Classification of LiveBus arrivals user behaviour

Abstract

With the increasing use of Intelligent Transport Systems, large amounts of data are created. Innovative information services are introduced and new forms of data are available, which could be used to understand the behaviour of travellers and the dynamics of people flows. This work analyse the requests for real time arrivals of bus routes at stops in London made by travellers using Transport for London's LiveBus Arrivals system. The available dataset consists of about one million requests for real time arrivals for each of the 28 days under observation. These data are analysed for different purposes. LiveBus Arrivals users are classified based on a set of features and using K-Means, Expectation Maximization, Logistic regression, One-level decision tree, Decision Tree, Random Forest, and Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO). The results of the study indicate that the LiveBus Arrivals requests can be classified into six main behaviours. It was found that the classification-based approaches produce better results than the clustering-based ones. The most accurate results were obtained with the SVM-SMO methodology (Precision of 97%). Furthermore, the behaviour within the six classes of users is analysed to better understand how users take advantage of the LiveBus Arrivals service. It was found that the 37% of users can be classified as interchange users. This classification could form the basis of a more personalised LiveBus Arrivals application in future, which could support management and planning by revealing how public transport and related services are actually used or update information on commuters.

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ORCID for Ioannis Kaparias: orcid.org/0000-0002-8857-1865

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