A review of research methods on the coupling relationship between urban rail transit and urban space: revealing spatiotemporal relationships through big data

Figures & data

Figure 1. Schematic Representation of the Impact of Urban URT on Urban Spatial Configuration.

Figure 2. Schematic diagram of coupling relationship between urban space and URT.

Figure 3. Schematic Representation of the Temporal Effectiveness and Sequencing in the Coupling Relationship between Urban URT and Urban Spatial Configuration.

Figure 4. Keyword distribution maps obtained by keyword analysis using CiteSpace.

Figure 5. Cluster distribution maps obtained by co-citation and coupling analysis using CiteSpace.

Table 1. Classification and data sources of functional elements.

Element Classification	Functional Elements	Data Sources
‘Behavior’ Functional Elements	City Residents’ Travel Survey Data (Shen, Chen, and Pan 2016)	Travel Purpose Data
		Mode of Transportation Survey Data
		Travel Time Data
		Travel Distance Data
		Data on Transportation Mode Selection
		Survey Data on Transfer Behavior
		Travel Frequency Data
		Survey Data on Time Distribution
	Data on Urban Transportation Behavior and Socioeconomic Characteristics (Tosa et al. 2018; Bardaka and Hersey 2019)	Correlation Data between Passenger Travel and Demographic Features
		Correlation Data between Passenger Travel and Income Levels
		Correlation Data between Passenger Travel and Residence Type
		Correlation Data between Passenger Travel and Educational Levels
	Urban Traffic Flow Data (Teng and Wang 2018; Wang and Cai 2019)	Traffic Flow Data for Road Segments and Roads
		Vehicle and Pedestrian Flow Data for Intersections and Crossroads
		Traffic Congestion and Bottleneck Data
	Passenger Travel Behavior Data within URT Systems (Eom et al. 2019; Lu et al. 2021; Jie et al. 2022)	Time and Location Data for Passengers Using URT
		Data on Passenger Travel Purposes and Modes
		Data on Passenger Travel Frequency and Duration
		Data on Passenger Transfer Behavior
		Data on Passenger Satisfaction and Experience Ratings
	URT Ticketing and Intelligent System Data (Gong, Lin, and Duan 2017; Zhao et al. 2017; Wang et al. 2018; Shen et al. 2020a; Tang et al. 2020; Xue et al. 2020; Deng et al. 2021)	URT Fare and Ticket Type Data
		URT Ticket Revenue and Cost Data
		Electronic Ticketing System and Smart Card Data
		Automatic Access Control and Passenger Flow Monitoring Data
	URT Network Data (Liu, Yao, and Li 2019; Wang et al. 2020; Luo et al. 2023)	URT Line and Station Data
		URT Operation and Scheduling Data
		URT Vehicle and Equipment Data
		URT Economic Benefit Data
		Complementary and Competitive Data between URT and Other Modes of Transportation
	Data on the Impact of Urban Space on URT (Lu et al. 2022)	Data on the Impact of Urban Land Use on URT Passenger Flow
		Data on the Impact of Urban Morphology on URT Passenger Flow
		Data on the Impact of Urban Development Strategies on URT Demand
	Traffic Behavior Model Data (Zhou et al. 2019; Luan et al. 2020)	Passenger Behavior Model Parameters and Demand Model Data
		Data on Travel Mode and Transportation Mode Choice Models
		Data on Transfer Behavior and Path Choice Models
	Traffic Demand Prediction Data (Gao et al. 2021)	Future Population and Employment Growth Forecast Data
		Urban Planning and Land Use Change Forecast Data
		Forecast Data on Urban Transportation Mode Transition
‘Spatial’ Functional Elements	Traditional Urban Spatial Attribute Data (Mbuh, Wheeler, and Cook 2021)	Land Use Data
		Land Use Change Data
		Urban Structure Data
		Environmental Data
		Urban Transportation Facility Data
		Underground Space Utilization Data
	Data on Innovative Urban Spatial Attributes (Zhuang, Zhang, and Lu 2022)	Urban Landscape and Visualization Data
		Urban Planning and Architectural Design Data
		Mobile Communication Data
		Geotagging Data
		Social Media Data
	Data on the Impact of URT on Urban Space (Zou, Xu, and Gao 2008)	Data on the Impact of URT on Urban Land Use
		Data on the Impact of URT on Urban Morphology
		Data on the Impact of URT on Urban Development Strategies
		Data on the Impact of URT on Environmental Quality
		Data on the Impact of URT on Socioeconomic Factors

Figure 6. Conceptual Framework Diagram Illustrating the Coupling Relationship between Urban URT and Urban Spatial Structure.

Shen, Q., P. Chen, and H. X. Pan. 2016. “Factors Affecting car Ownership and Mode Choice in Rail Transit-Supported Suburbs of a Large Chinese City.” Transportation Research Part A: Policy and Practice 94: 31–44. https://doi.org/10.1016/j.tra.2016.08.027.

 Web of Science ®Google Scholar

Tosa, C., H. Sato, T. Morikawa, and T. Miwa. 2018. “Commuting Behavior in Emerging Urban Areas: Findings of a Revealed-Preferences and Stated-Intentions Survey in Cluj-Napoca, Romania.” Journal of Transport Geography 68: 78–93. https://doi.org/10.1016/j.jtrangeo.2018.02.011.

 Web of Science ®Google Scholar

Bardaka, E., and J. Hersey. 2019. “Comparing the Travel Behavior of Affordable and Market-Rate Housing Residents in the Transit-Rich Neighborhoods of Denver, CO.” Travel Behaviour and Society 15: 74–87. https://doi.org/10.1016/j.tbs.2019.01.001.

 Web of Science ®Google Scholar

Teng, Z. Y., and C. Wang. 2018. “Research and Analysis of Passenger Flow Constraints in Urban Rail Transit.” Paper Presented at the International Symposium on Social Science and Management Innovation (SSMI), Xian, PEOPLES R CHINA, Dec 15-16.

 Google Scholar

Wang, Z., X. Y. Cai, and Ieee. 2019. “Research on Passenger Flow Prediction of Beijing Subway Based on Spatiotemporal Correlation Analysis.” Paper Presented at the 4th IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, PEOPLES R CHINA, Apr 12-15.

 Google Scholar

Eom, J. K., J. Choi, M. S. Park, and T. Y. Heo. 2019. “Exploring the Catchment Area of an Urban Railway Station by Using Transit Card Data: Case Study in Seoul.” Cities 95: 102364. https://doi.org/10.1016/j.cities.2019.05.033.

 Web of Science ®Google Scholar

Lu, K., J. T. Liu, X. S. Zhou, and B. M. Han. 2021. “A Review of Big Data Applications in Urban Transit Systems.” Ieee Transactions on Intelligent Transportation Systems 22 (5): 2535–2552. https://doi.org/10.1109/TITS.2020.2973365.

 Web of Science ®Google Scholar

Jie, Z., S. N. Ding, B. G. Guo, Z. C. Ma, and J. M. Jin. 2022. “Research on temporal and spatial distribution characteristics of passenger flow of Daxing airport line based on automatic fare collection data.” Expert Systems, 11. https://doi.org/10.1111/exsy.13009.

 Web of Science ®Google Scholar

Gong, Y. X., Y. Y. Lin, and Z. Y. Duan. 2017. “Exploring the Spatiotemporal Structure of Dynamic Urban Space Using Metro Smart Card Records.” Computers, Environment and Urban Systems 64: 169–183. https://doi.org/10.1016/j.compenvurbsys.2017.02.003.

 Web of Science ®Google Scholar

Zhao, J. J., Q. Qu, F. Zhang, C. Z. Xu, and S. Y. Liu. 2017. “Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data.” Ieee Transactions on Intelligent Transportation Systems 18 (11): 3135–3146. https://doi.org/10.1109/TITS.2017.2679179.

 Web of Science ®Google Scholar

Wang, Z. Y., Y. X. Hu, P. Zhu, Y. Qin, and L. M. Jia. 2018. “Ring Aggregation Pattern of Metro Passenger Trips: A Study Using Smart Card Data.” Physica A: Statistical Mechanics and its Applications 491: 471–479. https://doi.org/10.1016/j.physa.2017.08.105.

 Web of Science ®Google Scholar

Shen, Ping, Linxin Ouyang, Chong Wang, Yin Shi, and Yiheng Su. 2020a. “Cluster and Characteristic Analysis of Shanghai Metro Stations Based on Metro Card and Land-use Data.” Geo-Spatial Information Science 23 (4): 352–361. https://doi.org/10.1080/10095020.2020.1846463.

 Web of Science ®Google Scholar

Tang, L. Y., Y. Zhao, K. L. Tsui, Y. X. He, and L. W. Pan. 2020. “A Clustering Refinement Approach for Revealing Urban Spatial Structure from Smart Card Data.” Applied Sciences 10 (16): 5606. https://doi.org/10.3390/app10165606.

 Google Scholar

Xue, G., D. Q. Gong, J. H. Zhang, P. Zhang, and Q. M. Tai. 2020. “Passenger Travel Patterns and Behavior Analysis of Long-Term Staying in Subway System by Massive Smart Card Data.” Energies 13 (10): 2670. https://doi.org/10.3390/en13102670.

 Web of Science ®Google Scholar

Deng, Y., J. X. Wang, C. Gao, X. H. Li, Z. Wang, and X. L. Li. 2021. “Assessing Temporal-Spatial Characteristics of Urban Travel Behaviors from Multiday Smart-Card Data.” Physica A: Statistical Mechanics and its Applications 576: 126058. https://doi.org/10.1016/j.physa.2021.126058.

 Web of Science ®Google Scholar

Liu, S. S., E. J. Yao, and B. B. Li. 2019. “Exploring Urban Rail Transit Station-Level Ridership Growth with Network Expansion.” Transportation Research Part D: Transport and Environment 73: 391–402. https://doi.org/10.1016/j.trd.2018.04.006.

 Web of Science ®Google Scholar

Wang, Z. J., J. Q. Li, L. P. Huang, and Z. G. Yang. 2020b. “Discovering the Evolution of Beijing Rail Network in Fifty Years.” Modern Physics Letters B 34 (21): 2050212. https://doi.org/10.1142/s0217984920502127.

 Web of Science ®Google Scholar

Luo, Y., J. Y. Zheng, X. Wang, Y. Y. Tao, and X. X. Jiang. 2023. “A Neural Network Based on Spatial Decoupling and Patterns Diverging for Urban Rail Transit Ridership Prediction.” Ieee Transactions on Intelligent Transportation Systems 24 (12): 15248–15258. https://doi.org/10.1109/TITS.2023.3308949.

 Web of Science ®Google Scholar

Lu, W. B., Y. Zhang, C. Q. Ma, B. J. Zhou, and T. Wang. 2022. “Measuring the Relationship Between Influence Factor and Urban Rail Transit Passenger Flow: Correlation or Causality?” Journal of Urban Planning and Development 148 (3): 14. https://doi.org/10.1061/(asce)up.1943-5444.0000870.

 Web of Science ®Google Scholar

Zhou, Y., C. Y. Qian, H. Xiao, J. C. Xin, Z. X. Wei, and Q. Feng. 2019. “Coupling Research on Land Use and Travel Behaviors Along the Tram Based on Accessibility Measurement—Taking Nanjing Chilin Tram Line 1 as an Example.” Sustainability 11 (7): 2034. https://doi.org/10.3390/su11072034.

 Web of Science ®Google Scholar

Luan, X., L. Cheng, Y. Song, and J. B. Zhao. 2020. “Better Understanding the Choice of Travel Mode by Urban Residents: New Insights from the Catchment Areas of Rail Transit Stations.” Sustainable Cities and Society 53: 101968. https://doi.org/10.1016/j.scs.2019.101968.

 Web of Science ®Google Scholar

Gao, F. C., Z. Q. Wang, Z. G. Liu, and Ieee. 2021. “Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction.” Paper Presented at the 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA), Electr Network, Oct 06-09.

 Google Scholar

Mbuh, M. J., R. Wheeler, and A. Cook. 2021. “Spatiotemporal Analysis of Urban Heat Island Intensification in the City of Minneapolis-St. Paul and Chicago Metropolitan Areas Using Landsat Data from 1984 to 2016.” Geocarto International 36 (14): 1565–1590. https://doi.org/10.1080/10106049.2019.1655802.

 Web of Science ®Google Scholar

Zhuang, X. Y., L. Zhang, and J. Lu. 2022. “Past—Present—Future: Urban Spatial Succession and Transition of Rail Transit Station Zones in Japan.” International Journal of Environmental Research and Public Health 19 (20): 13633. https://doi.org/10.3390/ijerph192013633.

 PubMed Web of Science ®Google Scholar

Zou, X. L., R. H. Xu, and P. Gao. 2008. “Hierarchical Structure of the Mass and Group-Level Behaviors in Urban Rail Transfer Stations.” Paper presented at the International Conference on Pedestrian and Evacuation Dynamics, Wuppertal Univ, Wuppertal, GERMANY, Feb 27-29.

 Google Scholar

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.