Availability and Applicability of Emerging Transportation Dataset to the Near-Road Project-Level Air Quality Analysis
For project level analysis using MOVES model, detailed link level activity (volume, speed, source type mix, etc.) information are used to estimate link level emission rates or total emissions. Studies have shown that it is important to incorporate local specific travel activity as the driving characteristics of each area are unique due to different vehicle fleet composition, driving behavior, and road network topography (Yu et al, 2010; U.S.EPA, 2010). Survey conducted by FHWA showed that the large majority of agencies (approximately 92 percent) that are using MOVES reported they are dependent either fully or partially on the MOVES default data to prepare project specific inputs (NCHRP 25-38, 2014). Furthermore some sensitivity analyses on MOVES inputs that have covered project-scale inputs to varying degrees. However, more detailed analysis of the impacts of traffic inputs would be helpful for traffic and air quality modelers tasked with prioritizing and collecting local input data. Some of the data gaps in the current procedures are identified below: 1. Actual traffic volumes and speeds along with detailed traffic activity data would improve the accuracy of the analyses especially for arterial sites. 2. Fleet composition of the traffic data needs to be improved to be consistent with MOVES vehicle type requirement. The traffic counters classify the vehicles into 4-tire, single unit and multiple unit truck categories and post-processing has to be done to match approximately these categories with MOVES vehicle types. 3. Current and future traffic activity data by time of day needs to be consistent with time periods as required by the PM hot-spot process. The regional transportation models utilized by state agencies produce traffic data for time periods different from the PM hot-spot time periods. While conducting project-level air quality analysis, air quality and transportation practitioners face a range of choices in developing inputs for emissions and dispersion modeling. Considering traffic volume, speed, and source type data as an example, it is important to know what level of detail should be collected, light vs. heavy vehicles with average speed, or should additional data collection focus on link-specific distributions of different vehicle types including operating mode distribution. The traffic volume data sources may come from automatic traffic counters, simulation models (traffic micro, meso, and macro simulation models), real-time data (INRIX, GPS data etc.), etc. Some of these data sources may require intensive resources (cost and time) to post process in a format that can be used for air quality applications. Air quality and transportation practitioners need to know which inputs are likely to most influence emissions and air pollution concentrations to determine how to prioritize the limited resources available for data collection and analysis. In this study the Research Team will identify current state-of practice on the project-level traffic and other key inputs used in the project level air quality analysis. Various emerging traffic data sources that are available such as National Performance Research Dataset (NPMRDS), INRIX, ATRI, etc. will be explored for application in project level air quality analysis. The outcome of the research will lead to the development of a guide on traffic data sources availability and applicability (including expected cost effectiveness) with potential impacts on the emissions.
There is a critical need to improve MOVES inputs in the light of the recent EPA's requirement for hot-spot analyses that focus on specific transportation facilities and vehicle types. Further, as revealed by a recent survey, a majority of agencies depend either fully or partially on the MOVES default database which is known to be deficient for project-level analysis. Improved travel activity inputs that accurately reflect real-world traffic behavior will increase emissions and air quality prediction accuracy which in turn allows for an improved decision making process. The study results will establish a guide for best data sources availability with potential impacts on the air quality analysis. The study will also provide a qualitative assessment of cost effectiveness and resource requirements of using these emerging data sources. This evaluation will include aspects such as acquiring, processing, and maintaining the datasets. Practitioners can choose to incorporate the emerging datasets that best fit their needs into the air quality analyses. Some of the potential benefits for practitioners are identified below: • Accurate dispersion modeling by incorporating realistic data can refine the project-level conformity analysis results. With greater understanding the extent to which changes in various traffic inputs might influence emissions. • Funding and multi-million dollar decisions depend on the accurate modeling for mobile sources and the large source of error from incorrect traffic input needs work to provide the air quality analyst with procedures and tools that allow for better traffic inputs. • Potential to advance the state of the practice for traffic forecasting to improve and streamline project-level air quality analyses. • Achieve higher accuracy in modeling and obtaining realistic results of exposure assessment for transportation projects. • Improve the quality of the project-level traffic activity data for non-air quality purposes such as traffic noise assessment.
Texas A&M Transportation Institute
May 9, 2017
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