Two-stage cooperative optimisation of signal timing and vehicle trajectories for eco-driving at signalised intersections under HDV/CAV/CCAV mixed traffic.

Authors

Ruoyi Wen, Wei Chu, Xiaoyun Liu, Yunxin Huang, Zexiao Chen, Huiqiang Luo

Published in

PloS one. Volume 21. Issue 10. Pages e0359104. Epub Oct 27, 2026.

Abstract

The transportation sector accounts for approximately 20% of global energy consumption and is responsible for nearly 25% of global energy-related CO2 emissions, 75% of which come from road transport. Global energy consumption and CO2 emissions from transportation are projected to increase by more than 50% by 2030, and this energy consumption is causing environmental damage. As autonomous vehicles become more widespread, energy consumption issues in the new context of human-machine mixed driving are gradually attracting attention. To reduce energy consumption in emerging human-machine hybrid traffic flows and promote ecological driving, while accounting for the trend toward mixed traffic comprising autonomous and manually driven vehicles, this study proposes a method to guide manually driven vehicles at signalized intersections by having autonomous vehicles lead them. An ecological driving strategy based on a mixed-traffic-following model is established. The study designed a two-way, four-lane, cross-shaped, connected intersection scenario, employing a two-stage collaborative optimisation strategy for traffic signals and vehicle trajectories to coordinate the driving states of various vehicle types within the mixed fleet, thereby optimising following distances and speeds. By constructing vehicle energy consumption models and trajectory optimization models, effective reductions in vehicle energy consumption were achieved. Simulation results indicate that as the penetration rate of autonomous vehicles increases, the number of stops and energy consumption of vehicles in mixed traffic flow significantly decrease, validating the effectiveness of the proposed strategy and providing strong support for reducing carbon emissions.

PMID:
42945332
Bibliographic data and abstract were imported from PubMed on 28 Oct 2026.

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