Neal Lathia
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.
- R. W. Floyd. Algorithm 97: Shortest Path. Communications of the ACM, 5(6), June 1962. Google Scholar
Digital Library
- R. Zhou and E. A. Hansen. Breadth-First Heuristic Search. Artificial Intelligence, 170, April 2006. Google ScholarDigital Library
- C. Roth, S.M. Kang, M. Batty, and M. Barthelemy. Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows. PLOS ONE, 6.Google Scholar
- T. W. Anderson. The Statistical Analysis of Time Series. Wiley-Interscience, 1994.Google ScholarCross Ref
- J. R. Quinlan. Improved Use of Continuous Attributes in C4.5. Journal of Artificial Intelligence Research, 4:77--90, 1996. Google ScholarDigital Library
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, and P. Reutemann. The WEKA Data Mining Software. SIGKDD Explorations, 11, 2009. Google ScholarDigital Library
- M. C. Gonzalez, C. A. Hidalgo, and A-L Barabasi. Understanding Individual Human Mobility Patterns. Nature, 453:779--782, June 2008.Google ScholarCross Ref
- C. Ratti, R.M. Pulselli, S. Williams, and D. Frenchman. Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis. Environment and Planning B, 33(5):727--748, 2006.Google ScholarCross Ref
- D. Brockmann, L. Hufnagel, and T. Geisel. The Scaling Laws of Human Travel. Nature, 439, January 2006.Google Scholar
- D. J. Graham and S. Glaister. Spatial Implications of Transport Pricing. Journal of Transport Economics and Policy (JTEP), 40(2), May 2006.Google Scholar
- B. Ferris, K. Watkins, and A. Borning. OneBusAway: Results from Providing Real-Time Arrival Information for Public Transit. In ACM CHI, Atlanta, GA, 2010. Google ScholarDigital Library
- J.L. Ambite, G. Barish, C.A. Knoblock, M. Muslea, J. Oh, and S. Minton. Getting from Here to There: Interactive Planning and Agent Execution for Optimizing Travel. In 14th Conference on Innovative Applications of Artificial Intelligence (IAAI), pages 862--869, Menlo Park, California, 2002. Google ScholarDigital Library
- B. Schwartz. The Paradox of Choice: Why More is Less. Harper Perennial, 2004.Google Scholar
- O. Etzioni, C. A. Knoblock, R. Tuchinda, and A. Yates. To Buy or Not to Buy: Mining Airfare Data to Minimize Ticket Purchase Price. In ACM KDD, Washington DC, USA, 2003. Google ScholarDigital Library
- A. Borchers, J. Herlocker, J. Konstan, and J. Riedl. Ganging up on Information Overload. IEEE Computer, 31:106--108, 1998. Google ScholarDigital Library
- F. Ricci, L. Rokach, B. Shapira, and P. Kantor, editors. Recommender System Handbook. Springer, 2010. Google ScholarDigital Library
- D. Quercia, N. Lathia, F. Calabrese, G. Di Lorenzo, and J. Crowcroft. Recommending Social Events from Mobile Phone Location Data. In IEEE ICDM, Sydney, Australia, December 2010. Google ScholarDigital Library
- W. Jang. Travel Time and Transfer Analysis Using Transit Smart Card Data. Transportation Research Board, 2010.Google ScholarCross Ref
- H. Bryan and P. Blythe. Understanding Behaviour Through Smartcard Data Analysis. Proceedings of the Institution of Civil Engineers: Transport, 160(4), 2007.Google ScholarCross Ref
Index Terms
- Mining mobility data to minimise travellers' spending on public transport
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