Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing. (September 2015)

• Record Type: Journal Article
• Authors: Long, Ying; Thill, Jean-Claude
• Is Part Of: Computers, environment and urban systems. Volume 53(2015)
• Page Start: 19
• Page End: 35
• Publication Date: 2015-09
• Subjects: Bus smart card data -- Jobs–housing spatial mismatch -- Commuting trip -- Rule-based -- Beijing; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2015.02.005
• Languages: English
• ISSNs: 0198-9715

Abstract

Graphical abstract: Highlights: Using 78-million-trip bus smart card data (SCD) of Beijing. Setting rules from household travel survey for mining SCD. Evaluating and visualizing bus commuting patterns of Beijing. Analyzing commuting patterns of typical residence communities and business zones. Abstract: Location Based Services (LBS) provide a new perspective for spatiotemporally analyzing dynamic urban systems. Research has investigated urban dynamics using LBS. 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 large cities in China, and even in the world. This paper combines bus SCD for a one-week period with a oneday 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 are proposed, one for jobs–housing identification and the other 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 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.