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}<