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We propose a novel approach for trip prediction by analyzing user&#x0027;s trip histories. We augment users&#x0027; (self-) trip histories by adding &#x201C;similar&#x201D; trips from other users, which could be informative and useful for predicting future trips for a given user. This also helps to cope with noisy or sparse trip histories, where the self-history by itself does not provide a reliable prediction of future trips. We show empirical evidence that by enriching the users&#x0027; trip histories with additional trips, one can improve the prediction error by 15&#x0025;&#x223C;40&#x0025;, evaluated on multiple subsets of the Nancy2012 dataset. This real-world dataset is collected from public transportation ticket validations in the city of Nancy, France. Our prediction tool is a central component of a trip simulator system designed to analyze the functionality of public transportation in the city of Nancy.

Trip Prediction by Leveraging Trip Histories from Neighboring Users