Exploring the Effect of Built Environment Factors on Metro Station Ridership during the Holiday Season – A Case Study of the Beijing Metro System during the Chinese National Day Holidays

Zhenbao WANG; Yanfang HE; Xueqiao ZHAO; Yuqi LIANG; Shihao LI

doi:10.7307/ptt.v37i2.758

Authors

Zhenbao WANG Hebei University of Engineering, School of Architecture and Art
Yanfang HE Hebei University of Engineering, School of Architecture and Art
Xueqiao ZHAO Hebei University of Engineering, School of Architecture and Art
Yuqi LIANG Hebei University of Engineering, School of Architecture and Art
Shihao LI Hebei University of Engineering, School of Architecture and Art

DOI:

https://doi.org/10.7307/ptt.v37i2.758

Keywords:

metro station ridership, pedestrian catchment Areas (PCA), Multi-scale Geographically Weighted Regression (MGWR), built environment, National Day holidays

Abstract

Previous studies have primarily focused on the effect of the built environment on ridership during weekdays and weekends. This paper aims to investigate the spatial heterogeneity of the effect of built environment factors on ridership at metro stations during National Day holidays. Beijing is divided into three zones from inner to outer areas. Taking metro station boarding and alighting ridership during National Dayholidays as the dependent variable, 13 built environment factors were selected as independent variables according to the “7D” dimension of the built environment. The recommended pedestrian catchments (PCA) combinations for the three zones in Beijing are 400 m_500 m_400 m by using the Multi-Scale Geographically Weighted Regression (MGWR) model. We investigated the effect of built environment factors on metro ridership and spatial heterogeneity. The influencing factors that have significant effects on both boarding and alighting ridership are building density, number of commercial facilities, bus lines density, number of entrance and exit, number of office facilities, mixed utilization of land and road density. The MGWR model results are helpful to propose targeted strategies for revitalising the built environment around metro stations.

References

Dong X, et al. Can negative travel habits hinder positive travel behavioural change under Beijing vehicle restrictions? Promet-Traffic&Transportation. 2020;32(5):691-709. DOI: 10.7307/PTT.V32I5.3453.

Crane R, Scweitzer LA. Transport and sustainability: The role of the built environment. Built Environment. 2003;29:238-252. DOI: 10.2148/benv.29.3.238.54286.

Zhao J, et al. What influences metro station ridership in China? Insights from Nanjing. Cities. 2013;35:114-124. DOI: 10.1016/j.cities.2013.07.002.

Holz-Rau C, Scheiner J, Sicks K. Travel distances in daily travel and long-distance travel: What role is played by urban form? Environment and Planning A. 2014;46:488-507. DOI: 10.1068/a4640.

Reichert A, Holz-Rau C. Mode use in long-distance travel. Journal of Transport and Land Use. 2015;8:87-105. DOI: 10.5198/jtlu.2015.576.

Strömblad E, et al. Characteristics of everyday leisure trips by car in Sweden–Implications for sustainability measures. Promet-Traffic&Transportation. 2022;34:657-672. DOI: 10.7307/ptt.v34i4.4039.

Meng Z, Gao X. Beijing Metro will carry nearly 5.8 million passengers on its lines during the National Day holiday. People's Daily Online. 2021. http://bj.people.com.cn/n2/2021/0930/c82840-34939043.html [Accessed 19th Sep. 2024].

Xiang B. More metro lines in smaller Chinese cities. Xinhuanet. 2017. http://www.xinhuanet.com/english/2017-09/08/c_136594680.html [Accessed 19th Sep. 2024].

Yang L, Shen Q, Li Z. Comparing travel mode and trip chain choices between holidays and weekdays. Transportation Research Part A: Policy and Practice. 2016;91:273-285. DOI: 10.1016/j.tra.2016.07.001.

Hu X, et al. Traffic volume forecasting model of freeway toll stations during holidays–An SVM model. Promet-Traffic&Transportation. 2022;34:499-510. DOI: 10.7307/ptt.v34i3.3885.

Kim C, et al. What makes urban transportation efficient? evidence from subway transfer stations in Korea. Sustainability. 2017;9(11):2054. DOI: 10.3390/su9112054.

Liu J, Shi W, Chen P. Exploring travel patterns during the holiday season—A case study of Shenzhen metro system during the Chinese spring festival. ISPRS International Journal of Geo-Information. 2020;9(11):651. DOI: 10.3390/ijgi9110651.

An D, et al. Understanding the impact of built environment on metro ridership using open source in Shanghai. Cities. 2019;93:177-187. DOI: 10.1016/j.cities.2019.05.013.

Estupiñán N, Rodríguez DA. The relationship between urban form and station boardings for Bogotá’s BRT. Transportation Research Part A: Policy and Practice. 2008;42:296-306. DOI: 10.1016/j.tra.2007.10.006.

Sohn K, Shim H. Factors generating boardings at Metro stations in the Seoul metropolitan area. Cities. 2010;27:358-368. DOI: 10.1016/j.cities.2010.05.001.

Sung H, et al. Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership. Journal of Transport Geography. 2014;36:134-140. DOI: 10.1016/j.jtrangeo.2014.03.013.

Cardozo OD, García-Palomares JC, Gutiérrez J. Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Applied Geography. 2012;34:548-558. DOI: 10.1016/j.apgeog.2012.01.005.

Andersson DE, Shyr OF, Yang J. Neighbourhood effects on station-level transit use: Evidence from the Taipei metro. Journal of Transport Geography. 2021;94:103127. DOI: 10.1016/j.jtrangeo.2021.103127.

Chen E, et al. Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data. Cities. 2019;95:102359. DOI: 10.1016/j.cities.2019.05.028.

Li S, et al. Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China. Journal of Transport Geography. 2020;82:102631. DOI: 10.1016/j.jtrangeo.2019.102631.

Blainey S. Trip end models of local rail demand in England and Wales. Journal of Transport Geography. 2010;18(1):153-165. DOI: 10.1016/j.jtrangeo.2008.11.002.

Jun MJ, et al. Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul. Journal of Transport Geography. 2015;48:30-40. DOI: 10.1016/j.jtrangeo.2015.08.002.

Yu H, et al. Inference in multiscale geographically weighted regression. Geographical Analysis. 2019;52:87-106. DOI: 10.1111/gean.12189.

Fotheringham AS, Yang W, Kang W. Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers. 2017;107(6):1247-1265. DOI: 10.1080/24694452.2017.1352480.

Shen T, et al. On hedonic price of second-hand houses in Beijing based on multi-scale geographically weighted regression: Scale law of spatial heterogeneity. Econ. Geogr. 2020;0:75-83. DOI: 10.15957/j.cnki.jjdl.2020.03.009.

Chen Z, Deng S. Research on the relationship between block morphology and heat island intensity in Guangzhou’s main urban area based on multi-scale geographically weighted regression. Intell. Build. Smart City. 2021;10:13-17. DOI: 10.13655/j.cnki.ibci.2021.10.003.

Zhou Z, Cheng X. Spatial heterogeneity of influencing factors of PM2. 5 in Chinese cities based on MGWR model. China Environ. Sci. 2021;41:2552-2561. DOI: 10.19674/j.cnki.issn1000-6923.2021.0267.

Wang Z, et al. Spatial heterogeneity analysis for influencing factors of outbound ridership of subway stations considering the optimal scale range of “7D” built environments. Sustainability. 2022;14(23):16314. DOI: 10.3390/su142316314.

Wang Z, et al. Multi-scale geographically weighted elasticity regression model to explore the elastic effects of the built environment on ride-hailing ridership. Sustainability. 2023;15(6):4966. DOI: 10.3390/su15064966.

Kuby M, Barranda A, Upchurch C. Factors influencing light-rail station boardings in the United States. Transportation Research Part A : Policy and Practice. 2004;38(3):223-247. DOI: 10.1016/j.tra.2003.10.006.

Cervero R, Kockelman K. Travel demand and the 3Ds: Density, diversity, and design. Transportation research part D: Transport and environment. 1997;2(3):199-219. DOI: 10.1016/S1361-9209(97)00009-6.

Cervero R, et al. Influences of built environments on walking and cycling: Lessons from Bogotá. International Journal of Sustainable Transportation. 2009;3:203-226. DOI: 10.1080/15568310802178314.

Gan Z, et al. Examining the relationship between built environment and metro ridership at station-to-station level. Transportation Research Part D: Transport and Environment. 2020;82:102332. DOI: 10.1016/j.trd.2020.102332.

Liu C, et al. How to increase rail ridership in Maryland: Direct ridership models for policy guidance. Journal of Urban Planning and Development. 2016;142(4):04016017. DOI: 10.1061/(asce)up.1943-5444.0000340.

Chris DG, et al. How does the built environment affect transit use by train, tram and bus? Journal of Transport and Land Use. 2020;13(1):625-650. DOI: 10.5198/jtlu.2020.1739.

Zhao J, Deng W. Relationship of walk access distance to rapid rail transit stations with personal characteristics and station context. Journal of Urban Planning and Development. 2013;139:311-321. DOI: 10.1061/(asce)up.1943-5444.0000155.

Chalermpong S, Wibowo SS Transit station access trips and factors affecting propensity to walk to transit stations in Bangkok, Thailand. In Proceedings of the Proceedings of the Eastern Asia Society for Transportation Studies Vol. 6 (The 7th International Conference of Eastern Asia Society for Transportation Studies, 2007). 2007; pp. 232-232.DOI: 10.11175/eastpro.2007.0.232.0.

Wang Z, Chen F, Xu T. Interchange between metro and other modes: Access distance and catchment area. Journal of Urban Planning and Development. 2016;142(4):04016012. DOI: 10.1061/(asce)up.1943-5444.0000330.

Kim D, Elek P, Mirjana R. Mapping urbanities: Morphologies, flows, possibilities; Routledge; 2017.

Yu L, Cong Y, Chen K. Determination of the peak hour ridership of metro stations in Xi’an, China using geographically-weighted regression. Sustainability. 2020;12(6):2255. DOI: 10.3390/su12062255.

Wang Z, Wang S, Lian H. A route-planning method for long-distance commuter express bus service based on OD estimation from mobile phone location data: The case of the Changping Corridor in Beijing. Public Transport. 2020;13:101-125. DOI: 10.1007/s12469-020-00254-w.

Ren H, Wang Z, Chen Y. Optimal express bus routes design with limited-stop services for long-distance commuters. Sustainability. 2020;12(4):1669. DOI: 10.3390/su12041669.

Li L, et al. Spatial distribution of travel activities and its relationship with points of interest. Promet-Traffic&Transportation. 2021;33:1-16. DOI: 10.7307/ptt.v33i1.3462.

Mansour S, et al. Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression (MGWR). Sustain Cities Soc. 2021;65:102627. DOI: 10.1016/j.scs.2020.102627.

Moran PA. Notes on continuous stochastic phenomena. Biometrika. 1950;37:17-23. DOI:10.2307/2332142.

Castro MCD, Singer BHJGA. Controlling the false discovery rate: A New Application to Account for Multiple and dependent tests in local statistics of spatial association. Geographical Analysis. 2006;38:180-208. DOI: 10.1111/j.0016-7363.2006.00682.x.

Sung H, Oh J-T. Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea. Cities. 2011;28:70-82. DOI: 10.1016/j.cities.2010.09.004.

Wang Z, et al. Built environment renewal strategies aimed at improving metro station vitality via the interpretable machine learning method: A case study of Beijing. Sustainability. 2024;16(3):1178. DOI: 10.3390/su16031178.

Yao E, et al. Forecasting passenger flow distribution on holidays for urban rail transit based on destination choice behavior analysis. Journal of Advanced Transportation. 2021;2021:922660. DOI: 10.1155/2021/9922660.

Xie B, et al. Travel characteristics analysis and passenger flow prediction of intercity shuttles in the Pearl River delta on holidays. Sustainability. 2020;12(18):7249. DOI: 10.3390/su12187249.

Cai C, et al. Holiday destination choice behavior analysis based on AFC data of urban rail transit. Discrete Dynamics in Nature and Society. 2015;1:136010. DOI: 10.1155/2015/136010.

Wang Z et al. Contribution of built environment factors and their interactions with subway station ridership. Public Transport. 2024;16:929–965.DOI: 10.1007/s12469-024-00353-y.

Zhou G, Tang J. Forecast of urban rail transit passenger flow in holidays based on support vector machine model. In Proceedings of the 5th International Conference on Electromechanical Control Technology and Transportation (ICECTT), IEEE. 2020; pp. 585-589.

Li J, et al. Risk-based crowd massing early warning approach for public places: A case study in China. Safety Science. 2016;89:114-128. DOI: 10.1016/j.ssci.2016.06.007.

Batarce M, Muñoz JC, Torres I. Characterizing the public transport service level experienced by users: An application to six Latin American transit systems. Journal of Public Transportation. 2022;24:100006. DOI: 10.1016/j.jpubtr.2022.100006.

Zacharias J, Zhang T, Nakajima NJUDI. Tokyo Station City: The railway station as urban place. Urban Design International.2011;16:242-251. DOI: 10.1057/udi.2011.15.

Zhao J, et al. Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models. Transportation. 2013;41:133-155, DOI: 10.1007/s11116-013-9492-3.

Li S, et al. The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou. Cities. 2020;99:102580. DOI: 10.1016/j.cities.2019.102580.

Yang H, et al. Direct modeling of subway ridership at the station level: A study based on mixed geographically weighted regression. Canadian Journal of Civil Engineering. 2020;47:534-545. DOI: 10.1139/cjce-2018-0727.

Rosenbloom S, Clifton KJ. The puzzle of income, race, and density: Preliminary evidence on transit use from the 1991 American housing survey. Journal of public transportation. 1996;1:87-102. DOI: 10.5038/2375-0901.1.1.5.

Liu M, et al. Nonlinear effects of built environment features on metro ridership: An integrated exploration with machine learning considering spatial heterogeneity. Sustainable Cities and Society.2023;95:104613. DOI: 10.1016/j.scs.2023.104613.

Du Q, et al. Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership. Journal of Transport Geography. 2022;102:103380. DOI: 10.1016/j.jtrangeo.2022.103380.

Ding C, Cao X, Liu C. How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds. Journal of Transport Geography. 2019;77:70-78. DOI: 10.1016/j.jtrangeo.2019.04.011.

Cheng L, et al. Applying a random forest method approach to model travel mode choice behavior. Travel behaviour and society. 2019;14:1-10. DOI: 10.1016/j.tbs.2018.09.002.

Zhao X, et al. Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel Behaviour and Society. 2020;20:22-35. DOI: 10.1016/j.tbs.2020.02.003.

Hagenauer J, Helbich M. A comparative study of machine learning classifiers for modeling travel mode choice. Expert Systems with Applications. 2017;78:273-282,.DOI: 10.1016/j.eswa.2017.01.057.

Zhou S, et al. Impacts of building configurations on urban stormwater management at a block scale using XGBoost. Sustainable Cities and Society. 2022;87:104235. DOI: 10.1016/j.scs.2022.104235.

Song Y, et al. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data. GIScience & Remote Sensing. 2020;57:593-610. DOI: 10.1080/15481603.2020.1760434.

Ke G, et al. Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of 31st Conference on Neural Information Processing Systems (NIPS 2017), Dec. 2017, Long Beach, CA, USA. 2017. p. 3149-3157.