Measuring the Relationship between Influence Factor and Urban Rail Transit Passenger Flow: Correlation or Causality?
Publication: Journal of Urban Planning and Development
Volume 148, Issue 3
Abstract
The analysis of factors influencing urban rail transit (URT) passenger flow is often a precondition for establishing prediction models. Much past analysis has focused on correlation analysis and does not draw on research on causal mechanisms. In this paper, a novel causal inference method based on transfer information entropy (TIE) is proposed to determine the causality between influence factor and URT passenger flow. As a comparison, the matrix correlation coefficient (RV2 coefficient) is used to analyze the correlation. Taking the URT system in Xi’an, Shaanxi, China, as an example, the factors that may affect passenger flow are introduced and the causality and correlation are calculated. Compared with correlation analysis, the causal inference method can be used to derive the interactive relationship between influencing factors and passenger flow. At the same time, the causal inference method has greater adaptability to the type of passenger flow and the scope of influence. The result can be used for the theoretical support of transit-oriented development (TOD), and can also provide a reference for road traffic planning.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research was supported by the National Natural Science Foundation of China (Grant No. 72071041).
References
Budhathoki, K., and J. Vreeken. 2016. “Causal inference by compression.” In Proc., 2016 IEEE 16th Int. Conf. on Data Mining, 41–50. Piscataway, NJ: IEEE Press.
Cervero, R. 1996. “Mixed land-uses and commuting: Evidence from the American Housing Survey.” Transp. Res. Part A: Policy and Prac. 30 (5): 361–277. https://doi.org/10.1016/0965-8564(95)00033-X.
Condurat, M. 2016. “Chains of causality associated with the environmental impact of road transport system.” J. Sustainable Archit. Civ. Eng. 14 (1): 20–30. https://doi.org/10.5755/j01.sace.14.1.14658.
Cui, G. H., and X. S. Li. 2010. “Interactive mechanism of cointegration and causality between regional logistics and economic development.” J. Traffic Transp. Eng. 10 (5): 90–96.
Du, H., and C. Mulley. 2007. “The short-term land value impacts of urban rail transit: Quantitative evidence from Sunderland, UK.” Land Use Policy 24 (1): 223–233. https://doi.org/10.1016/j.landusepol.2005.12.003.
Goldfarb, D., and S. Evans. 2019. “Causal inference via conditional Kolmogorov complexity using MDL binning.” arXiv:1911.00332 [cs.LG].
Guo, Y., X. Wang, Q. Xu, S. Liu, and J. Han. 2020. “Weather impact on passenger flow of rail transit lines.” Civ. Eng. J. 6 (2): 276–284. https://doi.org/10.28991/cej-2020-03091470.
Guzman, L. A. 2019. “A strategic and dynamic land-use transport interaction model for bogotá and its region.” Transportmetrica B Transp. Dyn. 7 (1): 707–725.
Hakim, M. M., and R. Merkert. 2016. “The causal relationship between air transport and economic growth: Empirical evidence from South Asia.” J. Transp. Geogr. 56 (2): 120–127. https://doi.org/10.1016/j.jtrangeo.2016.09.006.
Han, P. W., and L. Nie. 2018. “Analysis on passenger flow changes during holidays—a case study of Beijing-Shanghai high-speed railway.” IOP Conf. Ser.: Earth Environ. Sci. 189 (4): 062051. https://doi.org/10.1088/1755-1315/189/6/062051.
Hasan, M. M., and J. Kim. 2016. “Analysing functional connectivity and causal dependence in road traffic networks with Granger causality.” In Proc., Australasian Transport Research Forum, 19. Melbourne, Australia: Australasian Transport Research Forum Incorporated.
He, Y., Y. Zhao, and K. L. Tsui. 2020. “Modeling and analyzing modeling and analyzing impact factors of metro station ridership: An approach based on a general estimating equation factors influencing metro station ridership: An approach based on general estimating equation.” IEEE Intell. Transp. Syst. Mag. 12 (4): 195–207. https://doi.org/10.1109/MITS.5117645.
Hongguo, X., Z. Huiyong, and Z. Fang. 2010. “Bayesian network-based road traffic accident causality analysis.” Vol. 3 of Proc., 2010 WASE Int. Conf. on Information Engineering, 413–417. Hebei, China: ICIE.
Janzing, D., and B. Schölkopf. 2010. “Causal inference using the algorithmic Markov condition.” IEEE Trans. Inf. Theory 56 (10): 5168–5194. https://doi.org/10.1109/TIT.2010.2060095.
Jiang, Y., P. Gu, Z. Cao, and Y. Chen. 2020. “Impact of transit-oriented development on residential property values around urban rail stations.” Transp. Res. Rec. J. Transp. Res. Board 2674 (384): 036119812091105.
Kim, C., L. R. F. Henneman, C. Choirat, and C. M. Zigler. 2020. “Health effects of power plant emissions through ambient air quality.” J. R. Stat. Soc. A 617: 661–673.
Kim, M.-K., S.-P. Kim, J. Heo, and H.-G. Sohn. 2017. “Ridership patterns at subway stations of Seoul capital area and characteristics of station influence area.” KSCE J. Civ. Eng. 21 (3): 964–975. https://doi.org/10.1007/s12205-016-1099-8.
Li, D., and J. Peng. 2018. “Study on the correlation between passenger flow characteristics of metro transit and land use—taking Daxing district of Beijing as an example.” In Proc. 2nd Int. Conf. on Economics and Management, Education, Humanities and Social Sciences, 25–29, Paris: Atlantis Press.
Li, S., D. Lyu, X. Liu, Z. Tan, F. Gao, G. Huang, and Z. Wu. 2020. “The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou.” Cities 99 (1): 102580. https://doi.org/10.1016/j.cities.2019.102580.
Li, X., Y. Liu, Z. Gao, and D. Liu. 2016. “Linkage between passenger demand and surrounding land-use patterns at urban rail transit stations: A canonical correlation analysis method and case study in Chongqing.” Int. J. Transp. Sci. Technol. 5 (1): 10–16. https://doi.org/10.1016/j.ijtst.2016.06.002.
Liu, Y., M. Tang, Z. Wu, Z. Tu, Z. An, N. Wang, and Y. Li. 2020. “Analysis of passenger flow characteristics and their relationship with surrounding urban functional landscape pattern.” Trans. GIS 24 (6): 1602–1629. https://doi.org/10.1111/tgis.v24.6.
Meng, X., and J. Han. 2016. “Roads, economy, population density, and CO2: A city-scaled causality analysis.” Resour. Conserv. Recycl. 128 (11): 508–515.
Miller, J., N. Garber, and S. Korukonda. 2011. “Understanding causality of intersection crashes.” Transp. Res. Rec. J. Transp. Res. Board 2236 (1): 110–119. https://doi.org/10.3141/2236-13.
Mützel, C. M., and J. Scheiner. 2021. “Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data.” Public Transp. 1: 1–24. https://doi.org/10.1007/s12469-021-00280-2.
Pan, H., and M. Zhang. 2008. “Rail transit impacts on land use: Evidence from Shanghai, China.” Transp. Res. Rec. J. Transp. Res. Board 2048 (1): 16–25. https://doi.org/10.3141/2048-03.
Peftitsi, S., E. Jenelius, and O. Cats. 2020. “Determinants of passengers’ metro car choice revealed through automated data sources: A Stockholm case study.” Transportmetrica A Transp. Sci. 16 (3): 529–549. https://doi.org/10.1080/23249935.2020.1720040.
Ramsay, J. O., J. ten Berge, and G. P. H. Styan. 1984. “Matrix correlation.” Psychometrika 49 (3): 403–423. https://doi.org/10.1007/BF02306029.
Rohit, S., and N. Peter. 2018. “Does urban rail increase land value in emerging cities? Value uplift from Bangalore metro.” Transp. Res. Part A Policy Pract. 117 (1): 70–86.
Sánchez, J. N., and M. A. P. Eroles. 2018. “Causal analysis of aircraft turnaround time for process reliability evaluation and disruptions’ identification.” Transportmetrica B Transp. Dyn. 6 (2): 115–128.
Shannon, C. E. 1948. “A mathematical theory of communication.” Bell Syst. Tech. J. 27 (3): 379–423. https://doi.org/10.1002/bltj.1948.27.issue-3.
Shen, P., L. Ouyang, C. Wang, Y. Shi, and Y. Su. 2020. “Cluster and characteristic analysis of Shanghai metro stations based on metro card and land-use data.” Geo-spatial Inf. Sci. 23 (4): 352–361. https://doi.org/10.1080/10095020.2020.1846463.
Smilde, A. K., H. A. L. Kiers, S. Bijlsma, C. M. Rubingh, and E. M. J. Van. 2008. “Matrix correlations for high-dimensional data: The modified RV-coefficient.” Bioinformatics 25 (3): 401–405. https://doi.org/10.1093/bioinformatics/btn634.
Sun, B. 2018.“Characteristics analysis of passenger flow on rail transit network in Suzhou based on AFC system.” Urban Public Transport 4: 1–12.
Sun, Y., J. Li, J. Liu, C. Chow, B. Sun, and R. Wang. 2015. “Using causal discovery for feature selection in multivariate numerical time series.” Mach. Learn. 101 (1–3): 377–395. https://doi.org/10.1007/s10994-014-5460-1.
Tao, Z., and J. Tang. 2019. “Real-time estimation of urban rail transit passenger flow status based on multi-source data.” J. Phys.: Conf. Ser. 1187 (5): 052070–052081.
Tasios, D., C. Tjortjis, and A. Gregoriades. 2019. “Mining traffic accident data for hazard causality analysis.” In Proc., 2019 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conf., 1–6, Piraeus, Greece: IEEE Press.
Teng, S. H. 2013. “Analysis of the relationship between land use, development, and construction near a rail traffic station and passenger flow volume: The case of Tianjin metro line 1.” Appl. Mech. Mater. 357–360: 1856–1862. https://doi.org/10.4028/www.scientific.net/AMM.357-360.
Tong, T. T., and L. Q. Cheng. 2010. “Research on attraction scope of urban rail transit station to the conventional bus passenger flow.” Vol. 23 of Proc., 2010 Int. Conf. on Intelligent Computation Technology and Automation, 1094–1097, Changsha, China: IEEE Press.
Vigen, T. 2015. “Spurious correlations.” Futures 512: 73.
Wang, H., L. Li, P. Pan, Y. Wang, and Y. Jin. 2019. “Early warning of burst passenger flow in public transportation system.” Transp. Res. Part C Emerging Technol. 105: 580–598. https://doi.org/10.1016/j.trc.2019.05.022.
Wang, J., B. Leng, J. Wu, H. Du, and Z. Xiong. 2020. “Metroeye: A weather-aware system for real-time metro passenger flow prediction.” IEEE Access 8: 129813–129829. https://doi.org/10.1109/Access.6287639.
Wang, J., N. Zhang, H. Peng, Y. Huang, and Y. Zhang. 2022. “Spatiotemporal heterogeneity analysis of influence factor on urban rail transit station ridership.” J. Transp. Eng. Part A Syst. 148 (2): 04021115. https://doi.org/10.1061/JTEPBS.0000639.
Wei, M. 2022. “How does the weather affect public transit ridership? A model with weather-passenger variations.” J. Transp. Geogr. 98 (1): 103242. https://doi.org/10.1016/j.jtrangeo.2021.103242.
Weiwei, W., and S. Xinghua. 2015. “Study on regular pattern of railway passener flow in three-daw holiday based on clustering method of time series.” Railway Comput. Appl. 24 (4): 23–27.
Xue, F., E. Yao, N. Huan, B. Li, and S. Liu. 2020. “Prediction of urban rail transit ridership under rainfall weather conditions.” J. Transp. Eng. Part A Syst. 146 (7): 1–12. https://doi.org/10.1061/JTEPBS.0000383.
Yi, Z., and D. Mi. 2016. “The impacts of urban mass rapid transit lines on the density and mobility of high-income households: A case study of Singapore.” Transp. Policy 51 (1): 70–80.
Zhang, J., F. Chen, Z. Cui, Y. Guo, and Y. Zhu. 2020. “Deep learning architecture for short-term passenger flow forecasting in urban rail transit.” IEEE Trans. Intell. Transp. Syst. 22 (11): 7004–7014. https://doi.org/10.1109/TITS.2020.3000761.
Zhang, S.-J., S.-P. Jia, Y. Bai, B.-H. Mao, C.-R. Ma, and T. Zhang. 2018. “Optimal adjustment schemes on the long through-type bus lines considering the urban rail transit network.” Discrete Dyn. Nat. Soc. 2018 (3): 1–15.
Zhao, L., and L. Shen. 2018. “The impacts of rail transit on future urban land use development: A case study in Wuhan, China.” Transp. Policy 81 (1): 396–405.
Zheng, Z., W. Jiancheng, W. Lixun, and S. Liying. 2015. “Study on the influencing factors and passenger volume analysis of lasting large scale activities.” In Proc., 2015 Int. Conf. on Transportation Information and Safety, 414–418, Wuhan, China: IEEE Press.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Nov 23, 2021
Accepted: May 6, 2022
Published online: Jun 24, 2022
Published in print: Sep 1, 2022
Discussion open until: Nov 24, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Zhenjun Zhu, Yong Zhang, Shucheng Qiu, Yunpeng Zhao, Jianxiao Ma, Zhanpeng He, Ridership Prediction of Urban Rail Transit Stations Based on AFC and POI Data, Journal of Transportation Engineering, Part A: Systems, 10.1061/JTEPBS.TEENG-7808, 149, 9, (2023).