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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2017-11-01


    Size :

    826391 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    CROWD SOURCED TRAFFIC REPORTING

    GUEZIEC ANDRE | European Patent Office | 2017

    Free access

    CROWD SOURCED TRAFFIC REPORTING

    GUEZIEC ANDRE | European Patent Office | 2018

    Free access

    Using crowd-sourced traffic data and open-source tools for urban congestion analysis

    Khaula Alkaabi / Mohsin Raza / Esra Qasemi et al. | DOAJ | 2024

    Free access

    Mining Urban Traffic Condition from Crowd-Sourced Data

    Mai-Tan, Ha / Pham-Nguyen, Hoang-Nam / Long, Nguyen Xuan et al. | Springer Verlag | 2020


    CROWD SOURCED TRAFFIC AND VEHICLE MONITORING SYSTEM

    EAKINS HARRY | European Patent Office | 2022

    Free access