Time-constrained shortest path search algorithm for urban road network

The topological features of urban road networks represent the interconnecting links between different road networks within cities. Shortest path search methods have garnered significant attention in the field of intelligent transportation. In real-world scenarios, it is essential to determine the shortest path between a given starting and target road section within a specific range based on road network topology. However, traditional path search methods often exhibit low efficiency during whole map searches. To address this issue, this paper introduces a time-constrained shortest path method leveraging the Dijkstra algorithm. In the proposed approach, the passing time of a road under the maximum speed limit is utilized as the weight of the road section, while the time constraint is set according to the actual requirements to find the shortest path between the starting and reachable target sections within the specified time range. Using map data from Xi’an City, this paper compares the time performance of time-constrained shortest path method to that of a uniform-cost search algorithm with spatio-temporal constraints. Experimental results indicate that the proposed method demonstrates superior time performance under various time constraints. The proposed approach is applicable to location prediction, online map matching, and other practical transportation issues, potentially enhancing the efficiency of existing algorithms.