An Eco-Driving Strategy at Multiple Fixed-Time Signalized Intersections Considering Traffic Flow Effects

To encourage energy saving and emission reduction and improve traffic efficiency in the multiple signalized intersections area, an eco-driving strategy for connected and automated vehicles (CAVs) considering the effects of traffic flow is proposed for the mixed traffic environment. Firstly, the formation and dissipation process of signalized intersection queues are analyzed based on traffic wave theory, and a traffic flow situation estimation model is constructed, which can estimate intersection queue length and rear obstructed fleet length. Secondly, a feasible speed set calculation method for multiple signalized intersections is proposed to enable vehicles to pass through intersections without stopping and obstructing the following vehicles, adopting a trigonometric profile to generate smooth speed trajectory to ensure good riding comfort, and the speed trajectory is optimized with comprehensive consideration of fuel consumption, emissions, and traffic efficiency costs. Finally, the effectiveness of the strategy is verified. The results show that traffic performance and fuel consumption benefits increase as the penetration rate of CAVs increases. When all vehicles on the road are CAVs, the proposed strategy can increase the average speed by 9.5%, reduce the number of stops by 78.2%, reduce the stopped delay by 82.0%, and reduce the fuel consumption, NO<sub>x</sub>, and HC emissions by 20.4%, 39.4%, and 46.6%, respectively.

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Publication Details

Journal
Sensors
Published
2024-09-30
DOI
https://doi.org/10.3390/s24196356
Citations
3
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
0.98

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article

An Eco-Driving Strategy at Multiple Fixed-Time Signalized Intersections Considering Traffic Flow Effects

Jinghua Guo, Junbin Guo, Jingyao Wang, Huinian Wang
3 citations
Sensors
Traffic control and management
0.98
article

An Eco-Driving Strategy at Multiple Fixed-Time Signalized Intersections Considering Traffic Flow Effects

Jinghua Guo, Junbin Guo, Jingyao Wang, Huinian Wang
article en
3 citations

Abstract

To encourage energy saving and emission reduction and improve traffic efficiency in the multiple signalized intersections area, an eco-driving strategy for connected and automated vehicles (CAVs) considering the effects of traffic flow is proposed for the mixed traffic environment. Firstly, the formation and dissipation process of signalized intersection queues are analyzed based on traffic wave theory, and a traffic flow situation estimation model is constructed, which can estimate intersection queue length and rear obstructed fleet length. Secondly, a feasible speed set calculation method for multiple signalized intersections is proposed to enable vehicles to pass through intersections without stopping and obstructing the following vehicles, adopting a trigonometric profile to generate smooth speed trajectory to ensure good riding comfort, and the speed trajectory is optimized with comprehensive consideration of fuel consumption, emissions, and traffic efficiency costs. Finally, the effectiveness of the strategy is verified. The results show that traffic performance and fuel consumption benefits increase as the penetration rate of CAVs increases. When all vehicles on the road are CAVs, the proposed strategy can increase the average speed by 9.5%, reduce the number of stops by 78.2%, reduce the stopped delay by 82.0%, and reduce the fuel consumption, NO<sub>x</sub>, and HC emissions by 20.4%, 39.4%, and 46.6%, respectively.

SensorsVol. 24(19)
Xiamen University (CN), PLA Rocket Force University of Engineering (CN)
National Natural Science Foundation of China, State Key Laboratory of Automotive Simulation and Control, State Key Laboratory of Automotive Safety and Energy
Affordable and clean energy
Openalex Percentile: Top 23%
Traffic control and management
0.98
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An Eco-Driving Strategy at Multiple Fixed-Time Signalized Intersections Considering Traffic Flow Effects — Jinghua Guo, Junbin Guo, et al. · Sensors (2024) | TGRS Research Map | TGRS