The effects of high-speed rail, bullet trains, and commuter rail on commuting flows across administrative boundaries: Implications for integrated transportation planning in China

Daming Lu

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences

Wenjia Zhang

Tongji University

Mei-Po Kwan

The Chinese University of Hong Kong

DOI: https://doi.org/10.5198/jtlu.2026.2855

Keywords: Intercity rail, Land-use contexts, Commuting across boundaries, Cross-classified multilevel model, China Greater Bay Area


Abstract

While the development of high-speed rail (HSR), bullet trains, and commuter rail has played an important role in shaping coordinated megaregional development, few empirical studies have compared the effects of different types of rail transport infrastructure on the number of commuters across administrative boundaries. Using big data from multiple sources, this study employs a cross-classified multilevel model to estimate three planning strategies that affect commuting across administrative boundaries at the sub-district/community scale in the Pearl River Delta (PRD) region, including (1) the facilitation of rail infrastructure, (2) the impedance of administrative boundaries, and (3) land-use settings in both residential and workplace destinations. The results suggest that HSR and commuter rail connectivity are positively associated with commuting across administrative boundaries, with a larger estimated coefficient for HSR, whereas bullet train connectivity is not significant. City and district boundaries are negatively associated with commuting across administrative boundaries and weaken its association with intercity rail connectivity. Positive interaction effects are also found between HSR connectivity and residential density in home communities and job density in work communities. These findings can help policymakers determine the priority of various railway infrastructures in the PRD region, also suggesting their incorporation into regional governance and land-use strategies.


References

Azaria, D. E., Troy, A., Lee, B. H. Y., Ventriss, C., & Voigt, B. (2013). Modeling the effects of an urban growth boundary on vehicle travel in a small metropolitan area. Environment and Planning B: Planning and Design, 40(5), 846–864. https://doi.org/10.1068/b38182

Bai, X., Zhai, W., Steiner, R. L., & He, Z. (2020). Exploring extreme commuting and its relationship to land use and socioeconomics in the central Puget Sound. Transportation Research Part D: Transport and Environment, 88, 102574. https://doi.org/10.1016/j.trd.2020.102574

Bay Area Council Economic Institute. (2021). New transbay rail crossing: Making the case for a key megaregional connection. Bay Area Council Economic Institute.

Beike Research Institute. (2020). Urban agglomeration observation series: New insights into cross-city employment and housing in the Guangdong-Hong Kong-Macao Greater Bay Area (in Chinese). Beike Research Institute.

Bergantino, A. S., & Madio, L. (2019). Intra- and inter-regional commuting: Assessing the role of wage differentials. Papers in Regional Science, 98(2), 1085–1114. https://doi.org/10.1111/pirs.12394

Cavallaro, F., & Dianin, A. (2019). Cross-border commuting in Central Europe: Features, trends and policies. Transport Policy, 78, 86–104. https://doi.org/10.1016/j.trapol.2019.04.008

Cavallaro, F., & Dianin, A. (2020). Efficiency of public transport for cross-border commuting: An accessibility-based analysis in Central Europe. Journal of Transport Geography, 89, 102876. https://doi.org/10.1016/j.jtrangeo.2020.102876

Chen, Z., & Haynes, K. E. (2015). Regional impact of public transportation infrastructure: A spatial panel assessment of the U.S. Northeast megaregion. Economic Development Quarterly, 29(3), 275–291. https://doi.org/10.1177/0891242415584436

Chinese State Council. (2004). Medium- and long-term railroad network plan (in Chinese). Chinese State Council.

Chung, H., Yang, Y., Chen, C. L., & Vickerman, R. (2020). Exploring the association of the built environment, accessibility and commuting frequency with the travel times of high-speed rail commuters: Evidence from China. Built Environment, 46(3), 342–361. https://doi.org/10.2148/benv.46.3.342

Claus, A. M., Arend, M. G., Burk, C. L., Kiefer, C., & Wiese, B. S. (2020). Cross-classified models in I/O psychology. Journal of Vocational Behavior, 120, 103447. https://doi.org/10.1016/j.jvb.2020.103447

Comerio, N., Minelli, E., Sottrici, F., & Torchia, E. (2021). Cross-border commuters: How to segment them? A framework of analysis. International Migration, 59(1), 74–88. https://doi.org/10.1111/imig.12720

Cui, C., Wu, X., Liu, L., & Zhang, W. (2021). The spatial-temporal dynamics of daily intercity mobility in the Yangtze River Delta: An analysis using big data. Habitat International, 106, 102174. https://doi.org/10.1016/j.habitatint.2020.102174

Cummings, C., & Mahmassani, H. (2022). Does intercity rail station placement matter? Expansion of the node-place model to identify station location impacts on Amtrak ridership. Journal of Transport Geography, 99, 103278. https://doi.org/10.1016/j.jtrangeo.2022.103278

de Abreu e Silva, J., Morency, C., & Goulias, K. G. (2012). Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal. Transportation Research Part A: Policy and Practice, 46(8), 1252–1264. https://doi.org/10.1016/j.tra.2012.05.003

Deng, T., & Nelson, J. D. (2011). Recent developments in bus rapid transit: A review of the literature. Transport Reviews, 31(1), 69–96. https://doi.org/10.1080/01441647.2010.492455

Diao, M., Zhu, Y., & Zhu, J. (2017). Intra-city access to inter-city transport nodes: The implications of high-speed-rail station locations for the urban development of Chinese cities. Urban Studies, 54(10), 2249–2267. https://doi.org/10.1177/0042098016646686

Ding, C., & Cao, X. (2019). How does the built environment at residential and work locations affect car ownership? An application of cross-classified multilevel model. Journal of Transport Geography, 75, 37–45. https://doi.org/10.1016/j.jtrangeo.2019.01.012

Faludi, A., & Waterhout, B. (2002). The making of the European spatial development perspective: No masterplan. Routledge. https://doi.org/10.4324/9780203378878

Ganning, J. (2018). The effects of commuter rail establishment on commuting and deconcentration. Regional Studies, 52(12), 1636–1645. https://doi.org/10.1080/00343404.2018.1437898

Garmendia, M., Ureña, J. M., & Coronado, J. M. (2011). Long-distance trips in a sparsely populated region: The impact of high-speed infrastructures. Journal of Transport Geography, 19(4), 537–551. https://doi.org/10.1016/j.jtrangeo.2010.06.002

Ghimire, R., & Lancelin, C. (2019). The relationship between financial incentives provided by employers and commuters’ decision to use transit: Results from the Atlanta Regional Household Travel Survey. Transport Policy, 74, 103–113. https://doi.org/10.1016/j.tranpol.2018.11.005

Guan, X., & Wang, D. (2019). Influences of the built environment on travel: A household-based perspective. Transportation Research Part A: Policy and Practice, 130, 710–724. https://doi.org/10.1016/j.tra.2019.10.003

Guirao, B., Campa, J. L., & Casado-Sanz, N. (2018). Labour mobility between cities and metropolitan integration: The role of high speed rail commuting in Spain. Cities, 78, 140–154. https://doi.org/10.1016/j.cities.2018.02.008

Guirao, B., Lara-Galera, A., & Campa, J. L. (2017). High speed rail commuting impacts on labour migration: The case of the concentration of metropolis in the Madrid functional area. Land Use Policy, 66, 131–140. https://doi.org/10.1016/j.landusepol.2017.04.035

Heuermann, D. F., & Schmieder, J. F. (2019). The effect of infrastructure on worker mobility: Evidence from high-speed rail expansion in Germany. Journal of Economic Geography, 19(2), 335–372. https://doi.org/10.1093/jeg/lby019

Ingvardson, J. B., & Nielsen, O. A. (2018). Effects of new bus and rail rapid transit systems – an international review. Transport Reviews, 38(1), 96–116. https://doi.org/10.1080/01441647.2017.1301594

Kanaroglou, P. S., Higgins, C. D., & Chowdhury, T. A. (2015). Excess commuting: A critical review and comparative analysis of concepts, indices, and policy implications. Journal of Transport Geography, 44, 13–23. https://doi.org/10.1016/j.jtrangeo.2015.02.009

Karácsony, P., Vinichenko, M. V., Antalík, I., Dávid, L. D., & Vasa, L. (2021). Analysis of cross-border commuters’ spatial mobility between western regions of Hungary and Slovakia. Geographia Technica, 16(1), 128–140. https://doi.org/10.21163/GT_2021.161.11

Kwan, M.-P. (2012). The uncertain geographic context problem. Annals of the Association of American Geographers, 102(5), 958–968. https://doi.org/10.1080/00045608.2012.687349

Lee, B., Gordon, P., Moore, J. E., & Richardson, H. W. (2011). The attributes of residence/workplace areas and transit commuting. Journal of Transport and Land Use, 4(3), 43–63. https://doi.org/10.5198/jtlu.v4i3.310

Limtanakool, N., Dijst, M., & Schwanen, T. (2006). The influence of socioeconomic characteristics, land use and travel time considerations on mode choice for medium- and longer-distance trips. Journal of Transport Geography, 14(5), 327–341. https://doi.org/10.1016/j.jtrangeo.2005.06.004

Lin, X., Yang, J., & MacLachlan, I. (2018). High-speed rail as a solution to metropolitan passenger mobility: The case of Shenzhen-Dongguan-Huizhou metropolitan area. Journal of Transport and Land Use, 11(1), 1257–1270. https://doi.org/10.5198/jtlu.2018.1297

Liu, S., Yao, E., & Li, B. (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

Ma, K. R., & Banister, D. (2006). Excess commuting: A critical review. Transport Reviews, 26(6), 749–767. https://doi.org/10.1080/01441640600782609

Maoh, H., & Tang, Z. (2012). Determinants of normal and extreme commute distance in a sprawled midsize Canadian city: Evidence from Windsor, Canada. Journal of Transport Geography, 25, 50–57. https://doi.org/10.1016/j.jtrangeo.2012.07.003

Medeiros, E., Ferreira, R., Boijmans, P., Verschelde, N., Spisiak, R., Skoniezki, P., Dietachmair, J., Hurnaus, K., Ebster, M., Madsen, S., Ballaguy, R.-L., Volponi, E., Isinger, E., Voiry, P., Markl-Hummel, L., Harster, P., Sippel, L., Nolte, J., Maarfield, S., … Berzi, M. (2021). Boosting cross-border regions through better cross-border transport services. The European case. Case Studies on Transport Policy, 9(1), 291–301. https://doi.org/10.1016/j.cstp.2021.01.006

Mitra, S. K., & Saphores, J.-D. M. (2019). Why do they live so far from work? Determinants of long-distance commuting in California. Journal of Transport Geography, 80, 102489. https://doi.org/10.1016/j.jtrangeo.2019.102489

National Development and Reform Commission. (2020). Guangdong-Hong Kong-Macao Greater Bay Area intercity railway construction plan (in Chinese). National Development and Reform Commission.

New York City Department of City Planning. (2019). The ins and outs of NYC com-muting: An examination of recent trends and characteristics of commuter exchanges between NYC and the surrounding metro region. New York City Department of City Planning.

Niebuhr, A., Granato, N., Haas, A., & Hamann, S. (2012). Does labour mobility reduce disparities between regional labour markets in Germany? Regional Studies, 46(7), 841–858. https://doi.org/10.1080/00343404.2010.532118

Rahman, M., & Akther, S. (2022). Intercity commuting in metropolitan regions: A mode choice analysis of commuters traveling to Dhaka from nearby cities. Journal of Urban Planning and Development, 148(1), 05021060. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000777

Ross, C. L., & Woo, M. (2011). Megaregions and mobility. The Bridge, 41(1), 24–34.

Snijders, T. A. B., & Bosker, R. J. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). SAGE.

Tang, J., Mokhtarian, P. L., & Zhen, F. (2020). How do passengers allocate and evaluate their travel time? Evidence from a survey on the Shanghai–Nanjing high speed rail corridor, China. Journal of Transport Geography, 85, 102701. https://doi.org/10.1016/j.jtrangeo.2020.102701

Wang, F., Wei, X., Liu, J., He, L., & Gao, M. (2019). Impact of high-speed rail on population mobility and urbanisation: A case study on Yangtze River Delta urban agglomeration, China. Transportation Research Part A: Policy and Practice, 127, 99–114. https://doi.org/10.1016/j.tra.2019.06.018

Wang, L., Zhang, S., Sun, W., & Chen, C. L. (2020). Exploring the physical and mental health of high-speed rail commuters: Suzhou-Shanghai inter-city commuting. Journal of Transport & Health, 18, 100902. https://doi.org/10.1016/j.jth.2020.100902

Zhang, W., Fang, C., Zhou, L., & Zhu, J. (2020). Measuring megaregional structure in the Pearl River Delta by mobile phone signaling data: A complex network approach. Cities, 104, 102809. https://doi.org/10.1016/j.cities.2020.102809

Zhang, W., Lu, D., Liu, H., & Li, B. (2024). Varying built environment contexts and trip chain decisions: A multinomial-choice gradient boosting decision trees analysis. Travel Behaviour and Society, 34, 100684. https://doi.org/10.1016/j.tbs.2023.100684

Zhang, W., Lu, D., Zhao, Y., Luo, X., & Yin, J. (2022). Incorporating polycentric development and neighborhood life-circle planning for reducing driving in Beijing: Nonlinear and threshold analysis. Cities, 121, 103488. https://doi.org/10.1016/j.cities.2021.103488