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Research on the Social Values of Vehicle–Road Collaborative Intelligence Systems: A Case Study in Beijing

Author

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  • Guangyu Zhu

    (State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing 100084, China)

  • Fuquan Zhao

    (State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing 100084, China)

  • Haokun Song

    (State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing 100084, China)

  • Wang Zhang

    (State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing 100084, China)

  • Zongwei Liu

    (State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China
    Tsinghua Automotive Strategy Research Institute, Tsinghua University, Beijing 100084, China)

Abstract

Intelligent vehicles are expected to yield significant benefits in traffic safety, traffic efficiency, energy conservation, and carbon emission reduction. As the collaborative intelligence technology route becomes an industry consensus, intelligent vehicles will generate greater social benefits under the empowerment of roadside intelligence infrastructure. At the same time, the introduction of roadside intelligence infrastructure also adds corresponding deployment costs and operation and maintenance costs. Currently, assessments of the comprehensive social benefits and cost inputs associated with the application of vehicle–road collaborative intelligence systems remain unclear, making it difficult to provide effective references for industry development. Therefore, it is necessary to conduct a comprehensive assessment of the multi-dimensional benefits generated by collaborative intelligence systems and the incremental costs. This study constructs a social value assessment model for vehicle–road collaborative intelligence systems, which includes three benefit sub-models for safety, efficiency, and carbon emission reduction, as well as two cost sub-models for vehicle-side networking and roadside intelligence infrastructure. Beijing is selected for case analysis. The social benefits and social incremental cost inputs of different intelligence deployment scenarios are scientifically evaluated and analyzed. The study indicates that by deploying roadside intelligence infrastructure and in-vehicle networking terminals as planned in Beijing, an accumulated safety benefit of 925.6 billion RMB, a traffic efficiency benefit of 628.9 billion RMB, and a carbon emission reduction benefit of 2.66 billion RMB are expected to be generated from 2024 to 2050. The cumulative cost investment of 28.8 billion RMB in roadside intelligence infrastructure and vehicle networking terminals is projected to yield approximately 20.8 times the increment in social comprehensive benefits. The deployment progress of roadside intelligence infrastructure and the loading progress of fleet networking terminals should be fully coordinated to maximize the social value of the system. The corresponding research findings can provide references for city managers in decision-making on intelligent road deployment, and for the coordination of vehicle manufacturers in equipping vehicle networking terminals.

Suggested Citation

  • Guangyu Zhu & Fuquan Zhao & Haokun Song & Wang Zhang & Zongwei Liu, 2025. "Research on the Social Values of Vehicle–Road Collaborative Intelligence Systems: A Case Study in Beijing," Sustainability, MDPI, vol. 17(4), pages 1-38, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1565-:d:1590837
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    References listed on IDEAS

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    1. Grant-Muller, Susan & Usher, Mark, 2014. "Intelligent Transport Systems: The propensity for environmental and economic benefits," Technological Forecasting and Social Change, Elsevier, vol. 82(C), pages 149-166.
    2. Haokun Song & Fuquan Zhao & Guangyu Zhu & Haoyi Zhang & Zongwei Liu, 2024. "Evaluation of Traffic Efficiency and Energy-Saving Benefits of L3 Smart Vehicles under the Urban Expressway Scenario," Sustainability, MDPI, vol. 16(10), pages 1-31, May.
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