Evaluating Environmental Impact of Traffic Congestion in Real Time Based on Sparse Mobile Crowd-Sourced Data
Peng Hao | University of California, Riverside
Traffic congestion degrades the air quality and threatens public health, especially for people living and working near major roadways. Sparse mobile crowd-sourced data, such as cellular network data and Global Positioning System (GPS) data, contain large amounts of traffic data, and provide a supplement or alternative approach to evaluate the environmental impact of traffic congestion.
This research establishes a framework for traffic-related air pollution evaluation using sparse mobile data and traffic volume data from California Performance Measurement System (PeMS) and the Los Angeles Department of Transportation (LADOT). It develops an effective tool to evaluate the environmental impact of traffic congestion in an accurate, timely and economic way. The proposed methods have good performance in estimating monthly peak hour fine particulate matter (PM 2.5) concentration, with error of 2 ug/m3 from the measurement from monitor sites. The estimated spatial distribution of annual PM 2.5 concentration also matches well with the concentration map from California Communities Environmental Health Screening Tool (CalEnviroScreen), but with higher resolution. The proposed system will help transportation operators and public health officials alleviate the risk of air pollution, and can serve as a platform for the development of other potential applications.