A high-precision heuristic model to detect home and work locations from smart card data |
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| Authors: | Nilufer Sari Aslam Tao Cheng James Cheshire |
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| Institution: | 1. SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London, London, UK;2. Department of Geography, University College London, London, UK |
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| Abstract: | Smart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of travelers. Processing and analyzing these data open new opportunities in urban modeling and travel behavior research. This study seeks to develop an accurate framework for the study of urban mobility from smart card data by developing a heuristic primary location model to identify the home and work locations. The model uses journey counts as an indicator of usage regularity, visit-frequency to identify activity locations for regular commuters, and stay-time for the classification of work and home locations and activities. London is taken as a case study, and the model results were validated against survey data from the London Travel Demand Survey and volunteer survey. Results demonstrate that the proposed model is able to detect meaningful home and work places with high precision. This study offers a new and cost-effective approach to travel behavior and demand research. |
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| Keywords: | Smart card data activity location modeling heuristic primary location model home and work locations human mobility pattern urban activity pattern |
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