Road Network Modeling with Layered Abstraction for Path Discovery in Vehicle Navigation Systems

Algorithms for path planning have recently drawn renewed attention from researchers due to the advances of intelligent transportation applications. The difference on path planning between past and nowadays is the complexity of dynamic transportation network. This paper focuses on the road network modeling to speed up real time path discovery in dynamic and complex road networks. On the basis of community detection (partitioning), an agglomerative hierarchical clustering algorithm with greedy strategy is proposed in this paper. The algorithm performs a hierarchical network abstraction to make a layered road network with largest community modularity. Static and dynamic information of the real time road network conditions is concerned in the process of network abstraction. Based on the road network layering, the performance of path discovery can be improved due to the reduction of unnecessary searching. The performance of the proposed algorithm was evaluated and compared with the well-known Dijkstra and A-star algorithms. The result shows that the proposed algorithm performs better than the other two algorithms.