Evaluating the environmental impact of traffic congestion based on sparse mobile crowd-sourced data

Abstract

Traffic congestion at arterial intersections and freeway bottleneck degrades the air quality and threatens the public health. Conventionally, air pollutant are monitored by sparsely-distributed Quality Assurance Air Monitoring Sites. Sparse mobile crowd-sourced data, such as cellular network data and GPS data, provide an 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 PeMS data. The proposed framework integrates traffic state model, emission model (EMFAC) and dispersion model (AERMOD). It develops an effective tool to evaluate the environmental impact of traffic congestion in an accurate, timely and economic way. The proposed model is applicable to varying traffic conditions and multiple transport modes on either urban arterial or freeways. The proposed system will provide suggestions to the transportation operator and public health officials to alleviate the risk of air pollutant, and can serve as a platform for other potential applications, such as eco-routing and eco-signal timing.