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

Szigeti, Szilárd; Földes, Dávid; Ye, Xin · 2023 · DOAJ

DOI: 10.14513/actatechjaur.00722

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Summary

This study investigates the impact of autonomous vehicle (AV) integration on electric vehicle (EV) energy consumption at unsignalized pedestrian crossings. Motivated by the rapid adoption of EVs and AVs, the research addresses a gap in literature regarding how AV-specific driving behaviors—such as strict speed limit adherence, mandatory pedestrian yielding, and fixed headways—affect energy efficiency in mixed traffic environments. The authors aim to quantify these effects to support the strategic introduction of AV technology. The researchers utilized the VISSIM microsimulation software to model a two-lane road segment with a pedestrian crossing in Budapest, Hungary. The simulation was calibrated using empirical data from roadside video cameras, which established that pedestrians typically cross when vehicle gaps exceed 50 meters and that human drivers yield to pedestrians 69% of the time. To simulate AV behavior, the model enforced a 50 km/h speed limit and specific headways (50, 60, and 70 meters). An external energy consumption model, programmed in C++, calculated energy use based on vehicle kinematics (speed, acceleration, mass) and regenerative braking efficiency. The study analyzed 65 scenarios varying AV traffic shares (0% to 100% in 25% increments) and traffic volumes (200 to 1,000 vehicles per hour). Results for high-volume scenarios (800–1,000 veh/h) were excluded due to congestion artifacts, focusing analysis on 200, 400, and 600 veh/h volumes. The findings reveal a dual effect of AV integration on energy consumption. For human-driven vehicles, the presence of AVs reduced energy consumption by up to 10.67% for yielding drivers and 12.41% for non-yielding drivers. This reduction is attributed to AVs enforcing speed limits and maintaining larger gaps, which allows pedestrians to cross without requiring human-driven vehicles to stop completely. Conversely, AVs themselves experienced increased energy consumption in all scenarios compared to a baseline of human-driven traffic. This increase was driven by the frequent minor accelerations and decelerations required to maintain strict headways and yield to pedestrians, as well as the loss of aerodynamic benefits from larger following distances. In high-density scenarios with 100% AV traffic, energy consumption increased by 35.92% at 400 veh/h and by 96.55% at 600 veh/h compared to the 0% AV baseline. The study concludes that while AVs can improve energy efficiency for human drivers by smoothing traffic flow and reducing unnecessary stops, their rule-following behavior inherently increases their own energy consumption, particularly in higher traffic volumes. The results highlight a trade-off between traffic safety/pedestrian priority and system-wide energy efficiency. The authors suggest that future improvements, such as vehicle-to-vehicle communication and predictive pedestrian movement analysis, could mitigate these energy penalties by enabling smoother coasting and regenerative braking instead of forceful stops.

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• traffic density