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Exploring the impact of connected and autonomous vehicles on freeway capacity using a revised Intelligent Driver Model

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  • Pengfei Liu
  • Wei (David) Fan

Abstract

Connected and autonomous vehicle (CAV) technologies are expected to change driving/vehicle behavior on freeways. This study investigates the impact of CAVs on freeway capacity using a microsimulation tool. A four-lane basic freeway segment is selected as the case study through the Caltrans Performance Measurement System (PeMS). To obtain valid results, various driving behavior parameters are calibrated to the real traffic conditions for human-driven vehicles. In particular, the calibration is conducted using genetic algorithm. A revised Intelligent Driver Model (IDM) is developed and used as the car-following model for CAVs. The simulation is conducted on the basic freeway segment under different penetration rates of CAVs and different freeway speed limits. The results show that with an increase in the market penetration rate, freeway capacity increases, and will increase significantly as the speed limit increases.

Suggested Citation

  • Pengfei Liu & Wei (David) Fan, 2020. "Exploring the impact of connected and autonomous vehicles on freeway capacity using a revised Intelligent Driver Model," Transportation Planning and Technology, Taylor & Francis Journals, vol. 43(3), pages 279-292, April.
    • Handle: RePEc:taf:transp:v:43:y:2020:i:3:p:279-292
    DOI: 10.1080/03081060.2020.1735746
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    Cited by:

    1. Chen, Yingda & Li, Keping & Zhang, Lun & Chen, Yili & Xiao, Xue, 2024. "Modeling and analysis of mixed traffic flow capacity and stability considering human-driven vehicle drivers' trust attitude towards intelligent connected vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).

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