Abstract
New smart card datasets are providing new opportunities to explore travel behaviour in much greater depth than anything accomplished hitherto. Part of this quest involves measuring the great array of regular patterns within such data and explaining these relative to less regular patterns which have often been treated in the past as noise. Here we use a simple method called DBSCAN to identify clusters of travel events associated with particular individuals whose behaviour over space and time is captured by smart card data. Our dataset is a sequence of three months of data recording when and where individual travellers start and end rail and bus travel in Greater London. This dataset contains some 640 million transactions during the period of analysis we have chosen and it enables us to begin a search for regularities at the most basic level. We first define measures of regularity in terms of the proportions of events associated with temporal, modal (rail and bus), and service regularity clusters, revealing that the frequency distributions of these clusters follow skewed distributions with different means and variances. The analysis then continues to examine how regularity relative to irregular travel across space, demonstrating high regularities in the origins of trips in the suburbs contrasted with high regularities in the destinations in central London. This analysis sets the agenda for future research into how we capture and measure the differences between regular and irregular travel which we discuss by way of conclusion.
Highlights
At coarse spatial and temporal scales, urban transportation systems might be assumed to be an amalgam of highly routinised traveller behaviours
New smart card datasets are providing new opportunities to explore travel behaviour in much greater depth than anything accomplished hitherto. Part of this quest involves measuring the great array of regular patterns within such data and explaining these relative to less regular patterns which have often been treated in the past as noise
Substantial explorations of travel behaviour have shown that the nature of such regularity is considerably more complex, varying within and between individuals on a day-to-day basis (Hanson and Huff 1986), influenced by socio-economic status and land use activity types (Zhong et al 2015), trip chaining (Primerano et al 2008), changes in weather (Saneinejad et al 2012), and simple ad hoc requirements such as managing congestion through physical means
Summary
At coarse spatial and temporal scales, urban transportation systems might be assumed to be an amalgam of highly routinised traveller behaviours. Through various measures of individual regularity with respect to the occurrence of trip-making, it is possible to explore how these dynamics can vary widely over space and time. Mobility data through GPS traces has enabled the automation and extension of longitudinal travel behaviour analysis (Pendyala 1999; Axhausen et al 2002). Through these data, measures of social travel (Schlich and Axhausen 2003; Schlich et al 2004), the extraction of activities and land use related to trip-making (Huang et al 2010; Zhong et al 2015) and the definition of individual activity spaces (Schonfelder and Axhausen 2003; Schonfelder et al 2006) have been explored
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