Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing

Abstract

Location Based Services (LBS) provide a new perspective for spatiotemporally analyzing dynamic urban systems. Research has investigated urban dynamics using GSM (Global System for Mobile Communications), GPS (Global Positioning System), SNS (Social Networking Services) and Wi-Fi techniques. However, less attention has been paid to the analysis of urban structure (especially commuting pattern) using smart card data (SCD), which are widely available in most cities. Additionally, ubiquitous LBS data, although providing rich spatial and temporal information, lacks rich information on the social dimension, which limits its in-depth application. To bridge this gap, this paper combines bus SCD for a one-week period with a one-day household travel survey, as well as a parcel-level land use map to identify job-housing locations and commuting trip routes in Beijing. Two data forms (TRIP and PTD) are proposed, with PTD used for jobs-housing identification and TRIP used for commuting trip route identification. The results of the identification are aggregated in the bus stop and traffic analysis zone (TAZ) scales, respectively. Particularly, commuting trips from three typical residential communities to six main business zones are mapped and compared to analyze commuting patterns in Beijing. The identified commuting trips are validated on three levels by comparison with those from the survey in terms of commuting time and distance, and the positive validation results prove the applicability of our approach. Our experiment, as a first step toward enriching LBS data using conventional survey and urban GIS data, can obtain solid identification results based on rules extracted from existing surveys or censuses.

Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data

“…J. Geo-Inf. 2017, 6, 318 4 of 21 form of stations or bus stops [19]. Map visualization also evolved over a period of time with more granularity in visualization combined with visual analytics.…”

“…Job housing location and commuting pattern [19] GIS platform 7 days Interactive visualization of human mobility with activity context [20] -1 week…”

Finding Pearls in London's Oysters

“…In terms of general properties of transit systems from such data, most of the work on different applications in places such as Shenzen (Gong et al, 2012), Beijing (Long and Thill, 2015), Santiago (Munizaga and Palma, 2012), Singapore (Zhong et al, 2015), and London (Gordon et al, 2013) deal with the general properties of these systems as well as ways of scaling trip movements to produce information about the locational activities associated with the places where people access the transit systems in question. In terms of extracting more detail on movement, there has been less progress because much of this depends on linking such data sets to cognate data and this requires a common key to enable integration of quite different data sets.…”

Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data

“…These data, however, only provide information on public transport travel demand, neglecting overall travel demand, although this should be taken into account by public transport operators (5). Travel habit surveys are traditionally used to collect data for estimating and analyzing the demand for transport (6,7). Travel habit surveys are used to analyze passenger demand and preferences per modality, journey purpose, and travel attributes (6,8).…”

“…Several data fusion studies considered either smartcard data or GSM data combined with travel habit surveys to successfully estimate trip purposes (7,15). A pilot data fusion study was performed in Emmen, the Netherlands, where smartcard and GSM data were fused to find areas with the potential to support additional public transport (16).…”