Computational modeling demonstrates route planning with taxi GPS trajectories outperforms static network models, suggesting experienced driver cognition improves automated navigation.
The popular methods of route planning, the planar and hierarchical algorithms, are mainly based on the Dijkstra's algorithm. The shortest path in machine computation is always different from the optimal one in a human cognition because travel experience information of human is neglected. In this paper,hierarchical travel experience information of road network is given by statistical analysis on a large amount of taxi GPS trajectories. According to taxi trajectories, the roads can be divided into frequent roads, secondary frequent roads and seldom roads. These trajectories can well reflect a hierarchical cognition of road network because considering taxi driver's cognition of the road network. With the hierarchical model of road network, the corresponding hierarchical route planning can be performed based on the experienced driver's cognition. The experiments show that the route results according to the experiential information approach to optimal results more than that according to static information of road network.
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Li et al. (2009) studied this question.
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