Efficient Similarity Search for Travel Behavior

To provide travel recommendations and planning in the intelligent transportation system (ITS), we must have the ability to find similar travel patterns among users based on their real mobility traces. To measure the similarity of user’s travel behavior, various methods have been proposed, but they usually only rely on a single attributes-related metric. In comparison, studies of the semantic relationships between travel attributes remain scarce, making it difficult to construct a complete mobility pattern that reveals the relevance between users or groups. In this paper, we introduced the heterogeneous information network to build a weighted travel network with spatial–temporal GPS trajectories. The heterogeneous network allows clustering the similar users based on the connections between different attributes instead of attribute values. On this basis, we defined the meta-paths for travel and used each meta-path to formulate a similarity measure over users by improving existing PathSim (Meta-path-based similarity measures) and SimRank. Next, we aggregated different similarities, where each meta-path was automatically weighted by the learning algorithm to make predictions. The experimental results showed that the recall of the similarity measurement algorithm using multiple meta-paths has improved, which yielded better results than the performance of the algorithm using a single meta-path. The performance of the improved PathSim model under different scales of data was 15% higher than the performance of the improved SimRank model in terms of precision and 21% higher in terms of recall. Due to the area under curve values, our experiments also show that a meta-path combination is more effective than the state-of-the-art approaches and can be efficiently computed.