Yanzhi "Ann" Xu

Effectiveness of Idle Reduction Technologies in Reducing Driver Exposure to Diesel Emissions

The purpose of this study was to investigate the effectiveness of idle reduction technologies (IRTs) in reducing driver exposure to diesel exhaust, and to study the cost effectiveness of these technologies. IRTs are devices that provide heating and cooling to the cabin of a truck without idling the truck engine. The focus was on diesel-powered IRTs (auxiliary power units and fuel-operated heaters), and their impact on particulate matter (PM2.5) exposure of drivers sleeping or resting in the truck’s cabin. The focus was on diesel-powered IRTs as these devices generate their own emissions, potentially exacerbating in-cab exposure levels. The project involved initial field data collection at truck stops in the states of Georgia, Texas and California. This was followed by laboratory testing in an environmentally controlled test chamber on a sample of trucks, with and without the use of IRTs. The study findings showed that the use of IRTs resulted in a significant reduction of PM2.5 levels in the truck cabin when compared with the baseline scenario of a truck parked with the engine off and the doors and windows closed. Idling the truck engine and running the air conditioning system was also found to reduce in-cabin PM2.5 levels relative to the baseline. The study supports the premise that IRTs reduce driver exposure to diesel exhaust. Additionally, it was found that these devices are cost effective in that they all have payback periods of less than five years, making them viable alternatives to idling the truck engine during long-duration rest periods.

A Semi-Automated Method to Generate GIS-Based Sidewalk Networks for Asset Management and Pedestrian Accessibility Assessment

Pedestrian infrastructure that is comfortable, connected to destinations of interest, and accessible to those with disabilities is vital to a safe, accessible, equitable, and sustainable transportation system. Planners recognize the benefits of providing well-maintained sidewalks and curb ramps, but often lack the asset management systems necessary to inventory sidewalk maintenance problems, prioritize sidewalk maintenance needs, and track the implementation of sidewalk improvement projects. Communities that are managing sidewalk presence and condition data typically link the data to their roadway network, which makes tracking specific sidewalk assets difficult. This paper introduces an affordable, semi-automated, and easy-to-implement process to generate a GIS-based sidewalk network with associated links and nodes representing crosswalks and intersections. Quantitative sidewalk condition data can be loaded onto the network, which allows it to be used to manage sidewalks as transportation assets, assessing pedestrian accessibility, prioritizing repairs or improvements, and to automatically identify accessible routes between origins and destinations. System inputs include parcel-level land-use and roadway centerline data, both of which are publicly available and free in most cases. The network is generated within the ArcGIS environment, using Python scripts to implement embedded ArcGIS functions. The method requires few computational resources, and tremendously reduces the manual labor required to develop a fully interconnected sidewalk network. Examples from multiple communities are presented to show how quantitative sidewalk condition data are loaded onto the network, and illustrate the network’s potential for pedestrian navigation and routing applications.

MOVES-Matrix and distributed computing for microscale line source dispersion analysis

ABSTRACT MOVES and AERMOD are the U.S. Environmental Protection Agency’s recommended models for use in project-level transportation conformity and hot-spot analysis. However, the structure and algorithms involved in running MOVES make analyses cumbersome and time-consuming. Likewise, the modeling setup process, including extensive data requirements and required input formats, in AERMOD lead to a high potential for analysis error in dispersion modeling. This study presents a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix, a high-performance emission modeling tool, with the microscale dispersion models CALINE4 and AERMOD. MOVES-Matrix was prepared by iteratively running MOVES across all possible iterations of vehicle source-type, fuel, operating conditions, and environmental parameters to create a huge multi-dimensional emission rate lookup matrix. AERMOD and CALINE4 are connected with MOVES-Matrix in a distributed computing cluster using a series of Python scripts. This streamlined system built on MOVES-Matrix generates exactly the same emission rates and concentration results as using MOVES with AERMOD and CALINE4, but the approach is more than 200 times faster than using the MOVES graphical user interface. Because AERMOD requires detailed meteorological input, which is difficult to obtain, this study also recommends using CALINE4 as a screening tool for identifying the potential area that may exceed air quality standards before using AERMOD (and identifying areas that are exceedingly unlikely to exceed air quality standards). CALINE4 worst case method yields consistently higher concentration results than AERMOD for all comparisons in this paper, as expected given the nature of the meteorological data employed. Implications: The paper demonstrates a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix with the CALINE4 and AERMOD. This streamlined system generates exactly the same emission rates and concentration results as traditional way to use MOVES with AERMOD and CALINE4, which are regulatory models approved by the U.S. EPA for conformity analysis, but the approach is more than 200 times faster than implementing the MOVES model. We highlighted the potentially significant benefit of using CALINE4 as screening tool for identifying potential area that may exceeds air quality standards before using AERMOD, which requires much more meteorology input than CALINE4.