Characterization of spatio-temporal distribution of vehicle emissions using web-based real-time traffic data

Vehicle emissions have become an increasingly important source to air pollution in China, thus an accurate estimate of vehicle emissions is essential but challenging for policy-making toward air quality improvement. Since vehicle emissions are episodic, roadway-based micro/meso-scale emissions are getting more and more attention for roadway exposure assessment and accuracy improvement of emission inventory. Hence, it is necessary to characterize the temporal and spatial distribution of vehicle emissions. However, due to the large number of vehicle population and managerial difficulties, it might not be practical to develop vehicle emission inventory based on all individual vehicles at a city level. This study aimed to develop an approach to use web-based real-time traffic data to estimate meso-scale vehicle emissions at a city level. Taking Chengdu as an example, traffic characteristics include driving modes, traffic flows, and fleet compositions under different traffic conditions were quantified using real-world measurements. Web-based traffic data was shown to have adequate accuracy for traffic characterization and thus emission estimation. Real-time traffic conditions of the study area derived from web-based traffic data were then matched with corresponding traffic characteristics. Combining with vehicle modal emission rates, roadway-based vehicle emissions were quantified both spatially and temporally. As expected, estimated roadway-based emissions correlated well with traffic conditions both temporally and spatially. Heavier traffic is usually associated with higher emissions. This study demonstrated that the web-based traffic data can be used in transportation and environment related research. Findings from this work can be used for hotspot identification in traffic and emissions and the associated risk analysis, traffic management, and many other applications.

Language

  • English

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Filing Info

  • Accession Number: 01728772
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jan 28 2020 9:47AM