Abstract: Aiming at the problem that the path planning methods based on the shortest path only focused on path length, while trajectory-based path planning methods excessively depended on users’ preference, we proposed a path planning method based on both routing preferences and path length. Firstly, the long short-term memory model was used to extract users’ routing preferences from the historical routing trajectory. Secondly, Markov chain Monte Carlo sampling technology was used to introduce the users’ routing preferences into the heuristic search algorithm A* to search for the shorter path in line with users’ routing preferences in the road network. Finally, taking Beijing road network and taxi trajectories as test data, the method was compared with the shortest path based planning method and the trajectory based path planning mehtod. The experimental results show that the path planning method is more stable, and the path planning has higher accuracy, shorter travel distance and travel time.

Key words: path planning, trajectory, long short-term memory, A* algorithm, Markov chain