A Trajectory Optimization Strategy for Connected and Automated Vehicles at Junction of Freeway and Urban Road

Zhongtai Jiang, Dexin Yu, Huxing Zhou, Siliang Luan and Xue Xing
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Zhongtai Jiang: School of Transportation, Jilin University, Changchun 130022, China
Dexin Yu: School of Transportation, Jilin University, Changchun 130022, China
Huxing Zhou: School of Transportation, Jilin University, Changchun 130022, China
Siliang Luan: School of Transportation, Jilin University, Changchun 130022, China
Xue Xing: College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin City 130022, China

Sustainability, 2021, vol. 13, issue 17, 1-22

Abstract: The phenomenon of stop-and-go traffic and its environmental impact has become a crucial issue that needs to be tackled, in terms of the junctions between freeway and urban road networks, which consist of freeway off-ramps, downstream intersections, and the junction section. The development of Connected and Automated Vehicles (CAVs) has provided promising solutions to tackle the difficulties that arise along intersections and freeway off-ramps separately. However, several problems still exist that need to be handled in terms of junction structure, including vehicle merging trajectory optimization, vehicle crossing trajectory optimization, and heterogeneous decision-making. In this paper, a two-stage CAV trajectory optimization strategy is presented to improve fuel economy and to reduce delays through a joint framework. The first stage considers an approach to determine travel time considering the different topological structures of each subarea to ensure maximum capacity. In the second stage, Pontryagin’s Minimum Principle (PMP) is employed to construct Hamiltonian equations to smooth vehicle trajectory under the requirements of vehicle dynamics and safety. Targeted methods are devised to avoid driving backwards and to ensure an optimal vehicle gap, which make up for the shortcomings of the PMP theory. Finally, simulation experiments are designed to verify the effectiveness of the proposed strategy. The evaluation results show that our strategy could effectively militate travel delays and fuel consumption.

Keywords: connected and automated vehicles; eco-driving; vehicle trajectory optimization; mobility (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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