Research on the development and application of highway intelligent information services based on vehicle road collaboration
Effectively integrating advanced information methods to promote the digital transformation of highways, improving their management efficiency and service level, is an important way to enhance the high-quality development of highway networks. Through the collection and integration of dynamic traffic information in the whole space and time, vehicle road collaboration can provide accurate, real-time and personalized travel information services. The multiple rounds of policy, technology, and market promotion have led to the rapid development of vehicle-road collaboration on intelligent highways, but the construction and application process is generally faced with the weak ability to reach the information, the sense of user experience is not strong and other issues. The coverage rate of roadside equipment and the penetration rate of vehicle end are increasing, laying the foundation for the construction of an integrated and refined travel information service model. This paper analyzes the key technologies and research status of information exchange and information services in vehicle road collaboration, looks forward to a new model of integrated travel services, and relies on the digital foundation of intelligent highways to create a highway information service model based on vehicle road collaboration technology. This provides a reference for expanding innovative applications of vehicle-road collaboration and enhancing the construction and operation efficiency of intelligent highways.
Authors
- Xiangming Chen (ORCID: https://orcid.org/0000-0002-2571-3539)
- Sheng Zhang (ORCID: https://orcid.org/0000-0001-5599-6261)
- LV Fen
- Zhen-Xing Shen (ORCID: https://orcid.org/0000-0002-4394-0786)
- Zhuofan Shi (ORCID: https://orcid.org/0000-0002-6769-687X)
Institutions
- China Academy of Transportation Sciences (CN)
- State Development & Investment Corporation (China) (CN)
Publication Details
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1117/12.3052943
- Primary Topic
- Traffic Prediction and Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00