Mining mobility data to minimise travellers' spending on public transport

This work explores the viability of building a fare-recommendation system for public transport networks by formalising the problem as two separate prediction problems and evaluating a number of algorithms that aim to match travellers to the best fare.

Abstract

As the public transport infrastructure of large cities expands, transport operators are diversifying the range and prices of tickets that can be purchased for travel. However, selecting the best fare for each individual traveller's needs is a complex process that is left almost completely unaided. By examining the relation between urban mobility and fare purchasing habits in large datasets from London, England's public transport network, we estimate that travellers in the city cumulatively spend, per year, up to approximately GBP 200 million more than they need to, as a result of purchasing the incorrect fares. We propose to address these incorrect purchases by leveraging the huge volumes of data that travellers create as they move about the city, by providing, to each of them, personalised ticket recommendations based on their estimated future travel patterns. In this work, we explore the viability of building a fare-recommendation system for public transport networks by (a) formalising the problem as two separate prediction problems and (b) evaluating a number of algorithms that aim to match travellers to the best fare. We find that applying data mining techniques to public transport data has the potential to provide travellers with substantial savings.