Toward accurate dynamic time warping in linear time and space
Intelligent Data Analysis
Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City
Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City
Mining Public Transport Usage for Personalised Intelligent Transport Systems
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Mining mobility data to minimise travellers' spending on public transport
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
How smart is your smartcard?: measuring travel behaviours, perceptions, and incentives
Proceedings of the 13th international conference on Ubiquitous computing
Computing with Spatial Trajectories
Computing with Spatial Trajectories
Mining temporal patterns of transport behaviour for predicting future transport usage
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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For people travelling using public transport, overcrowding is one of the major causes of discomfort. However, most Advanced Traveller Information Systems (ATIS) do not take crowdedness into account, suggesting routes either based on number of interchanges or overall travel time, regardless of how comfortable (in terms of crowdedness) the trip might be. Identifying times when public transport is overcrowded could help travellers change their travel patterns, by either travelling slightly earlier or later, or by travelling from/to a different but geographically close station. In this paper, we illustrate how historical automated fare collection systems data can be mined in order to reveal station crowding patterns. In particular, we study one such dataset of travel history on the London underground (known colloquially as the "Tube"). Our spatio-temporal analysis demonstrates that crowdedness is a highly regular phenomenon during the working week, with large spikes occurring in short time intervals. We then illustrate how crowding levels can be accurately predicted, even with simple techniques based on historic averages. These results demonstrate that information regarding crowding levels can be incorporated within ATIS, so as to provide travellers with more personalised travel plans.