Using data from the web to predict public transport arrivals under special events scenarios



The Internet has become the preferred resource to announce, search and comment about social events such as concerts, sports games, parades, demonstrations, sales or any other public event that potentially gathers a large group of people. These \emph{planned special events} often carry a potential disruptive impact to the transportation system, because they correspond to non-habitual behavior patterns that are hard to predict and plan for.

Except for very large and mega events (e.g. olympic games, football world cup), operators seldom apply special planning measures for two major reasons: the task of manually tracking which events are happening in large cities is labour-intensive; and, even with a list of events, their impact is hard to estimate, especially when more than one event happens simultaneously.

In this paper, we utilize the Internet as a resource for contextual information about special events and develop a model that predicts public transport arrivals in event areas. In order to demonstrate the feasibility
of this solution for practitioners, we apply off-the-shelf techniques both for Internet data collection and for the prediction model development. We demonstrate the results with a case study from the city-state of Singapore using public transport tap-in/tap-out data and local event information obtained from the Internet.


Intelligent Transportation Systems, machine learning, demand prediction, context mining


Intelligent Transportation Systems, machine learning, demand prediction, context mining

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Journal of Intelligent Transportation Systems: Technology, Planning, and Operations (JITS), Asad Khattak, November 2013

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Year 2014 : 1 citations

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