Communications of the ACM
Ganging up on Information Overload
Computer
To buy or not to buy: mining airfare data to minimize ticket purchase price
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Breadth-first heuristic search
Artificial Intelligence
Getting from here to there: interactive planning and agent execution for optimizing travel
IAAI'02 Proceedings of the 14th conference on Innovative applications of artificial intelligence - Volume 1
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
OneBusAway: results from providing real-time arrival information for public transit
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recommending Social Events from Mobile Phone Location Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Recommender Systems Handbook
How smart is your smartcard?: measuring travel behaviours, perceptions, and incentives
Proceedings of the 13th international conference on Ubiquitous computing
Avoiding the crowds: understanding Tube station congestion patterns from trip data
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Cognitive bias in network services
Proceedings of the 11th ACM Workshop on Hot Topics in Networks
Geo-spotting: mining online location-based services for optimal retail store placement
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.