Macroscopic Relationship between Traffic Condition and Fuel Consumption for an Urban Road Network: Case Study of Beijing

The waste of fuel causing by traffic congestion is a challenge faced by urban traffic management authorities and travelers. At the same time, massive traffic data allows high-resolution understanding of on-road operating conditions. The development of an algorithm to estimate total fuel consumption from primary traffic condition indices, for example, network average speed, will simplify the evaluation of fuel consumption from the management perspective and guide strategy at the local area level. The objective of this study is to develop a macroscopic relationship between total fuel consumption and the network average speed for an urban road network. Floating car data (FCD) covering 13 weekdays was collected in the field in Beijing, China. FCD from 10 ordinary weekdays are used to develop a quantitative model to define the macroscopic relationship between total fuel consumption and network average speed. The model is then validated by the FCD of the other three weekdays when the traffic demand is low. The average of the resultant absolute relative errors from the validation is found to be 4.65%, which indicates a reasonably high reliability of the developed model under various traffic conditions. The facility- and speed-specific distributions of vehicle kilometers traveled (VKT) are analyzed to explain the macroscopic relationship. The result indicates that the link VKT distribution at different speeds varies greatly when the traffic became congested on expressways. The link VKT distributions are similar for different traffic conditions on arterials and collectors.

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    • The Standing Committee on Transportation Energy (ADC70) peer-reviewed this paper (19-02400). © National Academy of Sciences: Transportation Research Board 2019.
  • Authors:
    • Wang, Jingyi
    • Song, Guohua
    • Yu, Lei
    • Lu, Hongyu
    • Sun, Jianping
    • Cheng, Ying
    • He, Weinan
  • Publication Date: 2019-11

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  • English

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  • Accession Number: 01708153
  • Record Type: Publication
  • Files: TRIS, TRB, ATRI
  • Created Date: Jun 19 2019 3:04PM