Trip Prediction by Leveraging Trip Histories from Neighboring Users

TL;DR

This paper introduces a trip prediction method that enhances individual user trip histories with similar trips from other users, significantly improving prediction accuracy in public transportation data.

Contribution

It presents a novel trip prediction approach that leverages neighboring users' trip histories to address data sparsity and noise, boosting prediction accuracy.

Findings

Prediction error reduced by 15%-40% with history augmentation

Effective on real-world Nancy2012 dataset

Improves trip prediction in public transportation systems

Abstract

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.