Research on Evaluation Method for Rational Layout of Urban Bayonet Devices
The layout of traditional traffic bayonet devices is determined entirely based on manual experience without a systematic quantitative analysis framework. This inevitably leads to persistent problems such as unreasonable resource allocation incomplete monitoring coverage, and redundant equipment deployment. Based on field-collected data of Weifang City, this paper constructs a comprehensive evaluation index system from four dimensions: spatial distribution balance, traffic flow bearing adaptability, facility operation efficiency, and road network coordination adaptability. A hierarchical scientific evaluation model is established by adopting a grid discretization model and the Analytic Hierarchy Process (AHP). The empirical results show that the overall evaluation score of bayonet layout in the study area is 65.2, reaching a "basically reasonable" level, while prominent deficiencies still exist, including insufficient monitoring coverage on main roads and overloaded operation of bayonet facilities on arterial roads. Based on the above analytical findings, a targeted technical improvement scheme for the existing traffic bayonet system is proposed. The research results can provide data support for the integration of stock resources and the site planning of new bayonet devices in the field of urban intelligent transportation, and further promote the construction of basic databases for scenarios such as vehicle license plate recognition.
References
- Khandelwal, S., Choudhury, Z., Shrivastava, S., & Purini, S. (2024). Seer: A framework for optimizing traffic camera placement and deep learning inference at the edge for vehicle path reconstruction. In Proceedings of the 2024 IEEE/ACM Symposium on Edge Computing (SEC) (pp. 333–345). IEEE.
- Standardization Administration of China. (2018). Specification for video image information collection in key public security areas (GB 37300-2018).
- Wei, C., Zhou, L., Yin, Q., & Lin, B. (2025). Research on the evaluation method of surveillance camera layout for 3D spatial grids. Transactions in GIS, 29(1), e70004.
- Ge, Y., Jin, P. J., & Zhang, T. (2025). Roadside CCTV traffic sensor coverage and spatial accuracy analysis with line-of-sight simulation over 3D digital surface model. Transportation Research Record.
- Cheng, F., Pan, F., Liu, C., & Chen, X. (2022). Planning and layout method of urban-scale intelligent video bayonets. Police Technology, 1.
- Lu, F., Sha, W. C., Feng, S. Q., & Zhu, L. Z. (2016). Research on spatial site selection model of public security video bayonets based on PGIS spatiotemporal big data. Guangdong Public Security Science and Technology, 2.
- Rokhsari, S., Delavar, M. R., Sadeghi-Niaraki, A., & Moshiri, B. (2012). Optimum site selection of urban traffic cameras based on AHP, Voronoi diagram and image fusion concept. In Proceedings of the 3rd International Conference on Road Accidents and Driving (ITAC).
- Qiao, W. Y., Huang, M., & Zhang, X. L. (2024). Optimization model of bayonet layout for individual vehicle travel detection. Acta Scientiarum Naturalium Universitatis Sunyatseni, 63(1), 137–144.
- Li, Y., Cheng, Z., Yao, X., et al. (2023). Multi-objective optimal deployment of road traffic monitoring cameras: A case study in Wujiang, China. Sustainability, 15(15), 12011.