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The impacts of connected vehicle technology on network-wide traffic operation and fuel consumption under various incident scenarios

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  • Arezoo Samimi Abianeh
  • Mark Burris
  • Alireza Talebpour
  • Kumares Sinha

Abstract

Incidents are a major source of traffic congestion and can lead to long and unpredictable delays, deteriorating traffic operations and adverse environmental impacts. The emergence of connected vehicles and communication technologies has enabled travelers to use real-time traffic information. The ability to exchange traffic information among vehicles has tremendous potential impacts on network performance especially in the case of non-recurrent congestion. To this end, this paper utilizes a microscopic simulation model of traffic in El Paso, Texas to investigate the impacts of incidents on traffic operation and fuel consumption at different market penetration rates (MPR) of connected vehicles. Several scenarios are implemented and tested to determine the impacts of incidents on network performance in an urban area. The scenarios are defined by changing the duration of incidents and the number of lanes closed. This study also shows how communication technology affects network performance in response to congestion. The results of the study demonstrate the potential effectiveness of connected vehicle technology in improving network performance. For an incident with a duration of 900 s and MPR of 80%, total fuel consumption and total travel time decreased by approximately 20%; 26% was observed in network-wide travel time and fuel consumption at 100% MPR.

Suggested Citation

  • Arezoo Samimi Abianeh & Mark Burris & Alireza Talebpour & Kumares Sinha, 2020. "The impacts of connected vehicle technology on network-wide traffic operation and fuel consumption under various incident scenarios," Transportation Planning and Technology, Taylor & Francis Journals, vol. 43(3), pages 293-312, April.
  • Handle: RePEc:taf:transp:v:43:y:2020:i:3:p:293-312
    DOI: 10.1080/03081060.2020.1735752
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    Cited by:

    1. Renjie Li & Yanyan Qin, 2024. "Car-Following Strategy Involving Stabilizing Traffic Flow with Connected Automated Vehicles to Reduce Particulate Matter (PM) Emissions in Rainy Weather," Sustainability, MDPI, vol. 16(5), pages 1-23, February.

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