Trip Prediction by Leveraging Trip Histories from Neighboring Users
This incremental improvement helps address noisy or sparse trip data for public transportation analysis in Nancy, France.
The paper tackles the problem of predicting user trips by augmenting individual trip histories with similar trips from other users, which reduces prediction error by 15%-40% on the Nancy2012 dataset.
We propose a novel approach for trip prediction by analyzing user's trip histories. We augment users' (self-) trip histories by adding 'similar' 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' trip histories with additional trips, one can improve the prediction error by 15%-40%, 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.