Understanding railway usage behavior with ten million GPS records
Document Type
Journal Article
Publication Date
2023
Subject Area
infrastructure - station, land use - planning, mode - rail, place - asia, place - urban, ridership - behaviour, technology - geographic information systems
Keywords
Big data, Built environment, Public transit, Railway usage behavior, Tokyo
Abstract
Considering the essential role of public railway transport in urban planning and development, it is crucial to understand railway travel behavior. With data and survey cost limitations, the railway travel behavior considered in previous studies suffers from a lack of comprehensiveness. Based on millions of GPS records, we analyzed railway usage behavior and then classified 36 stations on the JR Yamanote and JR Chuo lines in Tokyo based on railway usage behavior. To explore the built environment characteristics of each classification and the relationship between railway usage behavior and the built environment, we generated a series of built environment indicators based on the functional attributes around stations. The results show that railway usage behavior in non-secondary catchment areas correlates strongly with the commercial and functional attributes of stations. For non-core catchment areas around stations, consumption level, housing price, and passenger load are the main influencing indicators. Beyond these analysis results, we also present a policy discussion on railway station construction. We found that GPS records constitute valuable information for assessing station usage and neighborhood construction. These fundings can assist urban planners in performing better land use planning and development assessments for future cities.
Rights
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Recommended Citation
Jin, Y., Li, P., Chen, Z., Bharule, S., Jia, N., Chen, J., Song, X., Shibasaki, R., & Zhang, H, (2023). Understanding railway usage behavior with ten million GPS records. Cities, Vol. 133, 104117.
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