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The optimal path planning is one of the hot spots in the research of intelligence transportation and geographic information systems. There are many productions and applications in path planning and navigation, however due to the complexity of urban road networks, the difficulty of the traffic prediction increases. The optimal path means not only the shortest distance in geography, but also the shortest time, the lowest cost, the maximum road capacity, etc. In fast-paced modern cities, people tend to reach the destination with the shortest time. The corresponding paths are considered as the optimal paths. However, due to the high data sensing speed of GPS devices, it is different to collect or describe real traffic flows. To address this problem, we propose an innovative path planning method in this paper. Specially, we first introduce a crossroad link analysis algorithm to calculate the real-time traffic conditions of crossroads (i.e. the CrossRank values). Then, we adopt a CrossRank value based A-Star for the path planning by considering the real-time traffic conditions. To avoid the high volume update of CrossRank values, a R-Tree structure is proposed to dynamically update local CrossRank values from the multi-level subareas. In the optimization process, to achieve desired navigation results, we establish the traffic congestion coefficient to reflect different traffic congestion conditions. To verify the effectiveness of the proposed method, we use the actual traffic data of Beijing. The experimental results show that our method is able to generate the appropriate path plan in the peak and low dynamic traffic conditions as compared to online applications.

A Novel Vector-Based Dynamic Path Planning Method in Urban Road Network