The effect of intersection vehicle scheduling based on the service system of sensors and intelligent traffic road information

With the high-speed development of the automobile industry, the number of domestic vehicles has increased significantly, which has posed a great challenge to the efficiency of road access. To fully utilize the intersection scheduling function of the intelligent transportation system, an optimization of the stranded traffic system based on LoRa is studied. Then, the greedy strategy and adaptive coefficient are used for optimization to obtain an improved genetic algorithm (GA), which is used to verify the intersection scheduling function. In addition, comparative experiments are conducted with standard GA, adaptive genetic algorithm (AGA), and hybrid genetic algorithm (HGA). The AGA optimizes the GA by adapting genetic parameters, while the HGA combines GA with a simulated annealing algorithm. The results showed that comparing the improved GA with GA, AGA, and HGA, the improved GA reduced the average value of individual extremum by 50.36%, 47.51%, and 37.16%, respectively. Compared with other mainstream algorithms, the improved GA reduced the number of stranded vehicles at intersections to 0 during traffic light timing. This indicates that the improved GA has higher and better performance and scheduling advantages in the intersection scheduling of intelligent transportation road service systems. The research aims to provide new ideas for improving traffic efficiency in intelligent transportation systems and promoting the informatization development of modern traffic management.