Improved Trip Planning by Learning from Travelers' Choices

We analyze the work of urban trip planners and the relevance of trips they recommend upon user queries. We propose to improve the planner recommendations by learning from choices made by travelers who use the transportation network on the daily basis. We analyze individual travelers’ trips and convert them into pair-wise preferences for traveling from a given origin to a destination at a given time point. To address the sparse and noisy character of raw trip data, we model passenger preferences with a number of smoothed time-dependent latent variables, which are used to learn a ranking function for trips. This function can be used to re-rank the top planner’s recommendations. Results of tests for cities of Nancy, France and Adelaide, Australia show a considerable increase of the recommendation relevance.