Variable | Unit | Definition |
|---|---|---|
| (grams/mile) | Speed-based running exhaust emission factor for affected roadway before implementation (NOx, VOC, PM, or CO) during peak hours | |
| (grams/mile) | Speed-based running exhaust emission factor for affected roadway before implementation (NOx, VOC, PM, or CO) during off-peak hours | |
| (grams/mile) | Speed-based running exhaust emission factor for transit vehicle (NOx, VOC, PM, or CO) during peak hours – after | |
| (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) | |
| (grams/mile) | Speed-based running exhaust emission factor for transit vehicle (NOx, VOC, PM, or CO) during off-peak hours – after | |
| (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) | |
| (grams/trip) | Auto trip-end emission factor (NOx, VOC, PM, or CO) | |
| (grams/trip) | Bus (or other transit vehicle) trip-end emission factor (NOx, VOC, PM, or CO) – after | |
| (grams/trip) | Bus (or other transit vehicle) trip-end emission factor (NOx, VOC, PM, or CO) – before (Applicable only for transit replacement) | |
| VMT by transit vehicle during peak hours – after | ||
| VMT by transit vehicle during peak hours – before (Applicable only for transit replacement) | ||
| VMT by transit vehicle during off-peak hours | ||
| VMT by transit vehicle during off-peak hours – before (Applicable only for transit replacement) | ||
| Reduction in automobile VMT during peak hours | ||
| Reduction in automobile VMT during off-peak hours | ||
| (trips) | Vehicle trips by bus or other transit vehicle – after | |
| (trips) | Vehicle trips by bus or other transit vehicle – before | |
| (trips) | Reduction in number of automobile vehicle trips during peak hours | |
| (trips) | Reduction in number of daily automobile vehicle trips during off-peak hours |
MOVES2014 User’s Guide
TTI Emissions Inventory Estimation Utilities Using MOVES: MOVES2014Utl
Conformity Emissions Inventory Process Update
MOVES2014-Based Travel Demand Model Link Emissions Estimation Method
Maintaining Project Consistency with Transportation Plans with an Emphasis on Maintaining Air Quality Conformity (2016)
The researchers found that the main challenges leading to project inconsistencies are insufficient communication over the changes to projects design concept and scope, cost, and estimated letting date. The research team developed a Project Consistency Guidebook (PCG), a Supplementary Information Document (SID), and a project consistency checklist. The guidebook explains how project planning and development interact with the regional and project level air quality conformity process, and details procedures and tools that TxDOT and Texas Metropolitan Planning Organizations can use to understand and maintain project-level conformity and project consistency with applicable transportation plans and programs. The SID provides an overview of the subjects relevant to project consistency. The project consistency checklist serves as a guide to keep track of the changes to a project.
Publication date: 2016-10-30
Air Quality Performance Measures – Investigating the Use of STARS II in Mobile Source Air Quality Modeling (2015)
This memorandum summarizes work performed by Texas A&M Transportation Institute (TTI) staff in Fiscal Year (FY) 2015 on investigating the use of Texas Department of Transportation’s (TxDOT) Statewide Traffic Analysis and Reporting System II (STARS II) for mobile source air quality modeling and related uses. STARS II comprises a database of traffic activity and a web-based geographic information system (GIS) that can be used to search for and download traffic count data. This report provides a brief overview of the STARS II system, and discusses the importance of traffic data for air quality research. Our study methodology involved a structured brainstorm session among Air Quality and Transportation Engineering professionals to generate an expansive list of ideas for how STARS II data could be used in Air Quality studies. The study team then selected one of these ideas to perform a case-study. The case-study involved Truck traffic on the I-35 corridor between Laredo, TX and San Antonio, TX, and tested the ability of STARS II to provide data suitable for addressing a “real world” air quality problem. The conclusions from the study are that STARS II offers an important and currently underused data resource for air quality studies.