Speed-based running exhaust emission factor during off-peak hours in affected corridor after implementation (NOx, VOC, PM, or CO)

EFA,PEF_{A, P}

(grams/mile)

Speed-based running exhaust emission factor during peak hours in affected corridor after implementation (NOx, VOC, PM, or CO)

EFB,OPEF_{B, OP}

(grams/mile)

Speed-based running exhaust emission factor during off-peak hours in affected corridor before implementation (NOx, VOC, PM, or CO)

EFB,PEF_{B, P}

(grams/mile)

Speed-based running exhaust emission factor during peak hours in affected corridor before implementation (NOx, VOC, PM, or CO)

LL

(mile)

Length of corridor affected by signalization project

VD,OPV_{D, OP}

(vehicles/day)

Average daily volume for the corridor during off-peak hours

VD,PV_{D, P}

(vehicles/day)

Average daily volume for the corridor during peak hours

Source
Texas A&M Transportation Institute

Activity Methodologies

VP=VD×KV_{P} = V_{D} \times K
VOP=VD−VD×K×hPhOPV_{OP} = \frac {V_{D} – V_{D}\times K\times h_{P}}{h_{OP}}
V/CP=VPC×N×2V/C_{P}=\frac {V_{P}}{C \times N \times 2}
V/COP=VOPC×N×2V/C_{OP}=\frac {V_{OP}}{C \times N \times 2}
DR,P=D1,P+D2,PD_{R,P}=D_{1,P}+D_{2,P}
D1,P=(1−F1,R)×NS×0.5×c×(1−g/c)21−(min(1,V/CP)×g/c)D_{1,P}=(1-F_{1,R})\times N_{S} \times \frac {0.5 \times c \times (1-g/c)^2}{1- (min(1,V/C_{P})\times g/c)}
F1,R=1−1.33×g/C1−g/CF_{1,R}= \frac {1-1.33 \times g/C}{1-g/C}

Considering Arrival Type 4

D2,Pa=900×T×((V/CP−1)+(V/CP−1)2+8×l×V/CP×pAc×T)D_{2,Pa}=900 \times T \times ((V/C_{P}-1)+ \sqrt{(V/C_{P}-1)^2+8\times l \times \frac{V/C_{P}\times p_{A}}{c\times T}})
D2,Pb=900×T×((V/CP−1)+(V/CP−1)2+8×l×V/CP×pBc×T)D_{2,Pb}=900 \times T \times ((V/C_{P}-1)+ \sqrt{(V/C_{P}-1)^2+8\times l \times \frac{V/C_{P}\times p_{B}}{c\times T}})
D2,P=D2,Pb−D2,PaD_{2,P}=D_{2,Pb}-D_{2,Pa}
DR,OP=D1,P+D2,PD_{R,OP}=D_{1,P}+D_{2,P}
D1,OP=(1−F1,R)×NS×0.5×c×(1−g/c)21−(min(1,V/COP)×g/c)D_{1,OP}=(1-F_{1,R})\times N_{S} \times \frac {0.5 \times c \times (1-g/c)^2}{1- (min(1,V/C_{OP})\times g/c)}
D2,OPa=900×T×((V/COP−1)+(V/COP−1)2+8×l×V/COP×pAc×T)D_{2,OPa}=900 \times T \times ((V/C_{OP}-1)+ \sqrt{(V/C_{OP}-1)^2+8\times l \times \frac{V/C_{OP}\times p_{A}}{c\times T}})
D2,OPb=900×T×((V/COP−1)+(V/COP−1)2+8×l×V/COP×pBc×T)D_{2,OPb}=900 \times T \times ((V/C_{OP}-1)+ \sqrt{(V/C_{OP}-1)^2+8\times l \times \frac{V/C_{OP}\times p_{B}}{c\times T}})
D2,OP=D2,OPb−D2,OPaD_{2,OP}=D_{2,OPb}-D_{2,OPa}
VD,P=VP×hPV_{D,P}=V_{P} \times h_{P}
VD,OP=VOP×hOPV_{D,OP}=V_{OP} \times h_{OP}
TTA,P=TTB,P−DR,PTT_{A,P}=TT_{B,P}-D_{R,P}
TTB,OP=(40.0025×L)+LvL×FA+NS×0.5×c×(1−g/c)21−(min(1,V/COP)×g/c)TT_{B,OP}=(\frac{4}{0.0025\times L})+\frac{L}{v_{L}}\times F_{A} +N_{S} \times \frac{0.5 \times c \times (1-g/c)^2}{1-(min(1,V/C_{OP})\times g/c)}
FA=21+(1−VOPN×vL)0.21F_{A} = \frac {2}{1+(1-\frac{V_{OP}}{N \times v_{L}})^{0.21}}
TTA,OP=TTB,OP−DR,OPTT_{A,OP}=TT_{B,OP}-D_{R,OP}
vB,P=LTTB,Pv_{B,P}=\frac{L}{TT_{B,P}}
vA,P=LTTA,Pv_{A,P}=\frac{L}{TT_{A,P}}
vB,OP=LTTB,OPv_{B,OP}=\frac{L}{TT_{B,OP}}
vA,OP=LTTA,OPv_{A,OP}=\frac{L}{TT_{A,OP}}

Methodology and Assumptions
The calculator is designed to evaluate the benefits of coordinating a group of two or more traffic signals to work together along the corridor. With signal coordination, vehicles are moving through these intersections with least number of stops possible and at a reasonable speed. The calculator specifically quantifies corridor level reductions in travel times and delay, and the area-wide or system-wide improvements which can be estimated by summing the benefits of each individual corridor together. It estimates the daily activity benefits including the different traffic volumes between peak hours and off-peak hours. In the calculator, it is assumed that the signal coordination strategy is set up for a corridor with a pattern of high morning inbound traffic and high afternoon outbound traffic, which means the corridor is always one-direction coordinated.

The calculator requires the geographical information of the corridor, such as the numbers of signals in coordination, the length, the number of the through lanes, and the traffic information along the corridor, such as annual average daily traffic (AADT), peak-hour hourly traffic volume, posted speed limit, peak-hour travel time and average cycle length of coordinated signals. If peak-hour hourly traffic data is not available, the calculator uses a default hourly traffic distribution to calculate hourly traffic volume using users specified AADT.

The approach assumed that there are six peak hours during each analysis day. The parameters in delay calculations are based on uniform delay formulas and incremental delay formulas in HCM 2010. The effective green ratio and roadway capacity are varied based on different facility types and referred from research outcomes from Texas A&M Transportation Institute. The values of default parameters are suitable for most situations, but it is still recommended to use local specific data if they are available to replace these parameters.

This calculator first estimates the hourly traffic volume during off-peak hours based on peak-hour hourly volume, number of peak hours and number of off-peak hours. The volume to capacity ratio of these two periods are then calculated. It is assumed in the calculator that the total delay is consisted of uniform delay and incremental delay. The total delay after signal coordination and the existing off-peak hour delay are calculated based on HCM delay algorithms. The differences in the delays before and after coordination is termed total travel time saving along the corridor. The calculator estimates vehicular travel time saving of peak hour period and off-peak hour period separately.

Input Variable
Unit
Definition and Input Guidance
vLv_{L}

(mph)

Posted Speed Limit
Input the speed limit of the corridor

cc

(second)

Existing Average Cycle Length along the Corridor
Input the average cycle length of coordinated signalizations along the corridor

F1,RF_{1,R}

Reduction Factor of Uniform Delay
Use the default reduction factor or input local factor

CC

(vehicle/hour/lane)

Facility Capacity per Lane
Use the default lane capacity or input local capacity

PBP_{B}

Incremental Delay Upstream Filtering or Metering Adjustment Factor for Isolated Intersection
Default value: 1
Use the default isolated incremental delay adjustment factor for upstream signal distance greater than 0.6 miles or input local factor

PAP_{A}

Incremental Delay Upstream Filtering or Metering Adjustment Factor for Isolated Intersection
Default value: 1
Use the default isolated incremental delay adjustment factor for upstream signal distance greater than 0.6 miles or input local factor

ll

Incremental Delay Adjustment for the Actuated Control
Default value: 0.5
Use the default delay adjustment for the actuated control or input local adjustment

g/cg/c

Average Effective Green Ratio
Use the default effective green ratio or input local ratio

hPh_{P}

Hours in Peak Period
Default value: 6
Use the default number of peak hours or input local numbers

hOPh_{OP}

Hours in Off-Peak Period
Default value: 18
Use the default number of off-peak hours or input local numbers

KK

Proportion of AADT Expected to Occur in the Design Hour (Peak Hour)
Use the default peak hour volume factor if local peak hour hourly traffic volume is not available

Variable
Unit
Definition
NSN_{S}

(signalized intersection)

Existing number of through lanes along the corridor (one direction)

NN

(lane)

Existing number of through lanes along the corridor (one direction)

V0V_{0}

(vehicles/day)

Annual average daily traffic (both directions)

TTB,PTT_{B,P}

(minute)

Existing average corridor travel time during peak period (one direction)

VPV_{P}

(vehicle/hour)

Average Hourly Volume During Peak Period (Both Directions)

VOPV_{OP}

(vehicle/hour)

Average Hourly Volume During Off-Peak Period (Both Directions)

V/CPV/C_{P}

Peak Period V/C Ratio

V/COPV/C_{OP}

Off-Peak Period V/C Ratio

D1,PD_{1,P}

(minute/vehicle)

Travel Time Savings Peak Period in Uniform Delay

D2,PD_{2,P}

(minute/vehicle)

Travel Time Savings Peak Period in Incremental Delay

DR,PD_{R,P}

(minute/vehicle)

Travel Time Savings Peak Period

TT

1 Hour

D1,OPD_{1,OP}

(minute/vehicle)

Travel Time Savings Off-Peak Period in Uniform Delay

D2,OPD_{2,OP}

(minute/vehicle)

Travel Time Savings Off-Peak Period in Incremental Delay

DR,OPD_{R,OP}

(minute/vehicle)

Travel Time Savings Off-Peak Period

VD,PV_{D,P}

Average Daily Volume for the Corridor during Peak Hours

VD,OPV_{D,OP}

Average Daily Volume for the Corridor during Off-Peak Hours

TTA,PTT_{A,P}

(minute)

Travel Time After Improvements during Peak Period

TTB,OPTT_{B,OP}

(minute)

Existing Average Corridor Travel Time during Off-Peak Period

TTA,OPTT_{A,OP}

(minute)

Travel Time After Improvements during Off-Peak Period

vB,Pv_{B,P}

(mph)

Average Speed during Peak Hours in Affected Corridor before Implementation

vA,Pv_{A,P}

(mph)

Average Speed during Peak Hours in Affected Corridor after Implementation

vB,OPv_{B,OP}

(mph)

Average Speed during Off-Peak Hours in Affected Corridor before Implementation

vA,OPv_{A,OP}

(mph)

Average Speed during Off-Peak Hours in Affected Corridor after Implementation

5.5 Railroad Grade Separation

Reduce congestion in corridors by reducing idling times and leading to lower emissions and improved traffic system efficiency.

Description

Railroad grade separations remove periodic traffic delays on major roadways by raising or lowering either the rail line or the roadway and permitting more efficient flow of traffic at major rail crossings.

This strategy can be a large-scale project and may require high costs in right-of-way (ROW) and construction. Close cooperation must be gained with the affected railroad company. The system-wide air quality benefits are low and difficult to predict. However, these programs should provide measurable reductions in localized CO and HC emissions. Delay time is eliminated at the rail grade separation.

Application

Arterials with delays caused by at-grade rail crossings

Emissions Equations

Daily Emission Reduction (grams/day)=A×BDaily \space Emission \space Reduction \space (grams/day)= A \times B
A=tH,C/tH×VA = t_{H, C} / t_{H} \times V Equation with Text

The number of vehicles affected by rail crossing delays

B=tc/2×EFIB = t_{c} / 2 \times EF_{I} Equation with Text

The average idling emissions resulting from affected traffic idling at the closed crossing (assumed to be half of the average time the roadway is closed per train crossing)

Variable
Unit
Definition
EFIEF_{I}

(grams/hour)

Idling emission factor (NOx, VOC, PM, or CO)

tct_{c}

(hours/crossing)

Average amount of time rail crossing is closed due to train crossing

tHt_{H}

(hour)

Duration of analysis period

tH,Ct_{H, C}

(hour)

Hours per analysis period roadway is closed due to train crossing

VV

(vehicles/analysis period)

Bi-directional arterial volume for analysis period

Source
Texas A&M Transportation Institute

5.6 New Signal

Reduce vehicle emissions by decreasing vehicular stops and idling, which would in turn reduce travel times and traffic delays.

Description

The calculator quantifies the impact of using signal control in place of stop-sign control on vehicle activities and on vehicular emissions at individual intersections. Traffic signalization increases the efficiency of traffic flow at intersections, leading to reductions in travel times, delay, and stop-and-go driving.

Application

Emissions Equations

Daily Emission Reduction (grams/day)=A+BDaily \space Emission \space Reduction \space (grams/day)= A + B
A=(DB,P−DA,P)×EFI×VD,PA = (D_{B,P} – D_{A,P}) \times EF_{I} \times V_{D,P} Equation with Text

Change in idling emissions from reduced vehicle delay during the peak period

B=(DB,OP−DA,OP)×EFI×VD,OPB = (D_{B,OP} – D_{A,OP}) \times EF_{I} \times V_{D,OP} Equation with Text

Change in idling emissions from reduced vehicle delay during the off-peak period

Variable
Unit
Definition
EFIEF_{I}

(grams/hour)

Idling emission factor (NOx, VOC, PM, CO, or CO2)

DB,PD_{B,P}

(minutes/vehicle)

Peak hour intersection delay before intersection improvement

DB,OPD_{B,OP}

(minutes/vehicle)

Off-peak hour intersection delay before intersection improvement

DA,PD_{A,P}

(minutes/vehicle)

Peak hour intersection delay after intersection improvement

DA,OPD_{A,OP}

(minutes/vehicle)

Off-peak hour intersection delay after intersection improvement

VD,PV_{D,P}

(vehicles/day)

Average daily volume for the corridor during peak hour

VD,OPV_{D,OP}

(vehicles/day)

Average daily volume for the corridor during off-peak hour

Activity Methodologies

VP,Major=VD,Major×KV_{P,Major}= V_{D,Major} \times K
VOP,Major=VD,Major−VP,Major×hPhOPV_{OP,Major} = \frac{V_{D,Major} – V_{P,Major} \times h_{P}}{h_{OP}}
VP,Minor=VD,Minor×KV_{P,Minor}= V_{D,Minor} \times K
VOP,Minor=VD,Minor−VP,Minor×hPhOPV_{OP,Minor} = \frac{V_{D,Minor} – V_{P,Minor} \times h_{P}}{h_{OP}}
V/CP,Major=VP,MajorCMajor×NMajorV/C_{P,Major} = \frac{V_{P,Major}}{C_{Major} \times N_{Major}}
V/COP,Major=VOP,MajorCMajor×NMajorV/C_{OP,Major} = \frac{V_{OP,Major}}{C_{Major} \times N_{Major}}
V/CP,Minor=VP,MinorCMinor×NMinorV/C_{P,Minor} = \frac{V_{P,Minor}}{C_{Minor} \times N_{Minor}}
V/COP,Minor=VOP,MinorCMinor×NMinorV/C_{OP,Minor} = \frac{V_{OP,Minor}}{C_{Minor} \times N_{Minor}}
g/cMinor=c−(2+3)×2c−g/cMajorg/c_{Minor} = \frac{c-(2+3) \times 2}{c} – g/c_{Major}

Assuming that the signal has two phases and each phase has 3 seconds of yellow time and 2 seconds of all red time

DA,P=DA,P,Major×VP,Major+DA,P,Minor×VP,MinorVP,Major+VP,Minor×160D_{A,P} = \frac {D_{A,P,Major} \times V_{P,Major} + D_{A,P,Minor} \times V_{P,Minor}}{V_{P,Major}+V_{P,Minor}} \times \frac{1}{60}
DA,P,Major=DA,1,P,Major+DA,2,P,MajorD_{A,P,Major} = D_{A,1,P,Major} + D_{A,2,P,Major}
DA,1,P,Major=0.5c×(1−g/cMajor)21−(min(1,V/CP,Major)×g/cMajor×160D_{A,1,P,Major} = \frac{0.5c \times (1 – g/c_{Major})^2}{1-(min(1,V/C_{P,Major}) \times g/c_{Major}} \times \frac{1}{60}
DA,2,P,Major=(900((V/CP,Major−1)+(V/CP,Major−1)2+8×l×V/CP,MajorCP,Major))×160D_{A,2,P,Major} = (900((V/C_{P,Major}-1)+ \sqrt{(V/C_{P,Major}-1)^2 + 8 \times l \times \frac{V/C_{P,Major}}{C_{P,Major}}})) \times \frac{1}{60}
DA,OP,Major=DA,1,OP,Major+DA,2,OP,MajorD_{A,OP,Major} = D_{A,1,OP,Major} + D_{A,2,OP,Major}
DA,1,OP,Major=0.5c×(1−g/cMajor)21−(min(1,V/COP,Major)×g/cMajor×160D_{A,1,OP,Major} = \frac{0.5c \times (1 – g/c_{Major})^2}{1-(min(1,V/C_{OP,Major}) \times g/c_{Major}} \times \frac{1}{60}
DA,2,OP,Major=(900((V/COP,Major−1)+(V/COP,Major−1)2+8×l×V/COP,MajorCOP,Major))×160D_{A,2,OP,Major} = (900((V/C_{OP,Major}-1)+ \sqrt{(V/C_{OP,Major}-1)^2 + 8 \times l \times \frac{V/C_{OP,Major}}{C_{OP,Major}}})) \times \frac{1}{60}
DA,P,Minor=DA,1,P,Minor+DA,2,P,MinorD_{A,P,Minor} = D_{A,1,P,Minor} + D_{A,2,P,Minor}
DA,1,P,Minor=0.5c×(1−g/cMinor)21−(min(1,V/CP,Minor)×g/cMinor×160D_{A,1,P,Minor} = \frac{0.5c \times (1 – g/c_{Minor})^2}{1-(min(1,V/C_{P,Minor}) \times g/c_{Minor}} \times \frac{1}{60}
DA,2,P,Minor=(900((V/CP,Minor−1)+(V/CP,Minor−1)2+8×l×V/CP,MinorCP,Minor))×160D_{A,2,P,Minor} = (900((V/C_{P,Minor}-1)+ \sqrt{(V/C_{P,Minor}-1)^2 + 8 \times l \times \frac{V/C_{P,Minor}}{C_{P,Minor}}})) \times \frac{1}{60}
DA,OP,Minor=DA,1,OP,Minor+DA,2,OP,MinorD_{A,OP,Minor} = D_{A,1,OP,Minor} + D_{A,2,OP,Minor}
DA,1,OP,Minor=0.5c×(1−g/cMinor)21−(min(1,V/COP,Minor)×g/cMinor×160D_{A,1,OP,Minor} = \frac{0.5c \times (1 – g/c_{Minor})^2}{1-(min(1,V/C_{OP,Minor}) \times g/c_{Minor}} \times \frac{1}{60}
DA,2,OP,Minor=(900((V/COP,Minor−1)+(V/COP,Minor−1)2+8×l×V/COP,MinorCOP,Minor))×160D_{A,2,OP,Minor} = (900((V/C_{OP,Minor}-1)+ \sqrt{(V/C_{OP,Minor}-1)^2 + 8 \times l \times \frac{V/C_{OP,Minor}}{C_{OP,Minor}}})) \times \frac{1}{60}
VD,P=(VP,Major+VP,Minor)×hPV_{D,P} = (V_{P,Major} + V_{P,Minor}) \times h_{P}
VD,OP=(VP,Major+VP,Minor)×hOPV_{D,OP} = (V_{P,Major} + V_{P,Minor}) \times h_{OP}
DA,P=DA,P,Major×VP,Major+DA,P,Minor×VP,MinorVP,Major+VP,MinorD_{A,P} = \frac {D_{A,P,Major} \times V_{P,Major} + D_{A,P,Minor} \times V_{P,Minor}}{V_{P,Major}+V_{P,Minor}}
DA,OP=DA,OP,Major×VOP,Major+DA,OP,Minor×VOP,MinorVOP,Major+VOP,MinorD_{A,OP} = \frac {D_{A,OP,Major} \times V_{OP,Major} + D_{A,OP,Minor} \times V_{OP,Minor}}{V_{OP,Major}+V_{OP,Minor}}

Methodology and Assumptions
The methodology is designed to evaluate the benefits of using traffic signals instead of all-way stop signs to control traffic flow at individual intersections. With traffic signals, vehicles are expected to move through the intersection at a higher average speed than with stop signs when traffic demand is high. The calculator estimates daily activity benefits during both peak-hours and off-peak hours.

The methodology requires the geographical information of the intersection (e.g., area type, type of roadway facilities, the number of lanes on major/minor approaches) and general traffic information (e.g., annual average daily traffic [AADT], the proportion of AADT expected to occur in the peak hour [K-Factor], and the cycle length of the proposed intersection signal).

The methodology assumes that the default peak hour intersection delay before improvement (all-way stop signs control) is 50 seconds per vehicle (0.83 minutes per vehicle) and off-peak hour delay before improvement is 25 seconds per vehicle (0.42 minutes per vehicle) which represent LOS F and LOS C, respectively. Also, the methodology assumes that there are 6 peak hours and 18 off-peak hours per day. K-factors are assigned by region (0.15 of the K-factor for rural areas and 0.10 of the K-factor for urban areas). The effective green ratios and roadway capacities vary by facility type based on previous research from the Texas A&M Transportation Institute. The parameters in the delay calculations are based on the uniform delay and the incremental delay formulas presented in the Highway Capacity Manual (HCM 2016). The values of default parameters are suitable for most of the situations, but it is still recommended to use local specific data if they are available.

The calculator for new traffic signalization projects is consistent with Section 5.1 in The Texas Guide to Accepted Mobile Source Emission Reduction Strategies (MOSERS) – Module 2. The methodology estimates activity changes in vehicle delay with the application of new signals to an un-signalized intersection.

This methodology first assumes that, after installation of traffic signal control, the vehicles are moving through the intersection with fewer stops and experiencing less delay. To quantify the delay after the new signal, the methodology uses the HCM’s uniform delay and incremental delay algorithms to calculate total delay at the intersection. The difference in the delays in the before and after analysis is due to the signal installation. The methodology estimates vehicular delay for peak hour and off-peak hour periods separately since the traffic volumes are significantly different between these two periods. The calculations of vehicle activities and delays are listed in equations below.

Input Variable
Unit
Definition and Input Guidance
VP,MajorV_{P,Major}

(vehicles/hour)

Peak hour volume – major street approaches (both directions)

VP,MinorV_{P,Minor}

(vehicles/hour)

Peak hour volume – minor street approaches (both directions)

VOP,MajorV_{OP,Major}

(vehicles/hour)

Off-peak hour volume – major street approaches (both directions)

VOP,MinorV_{OP,Minor}

(vehicles/hour)

Off-peak hour volume – minor street approaches (both directions)

V/CP,MajorV/C_{P,Major}

Major street V/C during peak hours

V/CP,MinorV/C_{P,Minor}

Minor street V/C during peak hours

V/COP,MajorV/C_{OP,Major}

Major street V/C during off-peak hours

V/COP,MinorV/C_{OP,Minor}

Minor street V/C during off-peak hours

DA,1,P,MajorD_{A,1,P,Major}

minutes/vehicle

Major street peak hour uniform delay after improvement

DA,2,P,MajorD_{A,2,P,Major}

minutes/vehicle

Major street peak hour incremental delay after improvement

DA,P,MajorD_{A,P,Major}

minutes/vehicle

Major street peak hour total delay after improvement

DA,1,OP,MajorD_{A,1,OP,Major}

minutes/vehicle

Major street off-peak hour uniform delay after improvement

DA,2,OP.MajorD_{A,2,OP.Major}

minutes/vehicle

Major street off-peak hour incremental delay after improvement

DA,OP,MajorD_{A,OP,Major}

minutes/vehicle

Major street off-peak hour total delay after improvement

DA,1,P,MinorD_{A,1,P,Minor}

minutes/vehicle

Minor street peak hour uniform delay after improvement

DA,2,P,MinorD_{A,2,P,Minor}

minutes/vehicle

Minor street peak hour incremental delay after improvement

DA,P,MinorD_{A,P,Minor}

minutes/vehicle

Minor street peak hour total delay after improvement

DA,1,OP,MinorD_{A,1,OP,Minor}

minutes/vehicle

Minor street off-peak hour uniform delay after improvement

DA,2,OP.MinorD_{A,2,OP.Minor}

minutes/vehicle

Minor street off-peak hour incremental delay after improvement

DA,OP,MinorD_{A,OP,Minor}

minutes/vehicle

Minor street off-peak hour total delay after improvement

Resources

Highway Capacity Manual, Sixth Edition: A Guide for Multimodal Mobility Analysis
Transportation Research Board (2016), Description of methodology and default values for analyzing the signalized intersection

2020 Roadway Design Manual
Texas Department of Transportation (2020), Documentation of methodology and default values for analyzing transportation infrastructure in Texas

MOVES2014 Statewide Non-Link On-Road Emissions Inventories for 2006, 2012, and 2018
Texas A&M Transportation Institute (2014), Documentation of methodology and default values for analyzing transportation infrastructure in Texas

5.6 Signalized Intersection Improvements I

Reduce vehicle emissions by decreasing vehicular stops and idling, which would in turn reduce travel times and traffic delays.

Description

The calculator quantifies the impact of general intersection improvements on vehicle activities and vehicular emissions. It evaluates the emission reduction benefits from the addition of intersection through lanes, shared left-turn lanes, or dedicated turn lanes. The general intersection improvements increase the efficiency of traffic flow at intersections by improving roadway capacity and interconnection, leading to reductions in travel times, delay, and stop-and-go driving.

Application

The approach requires user-specific local inputs for one intersection listed as shown in the table below.

Emissions Equations

Daily Emission Reduction (grams/day)=A+B+C+DDaily \space Emission \space Reduction \space (grams/day)= A + B + C + D
A=((DP−TR−before,1−DP−TR−after,1)×VD,P−TR,1+(DP−L−before,1−DP−L−after,1)×VD,P−L,1)×EFIA = ((D_{P-TR-before,1} – D_{P-TR-after,1} ) \times V_{D, P-TR,1} + (D_{P-L-before,1} – D_{P-L-after,1} ) \times V_{D, P-L,1}) \times EF_{I} Equation with Text

Change in idling emissions from reduced vehicle delay on Street 1 during the peak period

B=((DOP−TR−before,1−DOP−TR−after,1)×VD,OP−TR,1+(DOP−L−before,1−DOP−L−after,1)×VD,OP−L,1)×EFIB = ((D_{OP-TR-before,1} – D_{OP-TR-after,1} ) \times V_{D, OP-TR,1} + (D_{OP-L-before,1} – D_{OP-L-after,1} ) \times V_{D, OP-L,1}) \times EF_{I} Equation with Text

Change in idling emissions from reduced vehicle delay on Street 1 during the off-peak period

C=((DP−TR−before,2−DP−TR−after,2)×VD,P−TR,2+(DP−L−before,2−DP−L−after,2)×VD,P−L,2)×EFIC = ((D_{P-TR-before,2} – D_{P-TR-after,2} ) \times V_{D, P-TR,2} + (D_{P-L-before,2} – D_{P-L-after,2} ) \times V_{D, P-L,2}) \times EF_{I} Equation with Text

Change in idling emissions from reduced vehicle delay on Street 2 during the peak period

D=((DOP−TR−before,2−DOP−TR−after,2)×VD,OP−TR,2+(DOP−L−before,2−DOP−L−after,2)×VD,OP−L,2)×EFID = ((D_{OP-TR-before,2} – D_{OP-TR-after,2} ) \times V_{D, OP-TR,2} + (D_{OP-L-before,2} – D_{OP-L-after,2} ) \times V_{D, OP-L,2}) \times EF_{I} Equation with Text

Change in idling emissions from reduced vehicle delay on Street 2 during the off-peak period

Variable
Unit
Definition
EFIEF_{I}

(grams/hour)

Idling emission factor (NOx, VOC, PM, or CO)

DP−TR−before,iD_{P-TR-before,i}

(vehicles/hour)

Average delay, through and right-turn traffic before intersection improvement during peak hours

DP−L−before,iD_{P-L-before,i}

(vehicles/hour)

Average delay, left-turn traffic before intersection improvement during peak hours

DOP−TR−before,iD_{OP-TR-before,i}

(vehicles/hour)

Average delay, through and right-turn traffic before intersection improvement during off-peak hours

DOP−L−before,iD_{OP-L-before,i}

(vehicles/hour)

Average delay, left-turn traffic before intersection improvement during off-peak hours

DP−TR−after,iD_{P-TR-after,i}

(vehicles/hour)

Average delay, through and right-turn traffic after intersection improvement during peak hours

DP−L−after,iD_{P-L-after,i}

(vehicles/hour)

Average delay, left-turn traffic after intersection improvement during peak hours

DOP−TR−after,iD_{OP-TR-after,i}

(vehicles/hour)

Average delay, through and right-turn traffic after intersection improvement during off-peak hours

DOP−L−after,iD_{OP-L-after,i}

(vehicles/hour)

Average delay, left-turn traffic after intersection improvement during off-peak hours

VD,P−TR,iV_{D, P-TR,i}

(vehicles/day)

Average daily through and right-turn traffic volume during peak hours (both direction)

VD,P−L,iV_{D, P-L,i}

(vehicles/day)

Average daily left-turn traffic volume during peak hours (both direction)

VD,OP−TR,iV_{D, OP-TR,i}

(vehicles/day)

Average daily through and right-turn traffic volume during off-peak hours (both direction)

VD,OP−L,iV_{D, OP-L,i}

(vehicles/day)

Average daily left-turn traffic volume during off-peak hours (both direction)

Source
Texas A&M Transportation Institute

Resources

Highway Capacity Manual, Sixth Edition: A Guide for Multimodal Mobility Analysis
Transportation Research Board (2016), Description of methodology and default values for analyzing the signalized intersection

2011 Texas Manual on Uniform Traffic Control Devices (TMUTCD) – Revision 2
Texas Department of Transportation (2014), Documentation on the requirement of the traffic signal in Texas

MOVES2014 Statewide Non-Link On-Road Emissions Inventories for 2006, 2012, and 2018
Texas A&M Transportation Institute (2014), Documentation of methodology and default values for analyzing transportation infrastructure in Texas

5.6 Signalized Intersection Improvements II

Activity Methodologies

  • VP−TR,i=VD−TR,i×KV_{P-TR,i}= V_{D-TR,i} \times K

  • VP−L,i=VD−L,i×KV_{P-L,i}=V_{D-L,i} \times K

  • VOP−TR,i=VD−TR,i−(VP−TR,i×hP)hOPV_{OP-TR,i}=\frac{V_{D-TR,i} -(V_{P-TR,i}\times h_{P})}{h_{OP}}

  • VOP−L,i=VD−L,i−(VP−L,i×hP)hOPV_{OP-L,i}=\frac{V_{D-L,i} -(V_{P-L,i}\times h_{P})}{h_{OP}}

  • g/cpermit,Max=c−(Y+AR)×4cg/c_{permit,Max}= \frac{c-(Y+AR)\times 4}{c}

    if both Street 1 and Street 2 left turn are protected

  • g/cpermit,Max=c−(Y+AR)×3cg/c_{permit,Max}= \frac{c-(Y+AR)\times 3}{c}

    if the left turn is protected on either street and the other is permitted

  • g/cpermit,Max=c−(Y+AR)×2cg/c_{permit,Max}= \frac{c-(Y+AR)\times 2}{c}

    if both Street 1 and Street 2 left turn are permitted

  • g/cpermit−TR,2=g/cpermit,Max−g/cpermit,TR,1g/c_{permit-TR,2}=g/c_{permit,Max}-g/c_{permit,TR,1}

  • g/cpermit−TR,i=g/cpermit,TR,i×VP−TR,inT,i+nR,i+nSR,iVP−TR,inT,i+nR,i+nSR,i+VP−L,inL,i+nSL,ig/c_{permit-TR,i}=g/c_{permit,TR,i}\times \frac{\frac{V_{P-TR,i}}{n_{T,i}+n_{R,i}+n_{SR,i}}}{\frac{V_{P-TR,i}}{n_{T,i}+n_{R,i}+n_{SR,i}}+\frac{V_{P-L,i}}{n_{L,i}+n_{SL,i}}}

  • g/cprotect−L,i=max(6/c,g/cpermit−TR,i−g/cprotect−TR,i)g/c_{protect-L,i}=max(6/c,g/c_{permit-TR,i}-g/c_{protect-TR,i})

  • CT−before,i=CT,iC_{T-before,i}=C_{T,i}

    if left turn is permitted

  • CT−before,i=CT,i×g/cprotect−TR,ig/cpermit−TR,iC_{T-before,i}=C_{T,i} \times \frac{g/c_{protect-TR,i}}{g/c_{permit-TR,i}}

    if left turn is protected

  • CL−before,1=g/cprotect−TR,1×VP−TR,22e−VP−TR,22×4.5/36001−e−VP−TR,22×2.5/3600C_{L-before,1}=g/c_{protect-TR,1}\times \frac{\frac{V_{P-TR,2}}{2}e^{-\frac{V_{P-TR,2}}{2}\times 4.5/3600}}{1-e^{-\frac{V_{P-TR,2}}{2}\times 2.5/3600}}

    if left turn is permitted

  • CL−before,1=CT,1×fProtect−L×g/cprotect−L,1g/cpermit−TR,1C_{L-before,1}=C_{T,1}\times f_{Protect-L}\times \frac{g/c_{protect-L,1}}{g/c_{permit-TR,1}}

    if left turn is protected

  • CL−before,2=g/cprotect−TR,2×VP−TR,12e−VP−TR,12×4.5/36001−e−VP−TR,12×2.5/3600C_{L-before,2}=g/c_{protect-TR,2}\times \frac{\frac{V_{P-TR,1}}{2}e^{-\frac{V_{P-TR,1}}{2}\times 4.5/3600}}{1-e^{-\frac{V_{P-TR,1}}{2}\times 2.5/3600}}

    if left turn is permitted

  • CL−before,2=CT,2×fProtect−L×g/cprotect−L,2g/cpermit−TR,2C_{L-before,2}=C_{T,2}\times f_{Protect-L}\times \frac{g/c_{protect-L,2}}{g/c_{permit-TR,2}}

    if left turn is protected

  • CR−before,i=CT,i×fRC_{R-before,i}=C_{T,i}\times f_{R}

    if left turn is permitted

  • CR−before,i=CT,i×fR×g/cprotect−TR,ig/cpermit−TR,iC_{R-before,i}=C_{T,i}\times f_{R}\times \frac{g/c_{protect-TR,i}}{g/c_{permit-TR,i}}

    if left turn is protected

  • CTR−Total−before,i=CT−before,i×nT,i+CR−before,i×(nR,i+nSR,i)C_{TR-Total-before,i}=C_{T-before,i}\times n_{T,i}+C_{R-before,i}\times (n_{R,i}+n_{SR,i})

  • CL−Total−before,i=CL−before,i×(nL,i+nSL,i)C_{L-Total-before,i}=C_{L-before,i}\times (n_{L,i}+n_{SL,i})

  • CTR−Total−after,i=CT−before,i×(nT,i+nT,i′)+CR−before,i×(nR,i+nSR,i+nR,i′+nSR,i′)C_{TR-Total-after,i}=C_{T-before,i}\times (n_{T,i}+n_{T,i}’)+C_{R-before,i}\times (n_{R,i}+n_{SR,i}+n_{R,i}’+n_{SR,i}’)

  • CL−Total−after,i=CL−before,i×(nL,i+nSL,i+nL,i′+nSL,i′)C_{L-Total-after,i}=C_{L-before,i}\times (n_{L,i}+n_{SL,i}+n_{L,i}’+n_{SL,i}’)

  • For intersection calculation

  • V/CP−TR,before,i=VP−TR,i/CTR−Total−before,iV/C_{P-TR,before,i}=V_{P-TR,i}/C_{TR-Total-before,i}

  • V/CP−L,before,i=VP−L,i/CL−Total−before,iV/C_{P-L,before,i}=V_{P-L,i}/C_{L-Total-before,i}

  • V/COP−TR,before,i=VOP−TR,i/CTR−Total−before,iV/C_{OP-TR,before,i}=V_{OP-TR,i}/C_{TR-Total-before,i}

  • V/COP−L,after,i=VOP−L,i/CL−Total−after,iV/C_{OP-L,after,i}=V_{OP-L,i}/C_{L-Total-after,i}

  • V/CP−TR,after,i=VP−TR,i/CTR−Total−after,iV/C_{P-TR,after,i}=V_{P-TR,i}/C_{TR-Total-after,i}

  • V/CP−L,after,i=VP−L,i/CL−Total−after,iV/C_{P-L,after,i}=V_{P-L,i}/C_{L-Total-after,i}

  • V/COP−TR,after,i=VOP−TR,i/CTR−Total−after,iV/C_{OP-TR,after,i}=V_{OP-TR,i}/C_{TR-Total-after,i}

  • V/COP−L,after,i=VOP−L,i/CL−Total−after,iV/C_{OP-L,after,i}=V_{OP-L,i}/C_{L-Total-after,i}

  • Delay calculation

  • g/cTR,i=g/cPermit−TR,ig/c_{TR,i}=g/c_{Permit-TR,i}

    if left turn is permitted

  • g/cTR,i=g/cprotect−TR,ig/c_{TR,i}=g/c_{protect-TR,i}

    if left turn is protected

  • g/cL,i=g/cPermit−L,ig/c_{L,i}=g/c_{Permit-L,i}

    if left turn is permitted

  • g/cL,i=g/cprotect−L,ig/c_{L,i}=g/c_{protect-L,i}

    if left turn is protected

  • DP−TR,before,i=D1P−TR,before,i+D2P−TR,before,iD_{P-TR,before,i}=D1_{P-TR,before,i}+D2_{P-TR,before,i}

    Where:

  • D1P−TR,before,i=0.5×c×(1−g/cTR,i)21−(min(1,V/CP−TR,before,i)×g/cTR,i)D1_{P-TR,before,i}=\frac{0.5\times c \times (1-g/c_{TR,i})^{2}}{1-(min(1,V/C_{P-TR,before,i})\times g/c_{TR,i})}

  • D2P−TR,before,i=900×((V/CP−TR,before,i−1)+(V/CP−TR,before,i−1)2+8×l×V/CP−TR,before,iCTR−Total−before,i)D2_{P-TR,before,i}=900 \times ((V/C_{P-TR,before,i}-1)+ \sqrt{(V/C_{P-TR,before,i}-1)^{2}+8 \times l \times \frac{V/C_{P-TR,before,i}}{C_{TR-Total-before,i}}} \quad)

  • DP−L,before,i=D1P−L,before,i+D2P−L,before,iD_{P-L,before,i}=D1_{P-L,before,i}+D2_{P-L,before,i}

    Where:

  • D1P−L,before,i=0.5×c×(1−g/cL,i)21−(min(1,V/CP−L,before,i)×g/cL,i)D1_{P-L,before,i}=\frac{0.5\times c \times (1-g/c_{L,i})^{2}}{1-(min(1,V/C_{P-L,before,i})\times g/c_{L,i})}

  • D2P−L,before,i=900×((V/CP−L,before,i−1)+(V/CP−L,before,i−1)2+8×l×V/CP−L,before,iCL−Total−before,i)D2_{P-L,before,i}=900 \times ((V/C_{P-L,before,i}-1)+ \sqrt{(V/C_{P-L,before,i}-1)^{2}+8 \times l \times \frac{V/C_{P-L,before,i}}{C_{L-Total-before,i}}} \quad)

  • DOP−TR,before,i=D1OP−TR,before,i+D2OP−TR,before,iD_{OP-TR,before,i}=D1_{OP-TR,before,i}+D2_{OP-TR,before,i}

    Where:

  • D1OP−TR,before,i=0.5×c×(1−g/cTR,i)21−(min(1,V/COP−TR,before,i)×g/cTR,i)D1_{OP-TR,before,i}=\frac{0.5\times c \times (1-g/c_{TR,i})^{2}}{1-(min(1,V/C_{OP-TR,before,i})\times g/c_{TR,i})}

  • D2OP−TR,before,i=900×((V/COP−TR,before,i−1)+(V/COP−TR,before,i−1)2+8×l×V/COP−TR,before,iCTR−Total−before,i)D2_{OP-TR,before,i}=900 \times ((V/C_{OP-TR,before,i}-1)+ \sqrt{(V/C_{OP-TR,before,i}-1)^{2}+8 \times l \times \frac{V/C_{OP-TR,before,i}}{C_{TR-Total-before,i}}} \quad)

  • DOP−L,before,i=D1OP−L,before,i+D2OP−L,before,iD_{OP-L,before,i}=D1_{OP-L,before,i}+D2_{OP-L,before,i}

    Where:

  • D1OP−L,before,i=0.5×c×(1−g/cL,i)21−(min(1,V/COP−L,before,i)×g/cL,i)D1_{OP-L,before,i}=\frac{0.5\times c \times (1-g/c_{L,i})^{2}}{1-(min(1,V/C_{OP-L,before,i})\times g/c_{L,i})}

  • D2OP−L,before,i=900×((V/COP−L,before,i−1)+(V/COP−L,before,i−1)2+8×l×V/COP−L,before,iCL−Total−before,i)D2_{OP-L,before,i}=900 \times ((V/C_{OP-L,before,i}-1)+ \sqrt{(V/C_{OP-L,before,i}-1)^{2}+8 \times l \times \frac{V/C_{OP-L,before,i}}{C_{L-Total-before,i}}} \quad)

  • DP−TR,after,i=D1P−TR,after,i+D2P−TR,after,iD_{P-TR,after,i}=D1_{P-TR,after,i}+D2_{P-TR,after,i}

    Where:

  • D1P−TR,after,i=0.5×c×(1−g/cTR,i)21−(min(1,V/CP−TR,after,i)×g/cTR,i)D1_{P-TR,after,i}=\frac{0.5\times c \times (1-g/c_{TR,i})^{2}}{1-(min(1,V/C_{P-TR,after,i})\times g/c_{TR,i})}

  • D2P−TR,after,i=900×((V/CP−TR,after,i−1)+(V/CP−TR,after,i−1)2+4×V/CP−TR,after,iCTR−Total−after,i)D2_{P-TR,after,i}=900 \times ((V/C_{P-TR,after,i}-1)+ \sqrt{(V/C_{P-TR,after,i}-1)^{2}+4 \times \frac{V/C_{P-TR,after,i}}{C_{TR-Total-after,i}}} \quad)

  • DP−L,after,i=D1P−L,after,i+D2P−L,after,iD_{P-L,after,i}=D1_{P-L,after,i}+D2_{P-L,after,i}

    Where:

  • D1P−L,after,i=0.5×c×(1−g/cL,i)21−(min(1,V/CP−L,after,i)×g/cL,i)D1_{P-L,after,i}=\frac{0.5\times c \times (1-g/c_{L,i})^{2}}{1-(min(1,V/C_{P-L,after,i})\times g/c_{L,i})}

  • D2P−L,after,i=900×((V/CP−L,after,i−1)+(V/CP−L,after,i−1)2+4×V/CP−L,after,iCL−Total−after,i)D2_{P-L,after,i}=900 \times ((V/C_{P-L,after,i}-1)+ \sqrt{(V/C_{P-L,after,i}-1)^{2}+4 \times \frac{V/C_{P-L,after,i}}{C_{L-Total-after,i}}} \quad)

  • DOP−TR,after,i=D1OP−TR,after,i+D2OP−TR,after,iD_{OP-TR,after,i}=D1_{OP-TR,after,i}+D2_{OP-TR,after,i}

    Where:

  • D1OP−TR,after,i=0.5×c×(1−g/cTR,i)21−(min(1,V/COP−TR,after,i)×g/cTR,i)D1_{OP-TR,after,i}=\frac{0.5\times c \times (1-g/c_{TR,i})^{2}}{1-(min(1,V/C_{OP-TR,after,i})\times g/c_{TR,i})}

  • D2OP−TR,after,i=900×((V/COP−TR,after,i−1)+(V/COP−TR,after,i−1)2+4×V/COP−TR,after,iCTR−Total−after,i)D2_{OP-TR,after,i}=900 \times ((V/C_{OP-TR,after,i}-1)+ \sqrt{(V/C_{OP-TR,after,i}-1)^{2}+4 \times \frac{V/C_{OP-TR,after,i}}{C_{TR-Total-after,i}}} \quad)

  • DOP−L,after,i=D1OP−L,after,i+D2OP−L,after,iD_{OP-L,after,i}=D1_{OP-L,after,i}+D2_{OP-L,after,i}

    Where:

  • D1OP−L,after,i=0.5×c×(1−g/cL,i)21−(min(1,V/COP−L,after,i)×g/cL,i)D1_{OP-L,after,i}=\frac{0.5\times c \times (1-g/c_{L,i})^{2}}{1-(min(1,V/C_{OP-L,after,i})\times g/c_{L,i})}

  • D2OP−L,after,i=900×((V/COP−L,after,i−1)+(V/COP−L,after,i−1)2+4×V/COP−L,after,iCL−Total−after,i)D2_{OP-L,after,i}=900 \times ((V/C_{OP-L,after,i}-1)+ \sqrt{(V/C_{OP-L,after,i}-1)^{2}+4 \times \frac{V/C_{OP-L,after,i}}{C_{L-Total-after,i}}} \quad)

  • V/CP−TR,before,i=VP−TR,i/CTR−Total−before,iV/C_{P-TR,before,i}=V_{P-TR,i}/C_{TR-Total-before,i}

  • V/CP−L,before,i=VP−L,i/CL−Total−before,iV/C_{P-L,before,i}=V_{P-L,i}/C_{L-Total-before,i}

  • V/COP−TR,before,i=VOP−TR,i/CTR−Total−before,iV/C_{OP-TR,before,i}=V_{OP-TR,i}/C_{TR-Total-before,i}

  • V/COP−L,before,i=VOP−L,i/CL−Total−before,iV/C_{OP-L,before,i}=V_{OP-L,i}/C_{L-Total-before,i}

  • V/CP−TR,after,i=VP−TR,i/CTR−Total−after,iV/C_{P-TR,after,i}=V_{P-TR,i}/C_{TR-Total-after,i}

  • V/CP−L,after,i=VP−L,i/CL−Total−after,iV/C_{P-L,after,i}=V_{P-L,i}/C_{L-Total-after,i}

  • V/COP−TR,after,i=VOP−TR,i/CTR−Total−after,iV/C_{OP-TR,after,i}=V_{OP-TR,i}/C_{TR-Total-after,i}

  • V/COP−L,after,i=VOP−L,i/CL−Total−after,iV/C_{OP-L,after,i}=V_{OP-L,i}/C_{L-Total-after,i}

Methodology and Assumptions
The methodology is designed to evaluate the impacts of the improvements to the four-leg fixed-time signalized intersection including change of geometric design, approach lane redistribution, and increasing capacity with no change of signal timing or phasing plan. With these general intersection improvements, vehicles are expected to move through the intersection with less delay and at a reasonable speed. It estimates the daily activity benefits with the consideration of different impacts during peak hours and off-peak hours.

The methodology requires the geographical information of the intersection, such as area type (rural, small urban, or urban), type of roadway facilities (freeway or arterial), intersection geometry (number of lanes of all approaches), and the traffic information for all approaches, such as annual average daily traffic (AADT), left-turn phase types, and information of existing intersection signal. This includes left turn phase type such as protected left turn lanes—where drivers have a “green arrow”-protected left turn—or permitted left turn lanes—where drivers are permitted to turn left when clear without green arrow protection.

The methodology assumes there are in total of six peak hours every day. The left-turn factors, right-turn factors, and other parameters in the delay calculations are based on HCM 2016. The effective green-to-cycle ratio (g/c ratio) and roadway capacity vary based on the facility type selected based on previous research by Texas A&M Transportation Institute. The values of default parameters are suitable for most situations but it is still recommended to use local specific data if they are available.

The traffic signalization project approach is an expansion from Section 5.6 in The Texas Guide to Accepted Mobile Source Emission Reduction Strategies (MOSERS) – Module 2. The calculator estimates activity changes in delay-per-vehicle savings with the change of geometric design of individual intersection.

This methodology first estimates the volume and capacity of each street for both peak hours and off-peak hours. The lanes have been categorized to different lane groups based on the phases in signal timing plan. The critical volume-capacity ratio of each street—which is the maximum volume-capacity ratio among all the lane groups of the street—is determined by calculating delays before and after the intersection improvement project. The total delay consists of uniform delay and incremental delay. The difference of the delays between before and after scenarios is the delay benefit brought by the improvement. The calculations of vehicle activities and vehicle delays are listed in equations below.

Input Variable
Unit
Definition and Input Guidance
cc(seconds)Existing intersection signal cycle length
YY(seconds)Existing intersection signal yellow time length per phase
ARAR(seconds)Existing intersection signal all-red time length per phase
nT,in_{T,i}Existing numbers of through lanes on Street i
nL,in_{L,i}Existing numbers of exclusive left lanes on Street i
nR,in_{R,i}Existing numbers of exclusive right lanes on Street i
nSL,in_{SL,i}Existing numbers of shared left lanes on Street i
nSR,in_{SR,i}Existing numbers of shared right lanes on Street i
VD−TR,iV_{D-TR,i}(vehicles/day)Annual average daily traffic – Through and right-turn lane group (each direction)
VL,iV_{L,i}(vehicles/day)Annual Average daily traffic – Left-turn lane group (each direction)
nT,i′n_{T,i}’Expected increase/decrease in number of through lanes
nL,i′n_{L,i}’Expected increase/decrease in number of exclusive left-turn lanes
nR,i′n_{R,i}’Expected increase/decrease in number of exclusive right-turn lanes
nSL,i′n_{SL,i}’Expected increase/decrease in number of shared left-turn lanes
nSR,i′n_{SR,i}’Expected increase/decrease in number of shared right-turn lanes
fProtect−Lf_{Protect-L}Protected left turn factor
Default value: 0.95
fRf_{R}Right turn factor
Default value: 0.85
hPh_{P}Peak hours in a day
Default value: 6
hOPh_{OP}Off-peak hours in a day
Default value: 18
KKProportion of AADT Expected to Occur in the Design Hour (Peak Hour)
Use the default peak hour volume factor if local peak hour hourly traffic volume is not available
CT,iC_{T,i}(vehicles/hour/lane)Through lane capacity – permitted left turn on Street i
g/cPermit−TR,1g/c_{Permit-TR,1}Through lane effective green to cycle length ratio – permitted left turn, Street 1
Variable
Unit
Definition
VP−TR,iV_{P-TR,i}(vehicles/hour)Peak hour volume for the through and right-turn lane group in each direction
VP−L,iV_{P-L,i}(vehicles/hour)Peak hour volume for the left-turn lane group in each direction
VOP−TR,iV_{OP-TR,i}(vehicles/hour)Off-peak hour volume for the through and right-turn lane group in each direction
VOP−L,iV_{OP-L,i}(vehicles/hour)Off-peak hour volume for the left-turn lane group in each direction
g/cpermit,maxg/c_{permit,max}Maximum effective green to cycle length ratio
g/cpermit−TR,ig/c_{permit-TR,i}Existing through and right-turn lane group effective green to cycle length ratio for Street i when the left turn phase is permitted
g/cprotect−TR,ig/c_{protect-TR,i}Existing through and right-turn lane group effective green to cycle length ratio for Street i when the left turn phase is protected
g/cprotect−L,ig/c_{protect-L,i}Existing left-turn lane group effective green to cycle length ratio for Street i when the left turn phase is protected
CT−before,iC_{T-before,i}(vehicles/hour/lane)Through lane capacity for Street i before improvement
CL−before,iC_{L-before,i}(vehicles/hour/lane)Shared left-turn lane/exclusive left-turn lane capacity for Street i before improvement
CR−before,iC_{R-before,i}(vehicles/hour/lane)Shared right-turn lane/exclusive right-turn lane capacity for Street i before improvement
CTR−Total−before,iC_{TR-Total-before,i}(vehicles/hour)Existing through and right-turn lane group capacity for Street i before improvement
CL−Total−before,iC_{L-Total-before,i}(vehicles/hour)Existing left turn lane group capacity for Street i before improvement
CTR−Total−after,iC_{TR-Total-after,i}(vehicles/hour)Through and right-turn lane group capacity for Street i after improvement
CL−Total−after,iC_{L-Total-after,i}(vehicles/hour)Left turn lane group capacity for Street i after improvement
V/CP−TR,before,iV/C_{P-TR,before,i}Existing through and right-turn traffic V/C of Street i before intersection implementation during peak hours
V/CP−L,before,iV/C_{P-L,before,i}Existing left-turn traffic V/C of Street i for the intersection before intersection implementation during peak hours
V/COP−TR,before,iV/C_{OP-TR,before,i}Existing Through and Right-turn Traffic V/C of Street i Before Intersection Implementation during Off-Peak hours
V/COP−L,before,iV/C_{OP-L,before,i}Existing Left-turn traffic V/C of Street i before intersection implementation during off-peak hours
V/CP−TR,after,iV/C_{P-TR,after,i}Existing through and right-turn traffic V/C of Street i after intersection implementation during peak hours
V/CP−L,after,iV/C_{P-L,after,i}Existing Left-turn traffic V/C of Street i after intersection implementation during peak hours
V/COP−TR,after,iV/C_{OP-TR,after,i}Existing through and right-turn traffic V/C of Street i after intersection implementation during off-peak hours
V/COP−L,after,iV/C_{OP-L,after,i}Existing left-turn traffic V/C of Street i after intersection implementation during off-peak hours
DP−TR,before,iD_{P-TR,before,i}(hours/vehicle)Existing through and right-turn total delay of Street i before intersection implementation during peak hours
D1P−TR,before,iD1_{P-TR,before,i}(hours/vehicle)Existing through and right-turn uniform delay of Street i before intersection implementation during peak hours
D2P−TR,before,iD2_{P-TR,before,i}(hours/vehicle)Existing through and right-turn incremental delay of Street i before intersection implementation during peak hours
DP−L,before,iD_{P-L,before,i}(hours/vehicle)Existing left-turn total delay of Street i before intersection implementation during peak hours
D1P−L,before,iD1_{P-L,before,i}(hours/vehicle)Existing left -turn uniform delay of Street i before intersection implementation during peak hours
D1P−L,before,iD1_{P-L,before,i}(hours/vehicle)Existing left-turn incremental delay of Street i before intersection implementation during peak hours
DOP−TR,before,iD_{OP-TR,before,i}(hours/vehicle)Existing through and right-turn total delay of Street i before intersection implementation during off-peak hours
D1OP−TR,before,iD1_{OP-TR,before,i}(hours/vehicle)Existing through and right-turn uniform delay of Street i before intersection implementation during off-peak hours
D2OP−TR,before,iD2_{OP-TR,before,i}(hours/vehicle)Existing through and right-turn incremental delay of Street i before intersection implementation during off-peak hours
DOP−L,before,iD_{OP-L,before,i}(hours/vehicle)Existing left-turn total delay of Street i before intersection implementation during off-peak hours
D1OP−L,before,iD1_{OP-L,before,i}(hours/vehicle)Existing left-turn uniform delay of Street i before intersection implementation during off-peak hours
D2OP−L,before,iD2_{OP-L,before,i}(hours/vehicle)Existing left-turn incremental delay of Street i before intersection implementation during off-peak hours
Variable
Unit
Definition
DP−TR,after,iD_{P-TR,after,i}(hours/vehicle)Existing through and right-turn total delay of Street i after intersection implementation during peak hours
D1P−TR,after,iD1_{P-TR,after,i}(hours/vehicle)Existing through and right-turn uniform delay of Street i after intersection implementation during peak hours
D2P−TR,after,iD2_{P-TR,after,i}(hours/vehicle)Existing through and right-turn incremental delay of Street i after intersection implementation during peak hours
DP−L,after,iD_{P-L,after,i}(hours/vehicle)Existing left-turn total delay of Street i after intersection implementation during peak hours
D1P−L,after,iD1_{P-L,after,i}(hours/vehicle)Existing left -turn uniform delay of Street i after intersection implementation during peak hours
D2P−L,after,iD2_{P-L,after,i}(hours/vehicle)Existing left-turn incremental delay of Street i after intersection implementation during peak hours
DOP−TR,after,iD_{OP-TR,after,i}(hours/vehicle)Existing through and right-turn total delay of Street i after intersection implementation during off-peak hours
D1OP−TR,after,iD1_{OP-TR,after,i}(hours/vehicle)Existing through and right-turn uniform delay of Street i after intersection implementation during off-peak hours
D2OP−TR,after,iD2_{OP-TR,after,i}(hours/vehicle)Existing through and right-turn incremental delay of Street i after intersection implementation during off-peak hours
DOP−L,after,iD_{OP-L,after,i}(hours/vehicle)Existing left-turn total delay of Street i after intersection implementation during off-peak hours
D1OP−L,after,iD1_{OP-L,after,i}(hours/vehicle)Existing left-turn uniform delay of Street i after intersection implementation during off-peak hours
D2OP−L,after,iD2_{OP-L,after,i}(hours/vehicle)Existing left-turn incremental delay of Street i after intersection implementation during off-peak hours

5.7 Shoulder Lane Application

Reduce emissions by decreasing VMT and increase average speeds on the lane.

Description

Shoulder lanes on controlled access highways are created for vehicles during peak hours when demand heavily exceeds the highway capacity. The approach in this project evaluates the shoulder lane facility improvements that are parallel to a freeway.

Application

Highways in areas of traffic congestion with sufficient available right-of-way.

Emissions Equations

Daily Emission Reduction (grams/day)=A+BDaily \space Emission \space Reduction \space (grams/day) = A + B
A=VS,A×(EFB−EFS,A)×NPH×LA = V_{S, A} \times (EF_{B} – EF_{S, A}) \times N_{PH} \times L Equation with Text

Change in running exhaust emissions from vehicles shifting from general purpose lanes to shoulder lanes

B=((VGPL,B−VS,A)×EFB−VGPL,A×EFGP,A)×NPH×LB = ((V_{GPL, B} – V_{S,A}) \times EF_{B} – V_{GPL, A} \times EF_{GP, A}) \times N_{PH} \times L Equation with Text

Change in running exhaust emissions of vehicles in general purpose lanes as a result of vehicles shifted away from general purpose lanes

Variable
Unit
Definition
EFBEF_{B}(grams/mile)Speed-based running exhaust emission factor for affected roadway before implementation (NOx, VOC, PM, or CO)
EFGP,AEF_{GP, A}(grams/mile)Speed-based running exhaust emission factor after implementation of HOV facility (general purpose lanes) (NOx, VOC, PM, or CO) (estimate)
EFS,AEF_{S, A}(grams/mile)Speed-based running exhaust emission factor on shoulder lane facility (NOx, VOC, PM, or CO) (estimate)
LL(mile)Length of shoulder lane facility
NPHN_{PH}hourNumber of peak hours (AM and/or PM)
VGPL,AV_{GPL, A}(vehicle/hour)Average hourly volumes on general purpose lanes during peak hours after implementation of shoulder lane facility
VGPL,BV_{GPL, B}(vehicle/hour)Average hourly volumes on general purpose lanes during peak hours before implementation of shoulder lane facility
VS,AV_{S, A}(vehicle/hour)Average hourly volumes on shoulder lanes during peak hours

Source
CalTrans (adapted by Texas A&M Transportation Institute)

Activity Methodologies

  • VGPL,B=VLane×NGPLV_{GPL,B}=V_{Lane} \times N_{GPL}

  • VS,A=VGPL,BNGPL×CGPL+NS×CGPL×FR×NS×CGPL×FRV_{S,A}= \frac{V_{GPL,B}}{N_{GPL}\times C_{GPL} + N_{S}\times C_{GPL} \times F_{R}} \times N_{S} \times C_{GPL} \times F_{R}

  • VGPL,A=VGPL,B−VS,AV_{GPL,A} = V_{GPL,B} – V_{S,A}

  • V/CGPL,B=VGPL,BC×NGPLV/C_{GPL,B} = \frac {V_{GPL,B}}{C \times N_{GPL}}

  • V/CS,A=VS,AC×FR×NSV/C_{S,A} = \frac {V_{S,A}}{C \times F_{R} \times N_{S}}

  • V/CGPL,A=VGPL,AC×NGPLV/C_{GPL,A} = \frac {V_{GPL,A}}{C \times N_{GPL}}

  • TTFreeFlow=LvFreeFlowTT_{Free Flow}= \frac {L}{v_{Free Flow}}

  • TTGPL,B=TTFreeFlow+DGPL,BTT_{GPL,B}= TT_{Free Flow}+D_{GPL,B}

  • TTS,A=TTFreeFlow+DSTT_{S,A}=TT_{Free Flow}+D_{S}

  • TTGPL,A=TTFreeFlow+DGPL,ATT_{GPL,A}= TT_{Free Flow}+D_{GPL,A}

  • DGPL,B=Min(AeB(V/CGPL,B),M)D_{GPL,B}=Min(Ae^{B(V/C_{GPL,B})},M)

  • DS,A=Min(AeB(V/CS,A),M)D_{S,A}=Min(Ae^{B(V/C_{S,A})},M)

  • DGPL,A=Min(AeB(V/CGPL,A),M)D_{GPL,A}=Min(Ae^{B(V/C_{GPL,A})},M)

  • vGPL,B=LTTGPL,Bv_{GPL,B}=\frac{L}{TT_{GPL,B}}

  • vS,A=LTTS,Av_{S,A}= \frac{L}{TT_{S,A}}

  • vGPL,A=LTTGPL,Av_{GPL,A}=\frac{L}{TT_{GPL,A}}

  • VMTGPL,B=VGPL,B×NPH×LVMT_{GPL,B}=V_{GPL,B}\times N_{PH} \times L

  • VMTS,A=VS,A×NS×LVMT_{S,A}=V_{S,A} \times N_{S} \times L

  • VMTGPL,A=VGPL,A×NPH×LVMT_{GPL,A}=V_{GPL,A}\times N_{PH} \times L

Methodology and Assumptions
The calculator is designed to evaluate the benefits of adding shoulder lane facilities along the freeway corridor. The method specifically calculates the corridor level reductions in travel speed, and the area-wide or system-wide improvements can be estimated by summing the individual benefits of each corridor together. It estimates the activity and emission benefits only for peak hours since the shoulder lane facility is expected to be operating only in peak hours.

The method requires the geographical information of the corridor, such as the area type, the corridor length, facility type, number of shoulder lanes and the traffic information along the corridor, such as annual average daily traffic (AADT), peak-hour hourly traffic volume, and peak time of the day. If there is no hourly traffic data available, the calculator refers a default hourly traffic distribution which can be used to calculate hourly traffic with the input of AADT.

The approach assumes there are three hours in morning peak period and another three hours in evening peak period as the default data. The default auto occupancy on general purpose lanes and shoulder lanes is 1.13 which is based on summary of Travel Trends, 2009 National Household Travel Survey. The parameters in delay calculations and facility capacities are based on delay volume equations from previous research outcomes from Texas A&M Transportation Institute. The capacity adjusment factor for shoulder lane is presumed at 0.7088, considering the shoulder lane capacity as 1650 vehicles/lane from FHWA. The values of default parameters are suitable for most situations, but it is still recommended to use local specific data if they are available.

The calculator estimates activity changes in vehicle speeds and vehicle miles traveled with the application of shoulder lane facility. This methodology first assumes that after shoulder lane application, the roadway capacity is increased. The vehicles on other general purpose lanes can move along the corridor with fewer number of stops possible and at a reasonable higher speed. Therefore, the total delay experienced along the corridor is reduced. To quantify the delay before and after the shoulder lane application, the calculator uses delay algorithms from Texas state wide emission inventory document to calculate difference of the delays. With corridor distance and free flow speed, the corridor average speeds before and after are computed. The calculator estimates vehicular travel time saving and vehicle miles traveled saving of peak hour period only since the shoulder lanes are expected to open only during peak periods of the day.

Input Variable
Unit
Definition and Input Guidance
LL(mile)Shoulder L Length
Input the length of shoulder lane
NGPLN_{GPL}(lane)Number of General Purpose Lanes
Input the numbers of general purpose lanes on roadway
VLaneV_{Lane}(vehicles/hour/lane)Volumes of Peak Hours
Input the traffic volume during local peak hours
NSN_{S}(lane)Number of Additional Shoulder Lanes
Input the projected additional shoulder lanes
NPHN_{PH}(hour)Peak Service Hours per Day
Default value: 3 or 6
Use the default number of peak hours or input data
The value is based on the input of peak time of the day, 3 hours for either morning peak period or evening peak period, or 6 hours for both
CC(vehicles/hour/lane)Facility Capacity
Use the default roadway capacity of input local data
The value is based on the input of area type and facility type.
vFreeFlowv_{Free Flow}(mph)Facility Free Flow Speed
Use the default roadway free flow speed or input data
The value is based on the input of area type and facility type.
FRF_{R}Shoulder Lane Capacity Adjustment Factor
Default value: 0.7088
Use the default reduction factor or input local data
A,B,MA,B,M(minutes/mile)Delay Calculation Term A, B and M
Use the default delay terms or input local data
Variable
Unit
Definition
VGPL,BV_{GPL,B}(vehicle/hour)General Purpose Lane Vehicle Volume – Before
VS,AV_{S,A}(vehicle/hour)Additional Shoulder Lane Volume
VGPL,AV_{GPL,A}(vehicle/hour)General Purpose Lane Vehicle Volume – After
V/CGPL,BV/C_{GPL,B}General Purpose Lane Volume/Capacity ratio – Before
V/CS,AV/C_{S,A}Shoulder Lane Volume/Capacity ratio – After
V/CGPL,AV/C_{GPL,A}General Purpose Lane Volume/Capacity ratio – After
TTFreeFlowTT_{Free Flow}(minute)Travel Time Under Free-Flow Conditions
vGPL,Bv_{GPL,B}(mph)General Purpose Lane Congested Speed – Before
vS,Av_{S,A}(mph)Shoulder Lane Congested Speed – After
vGPL,Av_{GPL,A}(mph)General Purpose Lane Congested Speed – After
DGPL,BD_{GPL,B}(minute)General Purpose Lane Delay – Before
DS,AD_{S,A}(minutes/vehicle)Shoulder Lane Delay – After
DGPL,AD_{GPL,A}(minutes/vehicle)General Purpose Lane Delay – After
TTGPL,BTT_{GPL,B}(minutes/vehicle)General Purpose Lane Travel Time – Before
TTS,ATT_{S,A}(minute)Shoulder Lane Travel Time – After
TTGPL,ATT_{GPL,A}(minute)General Purpose Lane Travel Time – After
VMTGPL,BVMT_{GPL,B}Daily Peak Hour General Purpose Lane VMT – Before
VMTS,AVMT_{S,A}Daily Peak Hour Shoulder Lane VMT – After
VMTGPL,AVMT_{GPL,A}Daily Peak Hour General Purpose Lane VMT – After

Resources

Summary of Travel Trends, 2009 National Household Travel Survey
U.S. Department of Transportation (2011), Summary of travel trends from 2009 household travel survey

Use of Freeway Shoulders for Travel — Guide for Planning, Evaluating, and Designing Part-Time Shoulder Use as a Traffic Management Strategy
Federal Highway Administration (2021), Description of methodology and default values for shoulder lane approach

MOVES2014-Based Travel Demand Model Link Emissions Estimation Method
Texas A&M Transportation Institute (2020), Description of methodology and default values for delay calculation

5.8 Roundabouts

Reduce emissions by reducing idling time at intersection with roundabouts.

Description

Roundabouts are intersections that circulate traffic flow around a central island with yield on entry traffic control. Roundabouts smooth traffic from all directions with less average time spent idling, which may lead to less idling emissions.

Application

Arterials or low to medium-capacity roadways with traffic signals or stop signs. Equation is for individual intersections.

Emissions Equations

Daily Emission Reduction (grams/day)=A+BDaily \space Emission \space Reduction \space (grams/day) = A + B

  • A=(DB,P−DA,P)×EFI×VD,PA = (D_{B,P}-D_{A,P})\times EF_{I} \times V_{D,P}

    Change in idling emissions from reduced vehicle delay times during the peak period

  • B=(DB,OP−DA,OP)×EFI×VD,OPB = (D_{B,OP}-D_{A,OP})\times EF_{I} \times V_{D,OP}

    Change in idling emissions from reduced vehicle delay times during the off-peak period
Variable
Unit
Definition
DA,PD_{A,P}(hour)Average vehicle delay at intersection after implementation during peak hours
DB,PD_{B,P}(hour)Average vehicle delay at intersection before implementation during peak hours
DA,OPD_{A,OP}(hour)Average vehicle delay at intersection after implementation during off-peak hours
DB,OPD_{B,OP}(hour)Average vehicle delay at intersection before implementation during off-peak hours
EFIEF_{I}(grams/hour)Idling emission factor (NOx, VOC, PM, or CO)
VD,OPV_{D,OP}(vehicle/day)Average daily traffic volume for the corridor during off-peak hours
VD,PV_{D,P}(vehicle/day)Average daily traffic volume for the corridor during peak hours

Source
Federal Highway Administration Southern Research Center & Texas A&M Transportation Institute

Activity Methodologies

  • VP,H=VP,H,V11+T100V_{P,H}= \frac{V_{P,H,V}}{ \frac{1}{1 + \frac{T}{100}}}

  • VOP,H=VD−VP,H,V×NPNOP×11+T100V_{OP,H} = \frac{V_{D} – V_{P,H,V} \times N_{P}}{N_{OP} \times \frac{1}{1 + \frac{T}{100}}}

  • VC,P=(1−PRT,n−1)×VP,n−1+(PLT,n−2+PUT,n−2)×VP,n−2+PUT,n−3×VP,n−3V_{C,P} = (1 – P_{RT,n-1}) \times V_{P,n-1} + (P_{LT,n-2} + P_{UT,n-2}) \times V_{P,n-2} + P_{UT,n-3} \times V_{P,n-3}

  • VC,OP=(1−PRT,n−1)×VOP,n−1+(PLT,n−2+PUT,n−2)×VOP,n−2+PUT,n−3×VOP,n−3V_{C,OP} = (1 – P_{RT,n-1}) \times V_{OP,n-1} + (P_{LT,n-2} + P_{UT,n-2}) \times V_{OP,n-2} + P_{UT,n-3} \times V_{OP,n-3}

  • CP=1130×e−1×10−3×VC,P×N when NCL=1; CP=1130×e−0.7×10−3×VC,P when NCL=2,N=1;CP=1130×e−0.7×10−3×VC,P+1130×e−0.75×10−3×VC,P when NCL=2,N=2C_{P} = 1130 \times e^{-1 \times 10^{-3} \times V_{C,P}} \times N \space when \space N_{CL}=1; \space C_{P} = 1130 \times e^{-0.7 \times 10^{-3} \times V_{C,P}} \space when \space N_{CL}=2,N=1; C_{P} = 1130 \times e^{-0.7 \times 10^{-3} \times V_{C,P}} + 1130 \times e^{-0.75 \times 10^{-3} \times V_{C,P}} \space when \space N_{CL}=2,N=2

  • COP=1130×e−1×10−3×VC,OP×N when NCL=1; COP=1130×e−0.7×10−3×VC,OP when NCL=2,N=1;COP=1130×e−0.7×10−3×VC,OP+1130×e−0.75×10−3×VC,OP when NCL=2,N=2C_{OP} = 1130 \times e^{-1 \times 10^{-3} \times V_{C,OP}} \times N \space when \space N_{CL}=1; \space C_{OP} = 1130 \times e^{-0.7 \times 10^{-3} \times V_{C,OP}} \space when \space N_{CL}=2,N=1; C_{OP} = 1130 \times e^{-0.7 \times 10^{-3} \times V_{C,OP}} + 1130 \times e^{-0.75 \times 10^{-3} \times V_{C,OP}} \space when \space N_{CL}=2,N=2

  • DA,P=3600CP+900×(VP,HCP−1+(VP,HCP−1)2+3600×VP,HCP450×CP)+5×MIN(VP,HCP,1)D_{A,P} = \frac{3600}{C_{P}} + 900 \times (\frac{V_{P,H}}{C_{P}} – 1 + \sqrt{ (\frac{V_{P,H}}{C_{P}} – 1)^2 + \frac{3600 \times \frac{V_{P,H}}{C_{P}}}{450 \times C_{P}}}) + 5 \times MIN(\frac{V_{P,H}}{C_{P}},1)

  • DA,OP=3600COP+900×(VOP,HCOP−1+(VOP,HCOP−1)2+3600×VOP,HCOP450×COP)+5×MIN(VOP,HCOP,1)D_{A,OP} = \frac{3600}{C_{OP}} + 900 \times (\frac{V_{OP,H}}{C_{OP}} – 1 + \sqrt{ (\frac{V_{OP,H}}{C_{OP}} – 1)^2 + \frac{3600 \times \frac{V_{OP,H}}{C_{OP}}}{450 \times C_{OP}}}) + 5 \times MIN(\frac{V_{OP,H}}{C_{OP}},1)

  • DR,P=DB,P−DA,PD_{R,P} = D_{B,P} – D_{A,P}

  • DR,OP=DB,OP−DA,OPD_{R,OP} = D_{B,OP} – D_{A,OP}

  • VP=VP,H×NP×11+T100V_{P} = V_{P,H} \times N_{P} \times \frac{1}{1+\frac{T}{100}}

  • VOP=VOP,H×NOP×11+T100V_{OP} = V_{OP,H} \times N_{OP} \times \frac{1}{1+\frac{T}{100}}

Methodology and Assumptions
The methodology is designed to evaluate the benefits of constructing a roundabout in an existing intersection to reduce the overall control delay. With roundabouts, the traffic flow is expected to be higher and have lesser stops thereby reducing the idling emissions generated from the tailpipe of a vehicle. The calculator estimates daily benefits during both peak and off-peak hours.

The calculator requires the geographical information (eg: road type, area type, number of lanes on both major and minor approaches), general traffic infomtion for all the approaches, the existing peak and off-peak hour delay in the intersection for all the approaches, and the truck percentage in the intersection.

This methodology assumes the peak hourly flow from the AADT based on the faciltiy type for each approach. User must input local data for peak-hourly volumes if available. The capacity is calculated based on the HCM equations for roundabout capcity. The capcity calculation is based on conflicitng volumes for each approach. The delay is then calculated based on the approach capacity and the approach volume using the HCM control delay equations.

The difference in delays is used to identify the emission beenfits of installing a roundabout in a traditional four-way intersection by using the idling emission factors obtained based on the geographical location of the intersection.

Input Variable
Unit
Definition and Input Guidance
NCLN_{CL}Input the number of circulating roundabout lanes
NNInput the number of lanes in the approach
VDV_{D}vehicles/dayInput the annual average daily traffic volume in the approach
DB,PD_{B,P}seconds/vehicleInput the existing delay per vehicle during peak hours in the approach
DB,OPD_{B,OP}seconds/vehicleInput the existing delay per vehicle during off-peak hours in the approach
TTpercentInput the exisitng truck percentage in the approach
PRTP_{RT}percentInput the right turn percentage of volume in the approach
PLTP_{LT}percentInput the left turn percentage of volume in the approach
PUTP_{UT}percentInput the U-turn percentage of volume in the approach
NPN_{P}Use the default number of peak hours or input local data
Default value: 6
NOPN_{OP}Use the default number of off-peak hours or input local data
Default value: 18
VP,H,VV_{P,H,V}Use the default peak-hour vehicular hourly or input local data
Variable
Unit
Definition
VP,HV_{P,H}(vehicles/hour)Peak hour hourly volume
VOP,HV_{OP,H}(vehicles/hour)Off-Peak hour hourly volume
VC,PV_{C,P}(vehicles/hour)Peak hour conflicting volume
VC,OPV_{C,OP}(vehicles/hour)Off-peak hour conflicitng volume
CPC_{P}Peak hour hourly capacity
COPC_{OP}Off-Peak hour hourly capacity
DA,PD_{A,P}Peak hour delay per vehicle after implementation
DA,OPD_{A,OP}Off-Peak hour delay per vehicle after implementation
DR,PD_{R,P}minutes/vehiclePeak hour delay reduction per vehicle
DR,OPD_{R,OP}minutes/vehicleOff-Peak hour delay reduction per vehicle

Resources

Highway Capacity Manual, Sixth Edition: A Guide for Multimodal Mobility Analysis
Transportation Research Board (2016), Description of methodology and default values for analyzing the signalized intersection

MOVES2014 Statewide Non-Link On-Road Emissions Inventories for 2006, 2012, and 2018
Texas A&M Transportation Institute (2014), Documentation of methodology and default values for analyzing transportation infrastructure in Texas

6.1 Park-and-Ride – New Facilities

Reduce vehicle trips and vehicle miles traveled (VMT) by enhancements of transit system and ridesharing.

Description

Construction of new park-and-ride facilities in locations remote from the central city area or major business activity centers or on the fringes of major employment centers. Lots or garages are constructed adjacent to or very near transit facilities or heavily traveled corridors. These lots are designed to be conducive to several modes of transportation including pedestrian and bicycle facilities.

Application

Cities with HOV facilities or public transit systems.

Emissions Equations

Daily Emission Reduction (grams/day)=A−B−CDaily \space Emission \space Reduction \space (grams/day) = A -B -C

  • A=VMTB×EFB−(VMTA×EFA,1+VMTC×EFA,2)A = VMT_{B} \times EF_{B} – (VMT_{A} \times EF_{A,1} + VMT_{C} \times EF_{A,2})

    Reduction in running emissions from reduced vehicle trips of auto

  • B=VMTT×EFT+VMTV×EFVB = VMT_{T} \times EF_{T} + VMT_{V} \times EF_{V}

    Additional running emissions from transit and vanpool vehicles

  • C=VTT×TEFT+VTV×TEFVC = VT_{T} \times TEF_{T} + VT_{V} \times TEF_{V}

    Additional starts emissions from transit and vanpool vehicles
Variable
Unit
Definition
VMTBVMT_{B}(mile)Vehicle miles traveled before park-and-ride program
EFBEF_{B}(grams/mile)Speed-based running exhaust emission factor before implementation for auto
VMTAVMT_{A}(mile)Vehicle miles traveled after park-and-ride program for auto vehicles
EFA,1EF_{A,1}(grams/mile)Speed-based running exhaust emission factor after implementation from home to parking facility for auto
VMTCVMT_{C}(mile)Additional vehicles miles traveled by carpool vehicles
EFA,2EF_{A,2}(grams/mile)Speed-based running exhaust emission factor after implementation from parking facility to work for auto
VMTTVMT_{T}(mile)Additional vehicles miles traveled by transit vehicles
EFTEF_{T}(grams/mile)Speed-based running exhaust emission factor for transit vehicle
VMTVVMT_{V}(mile)Additional vehicles miles traveled by vanpool vehicles
EFVEF_{V}(grams/mile)Speed-based running exhaust emission factor for vanpool vehicle
VTTVT_{T}(trips)Additional number of trips due to transit vehicles
TEFTTEF_{T}(grams/trip)Trip end emission factor for transit vehicle
VTVVT_{V}(trips)Additional number of trips due to vanpool vehicles
TEFVTEF_{V}(grams/trip)Trip end emission factor for vanpool vehicle

Source
Texas A&M Transportation Institute

Activity Methodologies

  • NA,C=NPK×UP×pCN_{A,C}=N_{PK}\times U_{P}\times p_{C}

  • NC=NA,C×OAOC−OAN_{C}=\frac{N_{A,C}\times O_{A}}{O_{C}-O_{A}}

  • NA,T=N×UP×pTN_{A,T}=N \times U_{P} \times p_{T}

  • NT=NA,T×OAOT−1N_{T}=\frac{N_{A,T}\times O_{A}}{O_{T}-1}

  • NA,V=N×UP×pVN_{A,V}=N \times U_{P} \times p_{V}

  • NV=NA,V×OAOV−1N_{V}=\frac{N_{A,V}\times O_{A}}{O_{V}-1}

  • pC+pT+pV=100p_{C}+p_{T}+p_{V}=100

  • VTB=2×(NA,C+NA,T+NA,V)VT_{B}=2\times (N_{A,C}+N_{A,T}+N_{A,V})

  • VTC=2×NCVT_{C}=2\times N_{C}

  • VTT=2×NTVT_{T}=2\times N_{T}

  • VTV=2×NVVT_{V}=2\times N_{V}

  • VMTA=VTB×L1VMT_{A}=VT_{B}\times L_{1}

  • VMTC=VTC×L2VMT_{C}=VT_{C}\times L_{2}

  • VMTT=VTT×L2VMT_{T}=VT_{T}\times L_{2}

  • VMTV=VTV×L2VMT_{V}=VT_{V}\times L_{2}

Methodology and Assumptions
The approach evaluates the benefits of park-and-ride facility to attract people to use alternative travel modes for their home-based work trips. With a new park-and-ride facility, users are expected to choose among carpool, public transit, and vanpool to travel to the work place. It estimates the activity and emission benefits in peak-hour since most of the home-based work trips happed during morning peak period and evening peak period. The method requires the geographical information of the park-and-ride facility, such as area type, roadway facility type, and peak hour facility utilization rate. It also requires the home-based work trip information, such as average trip distance among home, parking lot and work place, and the average speed of the trip. Since it is expected that the facility users are choosing from carpool, public transit and vanpool options, it requires a reasonable expectation of the percentage of each travel mode. Also, the calculator considers the additional idling or starts of carpool vehicles. It needs the distribution between idling while waiting and park while waiting as the input. The approach assumes the occupancy of each type of mode option at national level. The values of default parameters are suitable for most of the situations but it is still recommended to use local specific data if they are available.

The calculator estimates activity changes in vehicle trips and VMT as users use park-and-ride facility. The methodology first assumes that the home-based work trips happen in morning peak-period or evening peak period. The parking spaces are occupied with carpooling, vanpooling or public transit users. The carpooling vehicles are either idling or parked when they are waiting to pick other passengers. The auto trip distance is expected now to be shortened from home to work to home to parking facility. Because of the difference in vehicle occupancy, the number of auto trips and the VMT of auto trips are reduced. However, there is increase in number of trips, idling, and VMT from carpooling vehicles.

Input Variable
Unit
Definition and Input Guidance
NPKN_{PK}Number of Parking Spaces
Input the number of parking spaces in park-and-ride facility
L1L_{1}(mile)Average Auto Trip Length from Home to Parking Facility
Input the average trip length from home to parking facility
LL(mile)Average Auto Trip Length from Work to Home
Input the average trip length from home to work place
L2L_{2}(mile)Average Auto Trip Length from Work to Parking Facility
Input the average trip length from parking facility to work
LtL_{t}(mile)Additional transit miles from park-and-ride facility to work place. Same as L2 if there is not existing transit route along the facility
UPU_{P}(percent)Expected Peak Hour Parking Facility Utilization Rate
Input the utilization rate of parking spaces during peak hours
v1v_{1}(mph)Average Trip Average Speed from Home to Parking Facility
Input the average trip speed from home to parking facility
vv(mph)Average Trip Average Speed from Home to Work
Input the average trip speed from home to work place
v2v_{2}(mph)Average Trip Average Speed from Parking Place to Work
Input the average trip speed from work place to parking facility
pCp_{C}(percent)Percentage of Auto Vehicle Passengers Who are Expected to be Transferred to Carpool Vehicles
Input the percentage of program participants who are expected to transfer to carpool vehicles
pTp_{T}(percent)Percentage of Auto Vehicle Passengers Who are Expected to be Transferred to Public Transit Vehicles
Input the percentage of program participants who are expected to transfer to transit vehicles.
pVp_{V}(percent)Percentage of Auto Vehicle Passengers Who are Expected to be Transferred to Vanpool Vehicles
Input the percentage of program participants who are expected to transfer to vanpool vehicles.
OAO_{A}(person)Auto Occupancy
Default value: 1.13
Use the default or input local occupancy if data is available
OCO_{C}(person)Carpool Occupancy
Default value: 2.31
Use the default or input local occupancy if data is available
OTO_{T}(person)Transit Vehicle Occupancy
Default value: 25
Use the default or input local occupancy if data is available
The default value is preset
OVO_{V}(person)Vanpool Vehicle Occupancy
Default value: 8
Use the default or input local occupancy if data is available
The default value is preset
Variable
Unit
Definition
NA,CN_{A,C}(vehicle)Number of Auto Vehicles Whose Passengers are Expected to be Transferred to Carpool Vehicles
NCN_{C}(vehicle)Number of Auto Vehicles Which are Expected to be Carpool Vehicles
NA,TN_{A,T}(vehicle)Number of Auto Vehicles Whose Passengers are Expected to be Transferred to Transit Vehicles
NA,VN_{A,V}(vehicle)Number of Auto Vehicles Whose Passengers are Expected to be Transferred to Vanpool Vehicles
NTN_{T}(vehicle)Number of Transit Vehicles
NVN_{V}(vehicle)Number of Vanpool Vehicles
VTBVT_{B}(trip)Existing Number of Home to Work Place Trips of Park-and-Ride Participants
VTCVT_{C}(trip)Number of Parking Facility to Work Place Trips of Carpool Vehicles
VTTVT_{T}(trip)Number of Parking Facility to Work Place Trips of Transit Vehicles
VTVVT_{V}(trip)Number of Parking Facility to Work Place Trips of Vanpool Vehicles
VMTAVMT_{A}(miles)Vehicle Miles Traveled by Park-and-Ride Program for Auto Vehicles
VMTCVMT_{C}(miles)Additional Vehicle Miles Traveled by Park-and-Ride Program for Carpool Vehicles
VMTTVMT_{T}(miles)Additional Vehicle Miles Traveled by Park-and-Ride Program for Transit Vehicles
VMTVVMT_{V}(miles)Additional Vehicle Miles Traveled by Park-and-Ride Program for Vanpool Vehicles

6.2 Park-and-Ride – Improved Connections

Enhance the attraction of using park-and-ride facilities.

Description

A direct connector ramp between park-and-ride facilities and a freeway is an enhancement of the service provided by the parking facilities. Some emissions will be reduced as buses, vans, and carpools idle less while waiting to enter and exit the freeway. This strategy serves to enable park-and-ride facilities and improves public transit.

This measure is also more expensive than others. The location of the parking facilities relative to the freeway will determine the cost of constructing the ramp. Parking facilities adjacent to highways, requiring little site preparation, should demand less funding than others in more remote locations.

Application

Urban areas with park-and-ride facilities, transit service, and rideshare programs.

Emissions Equations

Daily Emission Reduction (grams/day)=A+BDaily \space Emission \space Reduction \space (grams/day)= A + B

  • A=(VMTBUS,B×EFB−VMTBUS,A×EFA)+(VMTAUTO,B×EFB−VMTAUTO,A×EFA)A = (VMT_{BUS, B} \times EF_{B} – VMT_{BUS, A} \times EF_{A}) + (VMT_{AUTO, B} \times EF_{B} – VMT_{AUTO, A} \times EF_{A})

    Reduction in vehicle running exhaust emissions from improved travel time from park-and-ride lot to freeway entrance

  • B=NP×FAT×TLPR×EFB×2 trips/dayB = N_{P} \times F_{AT} \times TL_{PR} \times EF_{B} \times 2 \space trips/day

    Reduction in auto running exhaust emissions from a reduction in commute trip length multiplied by two trips per day (round trip)
Variable
Unit
Definition
EFAEF_{A}(grams/mile)Speed-based running exhaust emission factor after implementation (NOx, VOC, PM, or CO)
EFBEF_{B}(grams/mile)Speed-based running exhaust emission factor before implementation (NOx, VOC, PM, or CO)
FATF_{AT}(%)Percentage of participants who previously drove single-occupancy vehicles (SOVs)
NPN_{P}Number of new park-and-ride participants
TLPRTL_{PR}(mile)Average trip length to park-and-ride facility
VMTAUTO,AVMT_{AUTO, A}Vehicle miles traveled by auto after implementation
VMTAUTO,BVMT_{AUTO, B}Vehicle miles traveled by auto before implementation
VMTBUS,AVMT_{BUS, A}Vehicle miles traveled by transit vehicle after implementation
VMTBUS,BVMT_{BUS, B}Vehicle miles traveled by transit vehicle before implementation

Source
Texas A&M Transportation Institute

6.3 Onsite Support Services

Reduce VMT through clustering of personal services at park-and-ride/fringe parking lots.

Description

Park-and-ride/fringe parking lots that provide personal support services enhance passenger use of the lot. Riders are able to conduct personal business in one place, which reduces VMT. Some services and amenities provided at park-and-ride/fringe parking lots include convenience stores, financial services, child-care centers, postal services, laundry/dry cleaning, and food services.

Application

Urban areas with existing park-and-ride/fringe parking lots.

Emissions Equations

Daily Emission Reduction (grams/day)=A+B+CDaily \space Emission \space Reduction \space (grams/day)= A + B + C

  • A=(NPK×UP×FUSE)×NHBO×TLHBO×EFB)A = (N_{PK} \times U_{P} \times F_{USE}) \times N_{HBO} \times TL_{HBO} \times EF_{B})

    Reduction in auto running exhaust emissions from a reduction in home-based other trips

  • B=(NPK×UP×FUSE)×NHBO×TEFAUTOB = (N_{PK} \times U_{P} \times F_{USE}) \times N_{HBO} \times TEF_{AUTO}

    Reduction in auto start exhaust emissions from a reduction in home-based other trips

  • C=NP×FAT×TLPR×EFB×2 trips/dayC = N_{P} \times F_{AT} \times TL_{PR} \times EF_{B} \times 2 \space trips/day

    Reduction in auto running exhaust emissions from a reduction in commute trip length multiplied by two trips per day (round trip)
Variable
Unit
Definition
EFBEF_{B}(grams/mile)Speed-based running exhaust emission factor before implementation (NOx, VOC, PM, or CO)
FATF_{AT}(percent)Percentage of participants who previously drove SOVs
FUSEF_{USE}(percent)Percentage of park-and-ride users that utilize the facilities
NHBON_{HBO}(trip)Average number of home-based other trips
NPN_{P}(participant)Number of new participants using onsite services at the park-and-ride/fringe parking lots
NPKN_{PK}(spaces)Number of parking spaces
TEFAUTOTEF_{AUTO}(grams/trip)Auto trip-end emission factor (NOx, VOC, PM, or CO)
TLHBOTL_{HBO}(mile)Average trip length of home-based other
TLPRTL_{PR}(mile)Average trip length to facility
UPU_{P}Parking lot utilization rate (estimate)

Source
Texas A&M Transportation Institute

7.2 Control of Truck Movement

Reduce congestion along corridors and reduce idling. Reduce ozone formation through an offset in emission times.

Description

Cities can regulate the movement of trucks within some areas at certain times. Historically, these programs have involved restricting trucks on local streets in certain areas of the central business district during peak hours, designating specific loading zones, delivery schedules, and truck routes, as well as multiple business delivery consolidation. However, controlling truck movements requires various legal restrictions that practitioners should definitely consider when proposing such measures. The cooperation and support of the trucking industry are crucial to program success.

Implementation of controls must involve consideration of time periods and routes currently being used for movements, direct costs to businesses for the controls, and indirect costs to the economy for changing truck movement patterns. Therefore, local traffic and economic data are essential to planning controls.

Application

Downtown areas or major business activity centers with alternate freeway and arterial routes available.

Emissions Equations

Daily Emission Reduction (grams/day)=ADaily \space Emission \space Reduction \space (grams/day)= A

  • A=∑(VMTB×EFBi−VMTA×EFAi)A= \sum (VMT_{B} \times EF_{Bi} – VMT_{A} \times EF_{Ai})

    The change in running exhaust emissions of trucks on the affected links before control subtracted by the running exhaust emissions of these trucks after control by reroute to alternative routes or reschedule to off-peak period.
Variable
Unit
Definition
EFAEF_{A}(grams/mile)Speed-based running exhaust emission factor for fleet composite with time period specific truck mix (NOx, VOC, PM, or CO)
EFBEF_{B}(grams/mile)Speed-based running exhaust emission factor for defined fleet composite with time period specific truck mix (NOx, VOC, PM, or CO)
iiTime period
VMTBVMT_{B}Vehicle miles traveled by truck fleet during peak period before control of truck movement
VMTAVMT_{A}Vehicle miles traveled by truck fleet after control of truck movement

Source
Texas A&M Transportation Institute

7.3 Truck Lane Restrictions

Reduce congestion along corridors and improves operational efficiency

Description

Transportation agencies can restrict the movement of heavy-duty trucks to two or more designated lanes of a highway. This ensures that at least one of the highway lanes (normally the left lane or inside lane) is used only by passenger vehicles. Trucks are often slower-moving than the passenger vehicles in these lanes. Therefore, controlling truck use of these lanes improves operational efficiency and highway safety. Truck lane restrictions should only be considered where there is a minimum of four percent trucks in the traffic stream over a 24-hour period and when approximately 10 percent of the total truck traffic is currently using the lanes on which the restrictions are to apply.

Application

The roadway section to be restricted should be at least six miles long and should have at least three lanes on each side of the freeway.

Emissions Equations

Daily Emission Reduction (grams/day)=A+B+C+DDaily \space Emission \space Reduction \space (grams/day) = A + B + C + D

  • A=VDA,P×(EFB−AUTO,P−EFA−AUTO,P)×LA = V_{DA, P} \times (EF_{B-AUTO, P} – EF_{A-AUTO, P}) \times L

    Change in auto running exhaust emissions during the peak period

  • B=VDA,OP×(EFB−AUTO,OP−EFA−AUTO,OP)×LB = V_{DA, OP} \times (EF_{B-AUTO, OP} – EF_{A-AUTO, OP}) \times L

    Change in auto running exhaust emissions during the off-peak period

  • C=VDT,P×(EFB−TRUCK,P−EFA−TRUCK,P)×LC = V_{DT, P} \times (EF_{B-TRUCK, P} – EF_{A-TRUCK, P}) \times L

    Change in truck running exhaust emissions during the peak period

  • D=VDT,OP×(EFB−TRUCK,OP−EFA−TRUCK,OP)×LD = V_{DT, OP} \times (EF_{B-TRUCK, OP} – EF_{A-TRUCK, OP}) \times L

    Change in truck running exhaust emissions during the off-peak period
Variable
Unit
Definition
EFA,Auto,OPEF_{A, Auto, OP}(grams/mile)Speed-based auto running exhaust emission factor during off-peak hours in affected corridor after implementation (NOx, VOC, PM, or CO)
EFA,Auto,PEF_{A, Auto, P}(grams/mile)Speed-based auto running exhaust emission factor during peak hours in affected corridor after implementation (NOx, VOC, PM, or CO)
EFB,Auto,OPEF_{B, Auto, OP}(grams/mile)Speed-based auto running exhaust emission factor during off-peak hours in affected corridor before implementation (NOx, VOC, PM, or CO)
EFB,Auto,PEF_{B, Auto, P}(grams/mile)Speed-based auto running exhaust emission factor during peak hours in affected corridor before implementation (NOx, VOC, PM, or CO)
EFA,Truck,OPEF_{A, Truck, OP}(grams/mile)Speed-based truck running exhaust emission factor during off-peak hours in affected corridor after implementation (NOx, VOC, PM, or CO)
EFA,Truck,PEF_{A, Truck, P}(grams/mile)Speed-based truck running exhaust emission factor during peak hours in affected corridor after implementation (NOx, VOC, PM, or CO)
EFB,Truck,OPEF_{B, Truck, OP}(grams/mile)Speed-based truck running exhaust emission factor during off-peak hours in affected corridor before implementation (NOx, VOC, PM, or CO)
EFB,Truck,PEF_{B, Truck, P}(grams/mile)Speed-based truck running exhaust emission factor during peak hours in affected corridor before implementation (NOx, VOC, PM, or CO)
LL(miles)Length of the roadway(s) implementing truck restriction strategy
VDA,OPV_{DA, OP}(vehicles)Average daily auto volume for the corridor during off-peak hours
VDA,PV_{DA, P}(vehicles)Average daily auto volume for the corridor during peak hours
VDT,OPV_{DT, OP}(vehicles)Average daily truck volume for the corridor during off-peak hours
VDT,PV_{DT, P}(vehicles)Average daily truck volume for the corridor during peak hours

Source
Texas A&M Transportation Institute

10.2 Idling Controls on Heavy-Duty Vehicles

Reduce vehicle emissions.

Description

This measure places restrictions on idling time for trucks, buses, construction equipment, and other heavy-duty on-road vehicles in the nonattainment area. The restriction may be automatic or manually implemented. Automatic restrictions would require a modification to a vehicle engine design that shuts off an idling vehicle engine after a set time limit. Manual restrictions would require the operator of the vehicle to shut off the engine.

The primary attraction of this measure to the regulated community is that it provides emission reduction benefits while also providing a cost savings through reduction in motor fuel consumption.

Application

Medium-sized and large urban areas with significant number of heavy-duty vehicles and equipment operating in the area.

Emissions Equations

Daily Emission Reduction (grams/day)=A×BDaily \space Emission \space Reduction \space (grams/day) = A \times B

  • A=NV×FParkA = N_{V} \times F_{Park}

    The number of vehicles in compliance with idling restrictions

  • B=EFI×(tB−tA)B = EF_{I} \times (t_{B} – t_{A})

    The reduction in idling exhaust emissions from reduced time spent in idling
Variable
Unit
Definition
EFIEF_{I}(grams/hour)Idling emission factor for trucks (NOx, VOC, PM, or CO)
FParkF_{Park}(percent)Compliance factor (percentage of vehicles that park instead of idling)
NVN_{V}(vehicle)Total number of vehicles
tAt_{A}(hour)Total idling time spent after implementation of control per vehicle per day
tBt_{B}(hour)Total idling time spent before implementation of control per vehicle per day

Source
Texas A&M Transportation Institute

Activity Methodologies

  • tB=t/60t_{B}=t/60

  • tA=t′/60t_{A}=t’/60

  • NV=Navg∗NN_{V}=N_{avg}*N

Methodology and Assumptions
The method is designed to evaluate the benefits of extended vehicle idling reduction strategies for bus terminals and truck rest areas. It estimates the activity and emission benefits on a daily basis. The method requires the basic information of the bus terminal or rest area, such as number of heavy-duty vehicles at the facilities, average idling time per vehicle per day, number of facilities to participate the program, maximum idling time per vehicle after control, and estimated percentage of vehicles that park instead of idling.

The approach of extended vehicle idling reduction project is consistent with Section 10.2 in The Texas Guide to Accepted Mobile Source Emission Reduction Strategies (MOSERS) – Module 2. The calculator estimates activity changes in three different types of facilities, transit bus terminal, school bus terminal, and truck rest area.

In both the school bus terminal and the transit bus terminal, the extended vehicle idling reduction strategy is applied to reduce idling time the facility buses. It is estimated that with idling control, the idling time is reduced to for each facility bus. The strategy assumes that a certain percentage of buses will park instead of idle.

In truck rest area, the extended vehicle idling reduction strategy is applied to reduce idling time of heavy-duty vehicles. It is estimated that with idling control, the idling time is reduced for each heavy-duty vehicle. The strategy assumes that a certain percentage of heavy-duty vehicles will park instead of idle.

Input Variable
Unit
Definition and Input Guidance
NavgN_{avg}(vehicle)Existing Average Hourly Numbers of Vehicles per Facility
Input the average number of vehicles per facility.
tt(minute)Existing average idle time per vehicle
Input the average idle type per vehicle
NN(facility)Number of Existing Facilities Plan to Reduce Idling Time
Input the numbers of facilities in this project
t′t’(minute)Maximum Idling Time Expected to be Allowed per vehicle by the Control
Input the maximum idling allowance in the project
FParkF_{Park}(percent)Compliance Factor (percentage of vehicles that park instead of idling)
Input the percentage of vehicles that park instead of idling
Variable
Unit
Definition
tBt_{B}(hour)Time spent in queue before implementation of restriction
tAt_{A}(hour)Time spent in queue after implementation of restriction
NVN_{V}(vehicle)Average number of vehicles for all facilities

14.1 Accelerated Vehicle Retirement – Cash Payments

Reduce fleet vehicle emissions

Description

Cash payment, or a bounty, is offered for older, high-emission vehicles. The vehicles are then scrapped. In some instances, non-emission-related parts from the vehicles may be salvaged for use as replacement parts. Cash payment programs should include follow-up and evaluation procedures to minimize any uncertainty in emission benefits.

Application

Best when utilized in conjunction with a regional inspection and maintenance (I/M) program. Congestion Mitigation and Air Quality Improvement Program (CMAQ) funds cannot be used for this strategy.

Emissions Equations

Daily Emission Reduction (grams/day)=VMTB×EFO−VMTA×EFN+(VTB×TEFO−VTA×TEFN)Daily \space Emission \space Reduction \space (grams/day)= VMT_{B} \times EF_{O} – VMT_{A} \times EF_{N} + (VT_{B} \times TEF_{O} – VT_{A} \times TEF_{N})
The average daily VMT of vehicles removed from service multiplied by the average daily composite emission factor for vehicles removed from service subtracted by the average daily VMT of new vehicles multiplied by the average daily composite emission factor for the replacement vehicles.
Variable
Unit
Definition
VMTAVMT_{A}VMT by the replacement vehicle (estimate)
VMTBVMT_{B}VMT by the vehicle to be replaced (estimate)
EFNEF_{N}(grams/mile)Replacement vehicle speed-based running exhaust emission factor (NOx, VOC, PM, or CO)
EFOEF_{O}(grams/mile)Retired vehicle speed-based running exhaust emission factor (NOx, VOC, PM, or CO)
VTAVT_{A}(trip)Number of trips of retired vehicle (estimate)
VTBVT_{B}(trip)Number of trips of replacement vehicle (estimate)
TEFOTEF_{O}(grams/trip)Trip end emission factor of retired vehicle (NOx, VOC, PM, or CO)
TEFNTEF_{N}(grams/trip)Trip end emission factor of replacement vehicle (NOx, VOC, PM, or CO)

Source
Texas A&M Transportation Institute

16.1 Clean Vehicle Program

Reduce vehicle emissions through new vehicle technology.

Description

Public funding can be committed toward the incremental cost of vehicles with lower emissions for public fleets. The program aids in converting light-duty vehicles, transit buses, school buses, and heavy-duty delivery trucks to natural gas and building a fleet of lower emission vehicles. Programs are open to all public fleets, transit agencies, and private companies.

Application

Cities, agencies, and employers with a large vehicle fleet.

Emissions Equations

Daily Emission Reduction (grams/day)=VMTREP×(EFB−EFA)+VTREP×(TEFB−TEFA)Daily \space Emission \space Reduction \space (grams/day)= VMT_{REP} \times (EF_{B} – EF_{A}) + VT_{REP} \times (TEF_{B} – TEF_{A})
Average daily VMT of the replaced vehicle multiplied by the change in pre-replacement and post-replacement composite emission factors
Variable
Unit
Definition
EFAEF_{A}(grams/mile)Speed-based running exhaust emission factor after replacement (NOx, VOC, PM, or CO)
EFBEF_{B}(grams/mile)Speed-based running exhaust emission factor before replacement (NOx, VOC, PM, or CO)
TEFATEF_{A}(grams/trip)Trip end emission factor after replacement (NOx, VOC, PM, or CO)
TEFBTEF_{B}(grams/trip)Trip end emission factor before replacement (NOx, VOC, PM, or CO)
VMTREPVMT_{REP}Average Daily VMT of the vehicle to be replaced
VTREPVT_{REP}Average Daily Trips of the vehicle to be replaced

Source
CalTrans/CARB

4.2 Trip-Reduction Programs

Achieve emission reduction goals by requiring specific reductions in the number of vehicle trips by employees of large companies.

Description

Trip-reduction programs require employers of specific-size companies to reduce the number of commute trips made by employees. Program goals can be mandatory or voluntary for employers. The program encourages use of alternative modes of travel including ridesharing, vanpooling, transit, walking/bicycling, and telecommuting among employees.

Application

Large companies and development projects in large metropolitan areas and suburbs.

Emissions Equations

Daily Emission Reduction (grams/day)=A+B−C−DDaily \space Emission \space Reduction \space (grams/day) = A + B – C – D

  • A=VTR×TEFAUTOA = VT_{R} \times TEF_{AUTO}

    Reduction in auto start emissions from trip reductions

  • B=VMTR×EFAUTOB = VMT_{R} \times EF_{AUTO}

    Reduction in auto running exhaust emissions from trip reductions

  • C=VTTransit×L×EFT+VTVanpool×L×EFVC = VT_{Transit} \times L \times EF_{T} + VT_{Vanpool} \times L \times EF_{V}

    Increase in running exhaust emissions from transit and vanpool trips

  • D=VTTransit×TEFT+VTVanpool×TEFVD = VT_{Transit} \times TEF_{T} + VT_{Vanpool} \times TEF_{V}

    Increase in starts emissions from transit and vanpool trips
Variable
Unit
Definition
EFAUTOEF_{AUTO}(grams/mile)Speed-based running exhaust emission factor for auto (NOx, VOC, PM, or CO)
TEFAUTOTEF_{AUTO}(grams/trip)Auto trip-end emission factor for auto (NOx, VOC, PM, or CO)
VMTRVMT_{R}(mile)Reduction in daily auto vehicle miles traveled (estimate)
VTRVT_{R}(trip)Reduction in number of daily vehicle trips
EFTEF_{T}(grams/mile)Speed-based running exhaust emission factor for transit bus (NOx, VOC, PM, or CO)
TEFTTEF_{T}(grams/trip)Trip-end emission factor for transit bus (NOx, VOC, PM, or CO)
EFVEF_{V}(grams/mile)Speed-based running exhaust emission factor for van (NOx, VOC, PM, or CO)
TEFVTEF_{V}(grams/trip)Auto trip-end emission factor for van(NOx, VOC, PM, or CO)
VTTransitVT_{Transit}(trip)Number of transit trips
VTVVT_{V}(trip)Number of vanpool trips

Source
Texas A&M Transportation Institute

Activity Methodologies

  • NB/P=N×pauto100×pB/P100N_{B/P} = N \times \frac{p_{auto}}{100} \times \frac{p_{B/P}}{100}

  • VTB/P=2×NB/POautoVT_{B/P}= \frac {2 \times N_{B/P}}{O_{auto}}

  • NTransit=N×pauto100×pTransit100N_{Transit} = N \times \frac{p_{auto}}{100} \times \frac{p_{Transit}}{100}

  • VTR,Transit=2×NTransitOautoVT_{R,Transit}= \frac {2 \times N_{Transit}}{O_{auto}}

  • NRideshare=N×pauto100×pRideshare100N_{Rideshare}= N \times \frac{p_{auto}}{100} \times \frac{p_{Rideshare}}{100}

  • VTR,Rideshare=2×NRideshareOauto−2×NRideshareORideshareVT_{R,Rideshare}= \frac {2 \times N_{Rideshare}}{O_{auto}} – \frac{2 \times N_{Rideshare}}{O_{Rideshare}}

  • NVanpool=N×pauto100×pVanpool100N_{Vanpool}= N \times \frac{p_{auto}}{100} \times \frac{p_{Vanpool}}{100}

  • VTR,Vanpool=2×NVanpoolOVanpoolVT_{R,Vanpool}= \frac {2 \times N_{Vanpool}}{O_{Vanpool}}

  • NP=NB/P+NTransit+NRideshareN_{P} = N_{B/P} +N_{Transit}+N_{Rideshare}

  • VTR=VTB/P+VTTransit+VTRideshareVT_{R}=VT_{B/P} + VT_{Transit} + VT_{Rideshare}

  • VMTR=VTR×LVMT_{R}=VT_{R} \times L

Methodology and Assumptions
The calculator is designed to evaluate the area-wide or site-wide benefits of encouraging alternative modes of travel for home-based work trips to the employees. The calculator estimates activity changes in vehicle trips and VMT including the reduction of auto trips and VMT from program participation. It assumes there are two home-based work trips per employee.

The method thus requires the basic information of total employment, and percentage of single occupant vehicle employees to participate each alternative mode program. It also requires the activity information including current trip mode shares of home-based work trips, average trip distance, and average speed of the trip.

The methodology first assumes that the employees would be the possible participants of the trip reduction program. The participants are expected to choose from rideshare, public transit, vanpooling or biking/walking as their new travel mode of home-based work trips. For biking/walking participants, the auto vehicle trips and auto vehicle VMT are fully eliminated. For the transit and vanpool trips, the auto trips are completely reduced but additional transit and vanpool trips are added. For ride share participants, the auto vehicle trips and auto vehicle VMT are reduced due to different vehicle occupancies.

Input Variable
Unit
Definition and Input Guidance
NN(employee)Total Employment (Site wide or Area-wide)
Input the total number of the employees covered with trip reduction program
LL(mile)Average Home-based Work Trip Distance per Employee
Input the average distance of home based work trips
pSOVp_{SOV}(percent)Current Trip Mode Shares of Home-Based Work Trip – Single Occupancy Vehicle
Input the percentage of SOV drivers among all the employees.
pB/Pp_{B/P}(percent)Expected Percentage of SOV Drivers to be Participants in the Bike/Pedestrian Program
Input the expected percentage of the SOV drives who would be the participants of bike/pedestrian program
pTransitp_{Transit}(percent)Expected Percentage of SOV Drivers to be Participants in the Public Transit Program
Input the expected percentage of the SOV drives who would be the participants of public transit program
PRideshareP_{Rideshare}(percent)Expected Percentage of SOV Drivers to be Participants in the Rideshare Program
Input the expected percentage of the SOV drives who would be the participants of rideshare program
PVanpoolP_{Vanpool}(percent)Expected Percentage of SOV Drivers to be Participants in the Vanpool Program
Input the expected percentage of the SOV drives who would be the participants of vanpool program
OautoO_{auto}(persons/vehicle)Average occupancy of auto
Default value: 1.13
Use the default or input local auto occupancy if data is available
ORideshareO_{Rideshare}(persons/vehicle)Rideshare Occupancy
Default value: 2.31
Using the default or input local rideshare occupancy if data is available
The default value is based on The Impact of HOT Lanes on Carpools
OVanpoolO_{Vanpool}(persons/vehicle)Vanpool Occupancy
Default value: 8
Using the default or input local vanpool occupancy if data is available
Variable
Unit
Definition
NB/PN_{B/P}(participant)Number of Bike/Pedestrian Program Participants
NTransitN_{Transit}(participant)Number of Public Transit Program Participants
NRideshareN_{Rideshare}(participant)Number of Rideshare Program Participants
NVanpoolN_{Vanpool}(participant)Number of Vanpool Program Participants
VTB/PVT_{B/P}(trip)Number of Trips Reduced of Bike/Pedestrian Program Participants
VTTransitVT_{Transit}(trip)Number of Trips Reduced of Transit Program Participants
VTRideshareVT_{Rideshare}(trip)Number of Trips Reduced of Rideshare Program Participants
VTVanpoolVT_{Vanpool}(trip)Number of Trips Reduced of Vanpool Program Participants
NPN_{P}(trip)Number of Trip Reduction Program Participants
VTRVT_{R}(trip)Reduction in Number of Daily Auto Vehicle Trips
VMTRVMT_{R} (mile)Reduction in Number of Daily Auto Vehicle Miles Traveled

12.1 Telecommuting

Reduce vehicle trips and work trip VMT.

Description

Telecommuting involves employees working at home or at satellite work centers with approval of employers for one or more days per week. Satellite work centers are constructed and maintained by employers or agencies and provide the required work tools for an employee to perform his or her tasks. Telecommuting has grown with the rise and adoption of information technology in the last two decades. The use of centers does not reduce trips but can significantly decrease VMT.

Application

Organizations that do not require daily face-to-face customer or coworker interaction or that otherwise require the constant physical presence of the employee.

Emissions Equations

Daily Emission Reduction (grams/day)=A+BDaily \space Emission \space Reduction \space (grams/day) = A + B

  • A=VTR×TEFAUTOA = VT_{R} \times TEF_{AUTO}

    Reduction in auto start emissions from trips reductions

  • B=VMTR×EFBB = VMT_{R} \times EF_{B}

    Reduction in auto running exhaust emissions from trips reductions
Variable
Unit
Definition
EFBEF_{B}(grams/mile)Speed-based running exhaust emission factor for participants before implementation (NOx, VOC, PM, or CO)
TEFAUTOTEF_{AUTO}(grams/trip)Auto trip-end emission factor (NOx, VOC, PM, or CO)
VMTRVMT_{R}Reduction in daily automobile VMT
VTRVT_{R}(trip)Reduction in number of daily vehicle trips

Source
CalTrans/CARB

Activity Methodologies

  • Work from Home

  • NH=N×pHN_{H}=N\times p_{H}

  • VTSOV,H=NH×pSOV×2VT_{SOV,H}=N_{H} \times p_{SOV} \times 2

  • VTR,H=NH×pR×2ORVT_{R,H}=\frac{N_{H} \times p_{R}\times 2}{O_{R}}

  • VTH=VTSOV,H+VTR,HVT_{H}=VT_{SOV,H}+VT_{R,H}

  • VMTH=VTH×LVMT_{H}=VT_{H} \times L

  • Work at Satellite Work Center

  • NS=N×pSN_{S}=N\times p_{S}

  • VTSOV,S=NS×pSOV,S×2VT_{SOV,S}=N_{S} \times p_{SOV,S} \times 2

  • VTR,S=NS×pR,S×2ORVT_{R,S}=\frac{N_{S} \times p_{R,S}\times 2}{O_{R}}

  • VTs=VTSOV,S+VTR,S−NS×pSOV×2−NS×pR×2ORVT_{s}=VT_{SOV,S}+VT_{R,S}-N_{S} \times p_{SOV} \times 2 – \frac{N_{S}\times p_{R}\times 2}{O_{R}}

  • VMTs=(VTSOV,S+VTR,S)×(TLW−TLTC)VMT_{s}=(VT_{SOV,S}+VT_{R,S})\times(TL_{W}-TL_{TC})

  • VTR=VTH+VTSVT_{R}=VT_{H}+VT_{S}

  • VMTR=VMTH+VMTSVMT_{R}=VMT_{H}+VMT_{S}

Methodology and Assumptions
The method is designed to evaluate the benefits of telecommuting strategies such as work from home or work at the satellite work center. It estimates the activity and emission benefits in daily scope even though these trips are expected to be made during peak period of the day.

The method requires the basic information of current home-based work trips, including total employment, average trip distance, current travel mode share, and trip average speed. It also expected to have inputs of telecommuting strategies such as expected percentage of program participants, expected telecommuting mode share, average trip distance to satellite work center and its trip average speed.

The approach assumes the rideshare auto occupancy based on national average value. The values of default parameters are suitable for most situations, but it is still recommended to use local specific data if they are available to replace these parameters.

The approach of telecommuting project is consistent with Section 12.1 in The Texas Guide to Accepted Mobile Source Emission Reduction Strategies (MOSERS) – Module 2. The calculator estimate activity changes brought by two sub strategies, work from home and work at satellite work center.

The home-based work trips are fully eliminated when the employees can work from home, so are the VMT by these vehicles. The total number of trips and the total VMT by program participants before they are working from home are the benefits of the strategy.

The number of home-based work trips is not changed under this strategy. It is expected that the trip distance is reduced so that the single trip VMT is reduced. The trip average speed might be increased if the satellite work center is in less congested area. The trip mode share might be changed as well. For example, the participants might drive their own vehicle instead of making home-based work trip rideshare with other people due to different destinations. The calculator provides the options to quantify these potential changes.

Input Variable
Unit
Definition and Input Guidance
NNTotal Employment (Site wide or Area-wide)
Input the total number of the employees covered with trip reduction program.
TLWTL_{W}(miles)Average Home-based Work Trip Length per Employee
Input the average distance of home based work trips.
pSOVp_{SOV}(percent)Current Trip Mode Shares of Home-based Work Trip – Single Occupant Vehicle
Input the percentage of SOV drivers among all the employees
pRp_{R}(percent)Current Trip Mode Shares of Home-based Work Trip – Rideshare
Input the percentage of rideshare participants among all the employees
vv(mph)Home-based Work Trip Average Speed
Input the average speed of home based work trips
pHp_{H}(percent)Expected Percentage of Total Employees to be Approved to Work from Home
Input the expected percentage of the participants of work form home program
pSp_{S}(percent)Expected Percentage of Total Employees to be Approved to Work at Satellite Work Center
Input the expected percentage of the participants of satellite work center program
pSOV,Sp_{SOV,S}(percent)Trip Mode Shares of Home-based Satellite Work Center Trips – Single Occupant Vehicle
Input the trip mode share of home-based satellite work center trips by SOV
pR,Sp_{R,S}(percent)Trip Mode Shares of Home-based Satellite Work Center Trips – Rideshare
Input the trip mode share of home-based satellite work center trips by rideshare
TLTCTL_{TC}(miles)Home-based Satellite Work Center Trip Distance
Input the trip distance of home-based satellite work center trips
vSv_{S}Home-based Satellite Work Center Trip Average Speed
Input the average speed of home-based satellite work center trips
ORO_{R}Rideshare Occupancy
Default value: 2.31
Use the default or input local rideshare occupancy if data is available
Variable
Unit
Definition
NHN_{H}(participant)Work from Home Strategy Participants
VTSOV,HVT_{SOV,H}(trip)Existing Number of Home-based Work Trips of Work from Home Strategy Participants by Single Occupant Vehicle
VTR,HVT_{R,H}(trip)Existing Number of Home-based Work Trips of Work from Home Strategy Participants by Rideshare Vehicles
VTHVT_{H}(trip)Number of Daily Trips Reduced by Work from Home Strategy
VMTHVMT_{H}Number of VMT Reduced by Work from Home Strategy
NSN_{S}(participant)Satellite Work Center Strategy Participants
VTSOV,SVT_{SOV,S}(trip)Existing Number of Home-based Work Trips of Satellite Work Center Strategy Participants by Single Occupant Vehicle
VTR,SVT_{R,S}(trip)Existing Number of Home-based Work Trips of Satellite Work Center Strategy Participants by Rideshare Vehicles
VTSVT_{S}(trip)Number of Daily Trips Reduced by Satellite Work Center Strategy
VMTSVMT_{S}Number of VMT Reduced by Satellite Work Center Strategy
VTRVT_{R}(trip)Reduction in Number of Daily Auto Vehicle Trips
VMTRVMT_{R}Reduction in Number of Daily Vehicle Miles Traveled

12.2 Flextime

Reduce peak hour congestion.

Description

Flextime allows employees to set arrival and/or departure times with the approval of the employer in order to avoid traveling at peak traffic times, but all employees are present for some core period of the workday

Application

Businesses or agencies that do not require specific hours of employee availability.

Emissions Equations

Daily Emission Reduction (grams/day)=(NP×TLW)×(EFB−EFA)×ND/5Daily \space Emission \space Reduction\space (grams/day)= (N_{P} \times TL_{W}) \times (EF_{B} – EF_{A}) \times N_{D}/5
Number of vehicle trips reduced multiplied by the average auto trip length. The number of flextime participants multiplied by the average auto commute trip length multiplied by the change in auto running exhaust emission factors due to improved average travel speed multiplied by the percentage of the work week affected by the strategy
Variable
Unit
Definition
EFAEF_{A}(grams/mile)Speed-based running exhaust emission factor for participants after implementation (NOx, VOC, PM, or CO)
EFBEF_{B}(grams/mile)Speed-based running exhaust emission factor for participants before implementation (NOx, VOC, PM, or CO)
NDN_{D}(day)Number of work days eliminated
NPN_{P}Number of participants
TLWTL_{W}(mile)Average auto trip length

Source
Texas A&M Transportation Institute

12.3 Compressed Work Week

Reduce work trips, VMT, and traffic volume by reducing days of travel to work site by employees and spreading trips outside the peak period.

Description

Compressed work weeks are work scheduling programs that condense a standard number of work hours into fewer than five days per week or fewer than 10 days per two-week period (e.g., four days at 10 hours per day or 80 hours over nine days).

Application

Employers who determine that productivity and services by their organization can be maintained by a compressed work schedule.

Emissions Equations

Daily Emission Reduction (grams/day)=A+B+CDaily \space Emission \space Reduction \space (grams/day)= A + B + C

  • A=VTR×TEFAUTOA = VT_{R} \times TEF_{AUTO}

    Reduction in auto start emissions from trips reductions

  • B=VMTR×EFBB = VMT_{R} \times EF_{B}

    Reduction in auto running exhaust emissions from trip reductions

  • C=NP×TLW×(EFB×EFA)×ND/ND,PRGC = N_{P}\times TL_{W} \times (EF_{B} \times EF_{A}) \times N_{D} / N_{D, PRG}

    The number of participants multiplied by the average auto commute trip length multiplied by the change in auto running exhaust emission factors due to improved average travel speed multiplied by the percentage of the work week affected by the strategy

  • VTR=NP×ND/ND,PRG×trips/dayVT_{R} = N_{P}\times N_{D} / N_{D, PRG} \times trips/day

    The number of program participants multiplied by the number of work days eliminated divided by the number of work days within the scheduling program multiplied by two trips per day (round trip)

  • VMTR=VTR×TLWVMT_{R} = VT_{R} \times TL_{W}

    The vehicle trips reduced multiplied by the average auto commute trip length
Variable
Unit
Definition
EFAEF_{A}(grams/mile)Speed-based running exhaust emission factor after implementation (NOx, VOC, PM, or CO)
EFBEF_{B}(grams/mile)Speed-based running exhaust emission factor for participants before implementation (NOx, VOC, PM, or CO)
NDN_{D}(day)Number of work days eliminated
ND,PRGN_{D, PRG}(day)Number of work days in the scheduling program (five or ten days)
NPN_{P}(participant)Number of participants
TEFAUTOTEF_{AUTO}(grams/trip)Auto trip-end emission factor (NOx, VOC, PM, or CO)
TLWTL_{W}(mile)Average auto trip length of commute to work
VMTRVMT_{R}Reduction in daily automobile VMT
VTRVT_{R}(trip)Reduction in number of daily vehicle trips

Source
CalTrans/CARB