Who, Where, Why and When? Using Smart Card and Social Media Data to Understand Urban Mobility

Abstract

This study describes the integration and analysis of travel smart card data (SCD) with points of interest (POIs) from social media for a case study in Shenzhen, China. SCD ticket price with tap-in and tap-out times was used to identify different groups of travellers. The study examines the temporal variations in mobility, identifies different groups of users and characterises their trip purpose and identifies sub-groups of users with different travel patterns. Different groups were identified based on their travel times and trip costs. The trip purpose associated with different groups was evaluated by constructing zones around metro station locations and identifying the POIs in each zone. Each POI was allocated to one of six land use types, and each zone was allocated a set of land use weights based on the number of POI check-ins for the POIs in that zone. Trip purpose was then inferred from trip time linked to the land use at the origin and destination zones using a novel “land use change rate” measure. A cluster analysis was used to identify sub-groups of users based on individual temporal travel patterns, which were used to generate a novel “boarding time profile”. The results show how different groups of users can be identified and the differences in trip times and trip purpose quantified between and within groups. Limitations of the study are discussed and a number of areas for further work identified, including linking to socioeconomic data and a deeper consideration of the timestamps of POI check-ins to support the inference of dynamic and multiple land uses at one location. The methods and metrics developed by this research use social media POI data to semantically contextualise information derived from the SCD and to overcome the drawbacks and limitations of traditional travel survey data. They are novel and generalizable to other studies. They quantify spatiotemporal mobility patterns for different groups of travellers and infer how their purposes of their journeys change through the day. In so doing, they support a more nuanced and detailed view of who, where, when and why people use city spaces.

Keywords

• smart card data
• individual mobility
• urban analytics
• big data
• social media