New perspectives on daily urban mobility: Harnessing the potential of Smart Card Travel data
Lead Research Organisation:
University College London
Department Name: Geography
Abstract
Smart Travel Cards (STCs) are introduced in many cities in the UK and elsewhere. The data form a novel resource routinely collected by transport authorities, complementing the wide range of travel data already held by those organisations. STCs record all electronic ticket transactions in public transport and in this way trace daily mobility in unprecedented detail for a large proportion of both local residents and visitors. In combination with other data held in the public and private domains, the data offer potential to understand, in new ways, the dynamics of daily, urban mobility for the benefit of transport organisations as well as important issues of wider concern in social science and policy. In particular, the data complement traditional, often costly data sources (Travel-To-Work component of the UK Census, travel diaries in household travel surveys) by offering a nearly real-time temporal resolution, geographical detail and wide population coverage. In assembling the data provided by TfWM (based on an existing formal data sharing agreement), the research will contribute to pressing social science and policy questions of achieving sustainable and inclusive mobility in the context of social inequalities and lifestyle choices (Kandt et al 2015). The research is also envisioned to deliver a framework to harness STC data for social science research more generally and thereby develop a blueprint for other cities that have introduced or will introduce STCs. The research also contributes directly to the objectives of TfWM of using their vast databases to support their mandate of delivering sustainable and inclusive mobility.
While the STC data are particularly interesting because they deliver key variables of travel demand (e.g. trip generation, frequency, distance, time of day, dwelling time), a number of heuristics will have to be developed in order to derive useful indicators of travel, ascertain the coverage of the data, understand the extent and operation of bias and link the indicators to wider social domains, such as exclusion or health and well-being. Travel demand is a derived demand and may be understood as traces of all sorts of daily activities, which aggregated across population groups, neighbourhoods or regions may well add up to a detailed, multi-level understanding of urban dynamics. Current work on using STC data is still in its infancy, especially in relation to efforts to extend analytics beyond operational questions of the transport sector. Within the research (and in accordance to existing interests at TfWM), it will need to be established, how many residents are represented in routinely collected datasets. This question is related to bias in Big Data and hence relates to a common concern in Big Data Analytics. Specific methods of turning the data into information include strategies and techniques to (1) generate daily mobility profiles from travel transactions, (2) link trips to daily activities, e.g. by inferring trip destinations and purposes and (3) infer demographics from mobility profiles. As part of these undertakings, the student will need to make use of the implicit geographic information available in the data through data linkage of STC-recorded boardings and GPS vehicle tracking data. The research thus links to the long-established geodemographics with geo-spatial research undertaken by Paul Longley's team (e.g. Longley et al 2016, Longley et al 2015). The resulting expertise from these steps will enable the student to both develop research frameworks of Big Data Analytics in social science and address challenges in the transport sector.
While the STC data are particularly interesting because they deliver key variables of travel demand (e.g. trip generation, frequency, distance, time of day, dwelling time), a number of heuristics will have to be developed in order to derive useful indicators of travel, ascertain the coverage of the data, understand the extent and operation of bias and link the indicators to wider social domains, such as exclusion or health and well-being. Travel demand is a derived demand and may be understood as traces of all sorts of daily activities, which aggregated across population groups, neighbourhoods or regions may well add up to a detailed, multi-level understanding of urban dynamics. Current work on using STC data is still in its infancy, especially in relation to efforts to extend analytics beyond operational questions of the transport sector. Within the research (and in accordance to existing interests at TfWM), it will need to be established, how many residents are represented in routinely collected datasets. This question is related to bias in Big Data and hence relates to a common concern in Big Data Analytics. Specific methods of turning the data into information include strategies and techniques to (1) generate daily mobility profiles from travel transactions, (2) link trips to daily activities, e.g. by inferring trip destinations and purposes and (3) infer demographics from mobility profiles. As part of these undertakings, the student will need to make use of the implicit geographic information available in the data through data linkage of STC-recorded boardings and GPS vehicle tracking data. The research thus links to the long-established geodemographics with geo-spatial research undertaken by Paul Longley's team (e.g. Longley et al 2016, Longley et al 2015). The resulting expertise from these steps will enable the student to both develop research frameworks of Big Data Analytics in social science and address challenges in the transport sector.