Two-stage cooperative optimisation of signal timing and vehicle trajectories for eco-driving at signalised intersections under HDV/CAV/CCAV mixed traffic
- 1Yunnan Yunling Highway Science and Technology Co., Ltd., Kunming, Yunnan, China
- 2Faculty of Transportation Engineering, Kunming University of Science and Technology Co., Ltd., Kunming, Yunnan, China
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.
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BibTeX
@article{wen2026twostage,
title = {{Two-stage cooperative optimisation of signal timing and vehicle trajectories for eco-driving at signalised intersections under HDV/CAV/CCAV mixed traffic}},
author = {Ruoyi Wen and Wei Chu and Xiaoyun Liu and Yunxin Huang and Zexiao Chen and Huiqiang Luo},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359104},
url = {https://doi.org/10.1371/journal.pone.0359104},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Two-stage cooperative optimisation of signal timing and vehicle trajectories for eco-driving at signalised intersections under HDV/CAV/CCAV mixed traffic
AU - Ruoyi Wen
AU - Wei Chu
AU - Xiaoyun Liu
AU - Yunxin Huang
AU - Zexiao Chen
AU - Huiqiang Luo
PY - 2026
DA - 2026/10/27
JO - PLOS One
DO - 10.1371/journal.pone.0359104
UR - https://doi.org/10.1371/journal.pone.0359104
AB - 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.
SN - 1932-6203
ER -