Urban Traffic Congestion State Recognition Supporting Algorithm Research on Vehicle Wireless Positioning in Vehicle-Road Cooperative Environment  
[PERSON]  
1 China Academy of Transportation Sciences, Beijing 100029, China; [EMAIL_ADDRESS] (C.G.); [EMAIL_ADDRESS] (X.L.)1  
[PERSON]  
2 School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; [EMAIL_ADDRESS]  
[PERSON]  
1 China Academy of Transportation Sciences, Beijing 100029, China; [EMAIL_ADDRESS] (C.G.); [EMAIL_ADDRESS] (X.L.)1  
[PERSON]  
1 China Academy of Transportation Sciences, Beijing 100029, China; [EMAIL_ADDRESS] (C.G.); [EMAIL_ADDRESS] (X.L.)1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
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and sharing to realize the acquisition of the real-time position of vehicles. At present, Global Positioning System (GPS) is a commonly used wireless positioning method for vehicles, but there are problems such as signal loss or interference under complex urban road environmental conditions, which can cause positioning blind spots. Due to the sparse layout of base stations on some urban roads, when there is a large flow of people and vehicles in the morning and evening peaks, a single base station might be overloaded, which would cause a positioning drift or the target vehicle to receive fewer than three base station signals. Thus, a low-cost and easy-to-layout local vehicle positioning method is required to eliminate positioning blind spots in complex road environments and improve the robustness of vehicle positioning. With the rapid construction of smart cities, many wireless sensor networks will be deployed on the side of urban roads to provide a sensor network environment for realizing the wireless positioning of vehicles.  
This article discusses the wireless positioning method of vehicles in a vehicle-road collaborative environment as a supplementary positioning method for the failure of the conventional positioning method and adapts measured data to conduct an empirical analysis of the proposed wireless positioning algorithm for vehicles. Based on reviews of related research, the article elaborates on the related theoretical methods of vehicle wireless positioning. A wireless vehicle positioning fusion algorithm that uses error weighting to eliminate LOS and NLOS errors is proposed. This article uses the experimental data in the outfield vehicle-road collaborative environment to compare and verify the positioning accuracy of the positioning fusion algorithm and analyzes the sensitivity of the distance propagation model parameters to the positioning error of the fusion algorithm.  
The research can make up for the short-term vehicle position loss caused by unstable vehicle environments and positioning conditions, help to realize the effective monitoring of the traffic flow operation state, and improve vehicle operation efficiency and safety. In further applications, it will play an important role in all-day real-time traffic congestion state recognition and provide a decision-making basis and technical support for improving traffic congestion in large and medium-sized cities.