# Trip Prediction by Leveraging Trip Histories from Neighboring Users

arXiv: https://arxiv.org/abs/1812.10097 · HTML: https://mlsanity.com/paper/1812.10097/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [A Unified Framework for Online Trip Destination Prediction](https://mlsanity.com/paper/2101.04520/) (2021)
- [Prediction of Time and Distance of Trips Using Explainable Attention-based LSTMs](https://mlsanity.com/paper/2303.15087/) (2023)

## Abstract (arXiv)

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.