Introducing the vehicle-infrastructure cooperative control system by quantifying the benefits for the scenario of signalized intersections

Xianing Wang, Linjun Lu, Zhan Zhang, Ying Wang and Haoming Li

Transportation Research Part A: Policy and Practice, 2025, vol. 192, issue C

Abstract: The vehicle-infrastructure cooperative control system (VICCS) leverages autonomous driving technology and interactive communication between vehicles and infrastructure to maximize system-wide benefits. As this technology emerges, a thorough socio-economic evaluation is essential to substantiate its utility. Analyzing comparisons with traditional systems will assist in adopting this innovative technology. This paper quantifies the potential benefits of the VICCS through several steps: defines the application scenarios of VICCS, models the behavioral control of vehicles and traffic signals, simulates the system in mixed-autonomy traffic environments at signalized intersections, analyzes the operational performance and service levels of VICCS, and evaluates the costs and benefits for the private and public sectors. This study employs a technical framework for VICCS that integrates deep reinforcement learning (DRL) methods to optimize vehicle speed and dynamic traffic signal control. The DRL approach is crafted to forecast the system’s performance and level of intelligence in prospective settings more accurately. The findings reveal that the anticipated VICCS will confer considerable benefits, including enhanced safety, operational efficiency, and environmental sustainability, at a cost to be incurred compared to existing systems. This will result in an annual economic gain of at least CNY10,000 (the difference between the expenditure and the gain) for the private and public sectors. This paper provides policy recommendations to support the strategic deployment of VICCS, informing stakeholders of the practical implications and facilitating the traffic system’s integration into advanced mechanisms.

Keywords: Vehicle-infrastructure cooperative control system; Connected automated vehicles; Deep reinforcement learning; Costs and benefits; Policy recommendations; Signalized intersections (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tra.2025.104378

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