A high-precision heuristic model to detect home and work locations from smart card data

Nilufer Sari Aslam · Tao Cheng · James A. Cheshire

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Human Mobility and Location-Based Analysis Transportation Planning and Optimization Urban Transport and Accessibility

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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References

2011Smart card data use in public transit: A literature review 2015Origin–destination trips by purpose and time of day inferred from mobile phone data 2011Identifying Important Places in People’s Lives from Cellular Network Data 2005The potential of public transport smart card data 2010Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones 2007Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System 2013Understanding urban human activity and mobility patterns using large-scale location-based data from online social media 2007Estimating a Rail Passenger Trip Origin‐Destination Matrix Using Automatic Data Collection Systems

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