Abstract
ABSTRACTSmart 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.
Highlights
Activity-based travel demand models rely on travel demand surveys
We focus here on the detection of primary locations and activities based on public transport smart card records for London (UK)
Since a minimum of 2 journeys are required to carry out an activity, a minimum of 2 and maximum of 60 journey counts are considered to examine the regularity of usage
Summary
Activity-based travel demand models rely on travel demand surveys. These surveys are designed to gather rich information about travel choices but are expensive to administer, and typically cover only a short time span and a relatively small proportion of travelers.Recent advancements in information and communication technologies have renewed interest in the activity-based approaches to human behavior research and urban planning. Large datasets of the kind generated by smart card systems offer enormous potential to better represent human travel behavior (Bagchi and White 2005; Pelletier, Trépanier, and Morency 2011) Despite such data sources lacking specific demographic information or information regarding users’ journey purposes, many of these aspects of human mobility can be inferred from smart card data when travel patterns are regular (Maat, van Wee, and Stead 2005; Manley, Zhong, and Batty 2018). With this in mind, we focus here on the detection of primary locations and activities based on public transport smart card records for London (UK). The results from the model were validated using responses from the London Travel Demand Survey (LTDS) and volunteer survey data
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