Efficient Estimation of Time-Dependent Shortest Paths Based on Shortcuts

The shortest path search in the road network in the road network is of great importance in various Intelligent Transportation Systems. However, the commonly used shortest path search algorithms, such as Dijkstra and A * algorithm, are time-consuming due to their complexity, which leads to their poor performance in large-scale road networks. Thus, a new optimization technology is required to solve the path search problem on large-scale road networks. In this paper, the temporal feature of the road network is considered for the shortest path search problem, which is closer to the road network of the real world. And, an algorithm called Time-Dependent A* With Shortcuts (TDAWS) is proposed to estimate the time-dependent shortest paths. Concretely, the road network is pre-processed offline and partitioned into several regions based on clustering, which captures the spatial pattern of the road network. Then we construct shortcuts contain the shortest paths information to reduce search time and propose two mechanisms called Hop On Directionally (HOD) and Hop-Off Early (HOE) to avoid unnecessary detours. We constructed an extensive experimental study on a road network with real-world taxi trajectory data and compared our approach with existing techniques. The results demonstrated that the time cost of our method is more stable and achieves up to 17 times faster than the precise shortest path searching algorithm with an acceptable extra ratio (about 20%) on the path length.