Discovering popular routes from trajectories

The booming industry of location-based services has accumulated a huge collection of users' location trajectories of driving, cycling, hiking, etc. In this work, we investigate the problem of discovering the Most Popular Route (MPR) between two locations by observing the traveling behaviors of many previous users. This new query is beneficial to travelers who are asking directions or planning a trip in an unfamiliar city/area, as historical traveling experiences can reveal how people usually choose routes between locations. To achieve this goal, we firstly develop a Coherence Expanding algorithm to retrieve a transfer network from raw trajectories, for indicating all the possible movements between locations. After that, the Absorbing Markov Chain model is applied to derive a reasonable transfer probability for each transfer node in the network, which is subsequently used as the popularity indicator in the search phase. Finally, we propose a Maximum Probability Product algorithm to discover the MPR from a transfer network based on the popularity indicators in a breadth-first manner, and we illustrate the results and performance of the algorithm by extensive experiments.