Simulating the alteration in energy consumption at a zebra crossing considering different traffic rates of electric and rule-following autonomous vehicles

Szilárd Szigeti; Dávid Földes; Xin Ye

doi:10.14513/actatechjaur.00722

Authors

Szilárd Szigeti Budapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Department of Transport Technology and Economics, Műegyetem rkp. 3, Budapest, Hungary H-1111 // KTI Hungarian Institute for Transport Sciences and Logistics Nonprofit Ltd., Than Károly u. 3-5, Budapest, Hungary H-1119 https://orcid.org/0000-0001-6061-7529
Dávid Földes Budapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Department of Transport Technology and Economics, Műegyetem rkp. 3, Budapest, Hungary H-1111 https://orcid.org/0000-0003-4352-8166
Xin Ye Vehicle Engineering Institute, Chongqing University of Technology, Chongqing 400054, P.R. China https://orcid.org/0000-0002-3765-1363

DOI:

https://doi.org/10.14513/actatechjaur.00722

Keywords:

Traffic simulation, Energy consumption, autonomous vehicle, electric vehicle, pedestrian crossing

Abstract

The progressive integration of autonomous vehicle (AV) technology holds the potential to reshape the prevailing traffic landscape. AVs have different driving characteristics than human-driven vehicles, which manifests itself in the strict adherence to speed limit, in giving priority to pedestrians, and in the pre-set headways they can keep. A traffic simulation environment was built around an unsignalized pedestrian crossing to measure the energy consumption of vehicles in the presence of AVs. The simulation environment was modified to adhere pedestrian-accepted gaps between vehicles in case of crossing. Considered vehicle types are yielding or not yielding human-driven, and AVs. Scenarios were built to model the AV traffic share, the different headways kept by AVs, and the various traffic volumes in each direction. The different driving behaviour and traffic share of AVs led to energy consumption changes, which were modelled through scenario analysis. The maximum energy consumption reduction of human-driven vehicles was 10.67% for yielding vehicles and 12.41% for non-yielding vehicles compared to the 0% AV traffic rate. Although, in case of AVs, the energy consumption increased in all scenarios compared to the basic version with only human-driven vehicles. In higher traffic scenarios, where only AVs were on the road, there was a substantial 35,92-96.55% increase in energy consumption, compared to the 0% AV ratio case. Thereby speed of vehicles, following distance and the number of stops affected the overall system efficiency. The results of this study can contribute to the understanding the impact of AVs which can support their introduction.

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