Evaluating Environmental Impact of Traffic Congestion in Real Time Based on Sparse Mobile Crowd-sourced Data
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Evaluating Environmental Impact of Traffic Congestion in Real Time Based on Sparse Mobile Crowd-sourced Data

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      Traffic congestion at arterial intersections and freeway bottlenecks degrades the air quality and threatens the public health. Conventionally, air pollutants are monitored by sparsely-distributed Quality Assurance Air Monitoring Sites. Sparse mobile crowd-sourced data, such as cellular network and Global Positioning System (GPS) data, contain large amount of traffic information, but have low sampling rate and penetration rate due to the cost limit on data transmission and archiving. The sparse mobile data 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 Los Angeles Department of Transportation (LADOT). The proposed framework integrates traffic state model, emission model and dispersion model. An effective tool is developed to evaluate the environmental impact of traffic congestion for both arterials and freeways 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.
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