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.

Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

“…Their research shows that Twitter data are a potential suitable source of data to analyze activity patterns. On the other hand, Xuan et al [43] analyzed different group's behavior using the smart card data (SCD) of Shenzhen combined with social media data. Focusing on students and travelers, they revealed useful insights into travel flows in both aspects of the spatial and temporal characteristics.…”

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

“…The identification of these secondary activities is not only beneficial to transport planners for a better appreciation of individuals' travel behaviour but also for commercial organizations in the context of consumer behaviour (Goulet-Langlois 2016) and for economists, providing a useful insight into the quality of life and aspiration (Nakamura et al, 2016). This expands the scope of research from urban transportation in travel behaviour and mobility (Yang et al 2019), trip purposes (Alsger et al, 2018), accessibility (Saif, Zefreh, and Torok 2019) to social studies (Zhu et al 2017) Secondary activities were investigated in the literature using activity-travel demand models derived from conventional travel surveys (Arentze and Timmermans 2007; Rasouli and Timmermans 2015). A relatively small sample size (only a one-day travel dairy) was used to estimate travel demand for the whole population.…”

“…(Bagchi and White 2005). Notwithstanding the wide range of positive characteristics, smart card data present several challenges such as: estimating a commuter's destination if public transport does not ask for alighting information (Gordon et al 2013), making demographic predictions if socio-demographic information is not accessible due to privacy concerns (Zhang, Cheng, and Sari Aslam 2019;Zhang and Cheng 2020), detecting activities in order to estimate a trip's purpose by linking smart card data with auxiliary data sources, such as land use maps and POIs (Devillaine, Munizaga, and Trépanier 2012; Kuhlman 2015; Sari Aslam and Cheng 2018;Yang et al 2019).…”

“…Mining activity patterns from smart card data is crucial to having an accurate estimate of travel purpose (Ma et al 2013(Ma et al , 2017. However, activity identification and inferring models using smart card data rarely implemented on public transport net works, even in cases where they have been applied, the scope of the models has been limited to primary activities (Chu and Chapleau 2010;Chakirov and Erath 2012;Devillaine, Munizaga, and Trépanier 2012;Zou et al, 2016;Yang et al 2019) except for Wang et al (2017) and Alsger et al (2018). According to Wang et al (2017), after-work activities were defined using time constrains once home and work locations had been identified within a sequence of activity patterns.…”

Anomalous Urban Mobility Pattern Detection Based on GPS Trajectories and POI Data

“…Urban mobility research studies the mobile flow of residents in urban areas [1][2][3][4][5][6]. The majority of human movement flows behave with a certain regularity, which are called normal urban mobility patterns.…”