Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London

Abstract

The large volume of data automatically collected by smart card fare systems offers a rich source of information regarding daily human activities with a high resolution of spatial and temporal representation. This provides an opportunity for aiding transport planners and policy-makers to plan transport systems and cities more responsively. However, there are currently limitations when it comes to understanding the secondary activities of individual commuters. Accordingly, in this paper, we propose a framework to detect and infer secondary activities from individuals' daily travel patterns from the smart card data and reduce the use of conventional surveys. First, we proposed a 'heuristic secondary activity identification algorithm', which uses commuters' primary locations (home & work) and the direction (from & to) information to identify secondary activities for individuals. The algorithm provides a high-level classification of the activity types as beforework, midday and after-work activity patterns of individuals. Second, this classification is semantically enriched using Points of Interests to provide meaningful insights into individuals' travel purposes and mobility in an urban environment. Lastly, using the transit data of London as a case study, the model is compared with a volunteer survey to demonstrate its effectiveness and offering a cost-effective method to travel demand research.

ActivityNET: Neural Networks to Predict Trip Purposes in Public Transport from Individual Smart Card Data and POIs.

“…The assumptions of activity extraction are applied in this stage (Sari Aslam et al2020) using transfer time and walking distance between public transit stops, which were assumed to be 15 min ( Transport for London TfL, 2019) and 800 m (RTPI, 2018;Alsger et al, 2018;Sari Aslam et al, 2020), respectively. The resulting dataset consists of 18,232 trip records, which means 9,116 data points (activities) from smart card data.…”

“…POIs from Twitter or Foursquare data have been used to investigate trip purposes, human mobility and urban flows to generate an understanding of transport and urban planning in cities (Chaniotakis, Antoniou and Pereira., 2016;Rashidi et al, 2017). To infer activities from transit data, the highest probability of activity types are used from POIs (Gong, L., X. Liu, L. Wu, 2016;Alsger et al, 2018;Sari Aslam et al, 2020). However, this section explains how both large datasets such as smart card data and land-use information (POIs) ought to be combined in details.…”

“…The second approach of the validation focusses on the comparison of the accuracy obtained from the highest probability of land-use information, which has been widely used in literature (Gong, L., X. Liu, L. Wu, 2016;Alsger et al, 2018;Sari Aslam et al, 2020). Thus, after phase 1, we have inferred the activities from smart card data using the highest probability of POIs as a benchmark model and compared the results with the labelled survey data.…”

“…On the other hand, collecting smart card data has shown great potential for investigating passengers' daily activities at an unprecedented scale such as a much larger population and a longer period of data collection (Anda, Erath and Fourie, 2017). In addition, smart card data reveal an individual's spatial-temporal activity pattern as a sequence of activity locations, activity start and end time, duration of the activity and land-use in the close proximity of alighting or boarding station (Faroqi, Mesbah and Kim, 2018) which could be further explored to derive the trip purpose of the travellers (Sari Aslam and Cheng, 2018;Sari Aslam et al, 2020).…”

“…Thus, longitudinal smart card data with volume and details need to be investigated for trip purposes such as home, work, entertainment, eating, shopping, drop-offs/pick-ups, and part-time work activities. However, the majority of the trip purpose identification models from smart card data are focused on only primary activities such as home and work/school (for adults and students, respectively) (Chakirov and Erath, 2012;Devillaine, Munizaga and Trépanier, 2012;Zou et al, 2016;Yang et al, 2019; Sari Aslam, Cheng and Cheshire, 2019) but rarely secondary activities (Alsger et al, 2018;Sari Aslam et al, 2020). The reason is that the handcrafted rules and number of constraints are limited and reduce the ability to identify trip purposes with high accuracy, specifically for secondary activities (Xiao, Juan and Zhang, 2016;Anda, Erath and Fourie, 2017), which are complex compared to regular commuters' activities.…”