A Machine-Learned Ranking Algorithm for Dynamic and Personalised Car Pooling Services

TL;DR

This paper introduces GoTogether, a machine learning-based recommender system that personalizes car pooling matches by learning individual preferences from historical choices, aiming to improve match success rates in urban mobility.

Contribution

It presents a novel personalized ranking algorithm for car pooling that adapts to individual preferences using learning-to-rank techniques and real mobility data.

Findings

Accurately predicts user preferences in static and dynamic scenarios.

Improves success rate of ride matches in simulated urban environments.

Leverages social media data to model plausible mobility patterns.

Abstract

Car pooling is expected to significantly help in reducing traffic congestion and pollution in cities by enabling drivers to share their cars with travellers with similar itineraries and time schedules. A number of car pooling matching services have been designed in order to efficiently find successful ride matches in a given pool of drivers and potential passengers. However, it is now recognised that many non-monetary aspects and social considerations, besides simple mobility needs, may influence the individual willingness of sharing a ride, which are difficult to predict. To address this problem, in this study we propose GoTogether, a recommender system for car pooling services that leverages on learning-to-rank techniques to automatically derive the personalised ranking model of each user from the history of her choices (i.e., the type of accepted or rejected shared rides). Then, GoTogether builds the list of recommended rides in order to maximise the success rate of the offered matches. To test the performance of our scheme we use real data from Twitter and Foursquare sources in order to generate a dataset of plausible mobility patterns and ride requests in a metropolitan area. The results show that the proposed solution quickly obtain an accurate prediction of the personalised user's choice model both in static and dynamic conditions.