Public transport vs. private vehicles: Spatial heterogeneity of accessibility to urban centers

Abstract

Urban transportation systems critically influence energy consumption and carbon emissions. To evaluate urban accessibility, it is essential to systematically compare the spatiotemporal efficiency of public transport and private vehicles. However, traditional methods are limited by data acquisition and computation costs. This study proposes a novel, cost-effective framework using open-source map data and Python-based routing tools to compare the spatiotemporal efficiency of public transport and private vehicles. Utilizing real-time traffic data, we establish automated processing workflows to analyze spatial heterogeneity in accessibility across central urban areas of Nanjing, with comparisons to Shanghai and Hangzhou. Key findings include: (1) Taking Nanjing as a case study, the research evaluates public transport accessibility in eastern China's riverfront economic zone using key indicators like travel time ratios, advantage areas, and walking transfer time. Spatial differentiation maps were used to clearly delineate underdeveloped public transport areas in Nanjing; (2) Despite differences in urban form and structure, all three cities exhibit similar characteristics: comparable travel time ratios, concentration of public transport advantages within 10 km of the city center, and a notably high share of walking transfer time; (3) The framework provides a scalable tool for analyzing spatial accessibility heterogeneity, supporting evidence-based public transport policy development.

Keywords

Public transport accessibility / OD travel routes / Sustainable public transport / Urban central area / Web-based map navigation

References

[1] Acheampong, R.A. , Asabere, S.B. , 2022. Urban expansion and differential accessibility by car and public transport in the Greater Kumasi city-region, Ghana—a geospatial modelling approach. J.Transp. Geogr. 98, 103257.

[2] Alexander, C. , Ishikawa, S. , Silverstein, M. , 1977. A Pattern Language: Towns, Buildings, Construction. Oxford University Press.

[3] Alsger, A. , Assemi, B. , Mesbah, M. , Ferreira, L. , 2016. Validating and improving public transport origin—destination estimation algorithm using smart card fare data. Transport. Res. C Emerg.Technol. 68, 490- 506.

[4] Arbex, R. , Cunha, C.B. , 2020. Estimating the influence of crowding and travel time variability on accessibility to jobs in a large public transport network using smart card big data. J. Transp.Geogr. 85, 102671.

[5] Basso, F. , Frez, J. , Martínez, L. , Pezoa, R. , Varas, M. , 2020. Accessibility to opportunities based on public transport gpsmonitored data: the case of Santiago, Chile. Travel Behav. Society 21, 140- 153.

[6] Beirão, G. , Sarsfield Cabral, J.A. , 2007. Understanding attitudes towards public transport and private car: a qualitative study. Transp. Policy 14 (6), 478- 489.

[7] Benenson, I. , Ben-Elia, E. , Rofe, Y. , Rosental, A. , 2017. Estimation of urban transport accessibility at the spatial resolution of an individual traveler. In: (Vonu) Thakuriah, P., Tilahun, N., Zellner, M. (Eds.), Seeing Cities Through Big Data: Research, Methods and Applications in Urban Informatics. Springer International Publishing, pp. 383-404.

[8] Benenson, I. , Martens, K. , Rofé Y. , Kwartler, A. , 2011. Public transport versus private car GIS-based estimation of accessibility applied to the Tel Aviv metropolitan area. Ann. Reg. Sci. 47 (3), 499- 515.

[9] Boeing, G. , 2017. OSMnx: new methods for acquiring, constructing, analyzing, and visualizing complex street networks. Comput.Environ. Urban Syst. 65, 126- 139.

[10] Boisjoly, G. , El-Geneidy, A.M. , 2017. How to get there? a critical assessment of accessibility objectives and indicators in metropolitan transportation plans. Transp. Policy 55, 38- 50.

[11] Boscoe, F.P. , Henry, K.A. , Zdeb, M.S. , 2012. A nationwide comparison of driving distance versus straight-line distance to hospitals. Prof. Geogr. 64 (2), 188- 196.

[12] Brielmann, A.A. , Buras, N.H. , Salingaros, N.A. , Taylor, R.P. , 2022. What happens in your brain when you walk down the street? Implications of architectural proportions, biophilia, and fractal geometry for urban science. Urban Sci. 6 (1), Article 1.

[13] Büchel, B. , Corman, F. , 2020. Review on statistical modeling of travel time variability for road-based public transport. Front.Built Environ. 6, 70.

[14] Chen, J. , Ni, J. , Xi, C. , Li, S. , Wang, J. , 2017. Determining intraurban spatial accessibility disparities in multimodal public transport networks. J. Transp. Geogr. 65, 123- 133.

[15] Chen, S. , Claramunt, C. , Ray, C. , 2014. A spatio-temporal modelling approach for the study of the connectivity and accessibility of the Guangzhou metropolitan network. J. Transp. Geogr. 36, 12- 23.

[16] Cheng, S. , Xie, B. , Bie, Y. , Zhang, Y. , Zhang, S. , 2018. Measure dynamic individual spatial-temporal accessibility by public transit: integrating time-table and passenger departure time. J.Transp. Geogr. 66, 235- 247.

[17] Christodoulou, A. , Christidis, P. , 2021. Evaluating congestion in urban areas: the case of Seville. Res. Transport. Bus. Manag. 39, 100577.

[18] Cui, M. , Levinson, D. , 2018. Full cost accessibility. J. Transp. Land Use 11 (1), Article 1.

[19] Currie, G. , 2004. Gap analysis of public transport needs: measuring spatial distribution of public transport needs and identifying gaps in the quality of public transport provision. Transp. Res.Rec.: J. Transport. Res. Board 1895 (1), 137- 146.

[20] Ding, J. , Zhang, Y. , Li, L. , 2018. Accessibility measure of bus transit networks. IET Intell. Transp. Syst. 12 (7), 682- 688.

[21] Ding, X. , Hong, C. , Wu, J. , Zhao, L. , Shi, G. , Liu, Z. , Hong, H. , Zhao, Z. , 2023. Research on time-based fare discount strategy for urban rail transit peak congestion. Urban Rail Transit 9 (4), 352- 367.

[22] Djurhuus, S. , Sten Hansen, H. , Aadahl, M. , Glümer, C. , 2016. Building a multimodal network and determining individual accessibility by public transportation. Environ. Plann. Plann.Des. 43 (1), 210- 227.

[23] Farber, S. , Fu, L. , 2017. Dynamic public transit accessibility using travel time cubes: comparing the effects of infrastructure (dis)investments over time. Comput. Environ. Urban Syst. 62, 30- 40.

[24] Frank, L. , Bradley, M. , Kavage, S. , Chapman, J. , Lawton, T.K. , 2008. Urban form, travel time, and cost relationships with tour complexity and mode choice. Transportation 35 (1), 37- 54.

[25] Friman, M. , Larhult, L. , Gärling, T. , 2013. An analysis of soft transport policy measures implemented in Sweden to reduce private car use. Transportation 40 (1), 109- 129.

[26] Gardner, B. , Abraham, C. , 2010. Going green? Modeling the impact of environmental concerns and perceptions of transportation alternatives on decisions to drive. J. Appl. Soc. Psychol. 40 (4), 831- 849.

[27] Geurs, K.T. , Wee, B. , 2004. Accessibility evaluation of land-use and transport strategies: review and research directions. J. Transp.Geogr. 12 (2), 127- 140.

[28] Goliszek, S. , 2021. GIS tools and programming languages for creating models of public and private transport potential accessibility in Szczecin, Poland. J. Geogr. Syst. 23 (1), 115- 137.

[29] Goliszek, S. , 2022. Private car transport or public transport? The study of daily accessibility in Szczecin. Geogr. Casopis 74 (4), 337- 356.

[30] Hansen, W.G. , 1959. How accessibility shapes land use. J. Am. Inst.Plan. 25 (2), 73- 76.

[31] Huang, J. , Xiong, M. , Wang, J. , 2022. Route choice and parallel routes in subway networks: a comparative analysis of Beijing and Shanghai. Tunn. Undergr. Space Technol. 128, 104635.

[32] Ibrahim, M.F. , 2003. Car ownership and attitudes towards transport modes for shopping purposes in Singapore. Transportation 30 (4), 435- 457.

[33] Kapatsila, B. , Palacios, M.S. , Grisé E. , El-Geneidy, A. , 2023. Resolving the accessibility dilemma: comparing cumulative and gravity-based measures of accessibility in eight Canadian cities. J. Transp. Geogr. 107, 103530.

[34] Langford, M. , Higgs, G. , Fry, R. , 2016. Multi-modal two-step floating catchment area analysis of primary health care accessibility. Health Place 38, 70- 81.

[35] Lättman, K. , Friman, M. , Olsson, L.E. , 2020. Restricted car-use and perceived accessibility. Transport. Res. Transp. Environ. 78, 102213.

[36] Lee, J.-H. , Cho, S.-H. , Kim, D.-K. , Lee, C. , 2016. Valuation of travel time reliability accommodating heterogeneity of route choice behaviors. Transp. Res. Rec. 2565 (1), 86- 93.

[37] Lee, K. , Chae, Y. , 2021. Analysis of socioeconomic disparities and accessibility to cooling centers using travel time of 100 m×100 m grids in South Korea. Urban Clim. 36, 100762.

[38] Loo, B.P.Y. , Huang, Z. , 2022. Spatio-temporal variations of traffic congestion under work from home (WFH) arrangements: lessons learned from COVID-19. Cities 124, 103610.

[39] Lucas, K. , Wee, B. , Maat, K. , 2016. A method to evaluate equitable accessibility: combining ethical theories and accessibility-based approaches. Transportation 43 (3), 473- 490.

[40] Massobrio, R. , Nesmachnow, S. , 2020. Urban mobility data analysis for public transportation systems: a case study in Montevideo, Uruguay. 10 (16). Article 16. Appl. Sci. 10 (16), Article 16.

[41] Massobrio, R. , Nesmachnow, S. , 2022. Travel time estimation in public transportation using bus location data. In: Smart Cities(ICSCeCities 2021). Springer, pp. 192-206.

[42] Mazloumi, E. , Currie, G. , Rose, G. , 2010. Using GPS data to gain insight into public transport travel time variability. J. Transport.Eng. 136 (7), 623- 631.

[43] Mehaffy, M. , Porta, S. , Rofè Y. , Salingaros, N. , 2010. Urban nuclei and the geometry of streets: the 'emergent neighborhoods' model. Urban Des. Int. 15 (1), 22- 46.

[44] Mehaffy, M.W. , Porta, S. , Romice, O. , 2015. The "neighborhood unit" on trial: a case study in the impacts of urban morphology. J. Urbanism: Int. Res. Placemaking Urban Sustain. 8 (2), 199- 217.

[45] Miller, H.J. , 2017. Time geography and space-time prism. In: International Encyclopedia of Geography: People, the Earth, Environment and Technology. Wiley.

[46] Mocanu, T. , Joshi, J. , Winkler, C. , 2021. A data-driven analysis of the potential of public transport for German commuters using accessibility indicators. Eur. Transp. Res. Rev. 13 (1), 54.

[47] Morris, J.M. , Dumble, P.L. , Wigan, M.R. , 1979. Accessibility indicators for transport planning. Transport. Res. Gen. 13 (2), 91- 109.

[48] Nesmachnow, S. , Massobrio, R. , Guridi, S. , Olmedo, S. , Tchernykh, A. , 2023. Big data analysis for travel time characterization in public transportation systems. Sustainability. 15 (19), Article 19.

[49] Páez, A. , Scott, D.M. , Morency, C. , 2012. Measuring accessibility: positive and normative implementations of various accessibility indicators. J. Transp. Geogr. 25, 141- 153.

[50] Pelletier, M.-P. , Trépanier, M. , Morency, C. , 2011. Smart card data use in public transit: a literature review. Transport. Res. C Emerg. Technol. 19 (4), 557- 568.

[51] Perera, S. , Massobrio, R. , 2023. Walking accessibility to the public transport network in Montevideo, Uruguay. In: Nesmachnow, S., Hernández Callejo, L. (Eds.), Smart Cities. Springer, Nature Switzerland, pp. 17-30.

[52] Qi, Z. , Lim, S. , Rashidi, T. , 2020. Assessment of transport equity to Central Business District (CBD) in Sydney, Australia. Transport.Lett. 12 (4), 246- 256.

[53] Ruiz-Pérez, M. , Seguí-Pons, J.M. , Salleras-Mestre, X. , 2023. Bibliometric analysis of equity in transportation. Heliyon 9 (8), e19089.

[54] Ryan, J. , Pereira, R.H.M. , 2021. What are we missing when we measure accessibility? Comparing calculated and self-reported accounts among older people. J. Transp. Geogr. 93, 103086.

[55] Salonen, M. , Toivonen, T. , 2013. Modelling travel time in urban networks: comparable measures for private car and public transport. J. Transp. Geogr. 31, 143- 153.

[56] Sarkar, P.K. , Jain, A.K. , 2017. Defining and assessing congestion inside metro trains and at station: case study of Delhi metro, India. Int. J. Traffic Transp. Eng. 7 (1), 93- 107.

[57] Shi, F. , 2021. Research on accessibility and equity of urban transport based on multisource big data. J. Adv. Transp. 2021, 1103331.

[58] Su, S. , Li, Z. , Xu, M. , Cai, Z. , Weng, M. , 2017. A geo-big data approach to intra-urban food deserts: transit-varying accessibility, social inequalities, and implications for urban planning. Habitat Int. 64, 22- 40.

[59] Sun, C. , Quan, W. , 2022. A novel model for accessibility of bus station and its application to bottlenecks identification: a case study in Harbin. Complexity 2022, 1816778.

[60] Tahmasbi, B. , Haghshenas, H. , 2019. Public transport accessibility measure based on weighted door to door travel time. Comput.Environ. Urban Syst. 76, 163- 177.

[61] Tao, Z. , Wang, Q. , 2022. Facility or transport inequality? Decomposing healthcare accessibility inequality in Shenzhen, China. Int. J. Environ. Res. Publ. Health 19 (11), Article 11.

[62] Tian, P.-N. , Mao, B.-H. , Tong, R.-Y. , Zhang, H.-X. , Zhou, Q. , 2023. Analysis of carbon emission level and intensity of China's transportation industry and different transportation modes. Clim. Change Res. 19 (3), 347- 356.

[63] Wang, W. , Attanucci, J.P. , Wilson, N.H.M. , 2011. Bus passenger origin-destination estimation and related analyses using automated data collection systems. J. Public Transport. 14 (4), 131- 150.

[64] Welch, T.F. , Widita, A. , 2019. Big data in public transportation: a review of sources and methods. Transp. Rev. 39 (6), 795- 818.

[65] Wemegah, T.D. , Zhu, S. , Atombo, C. , 2018. Modeling the effect of days and road type on peak period travels using structural equation modeling and big data from radio frequency identification for private cars and taxis. Eur. Transp. Res. Rev. 10 (2), 39.

[66] Yang, L. , Chau, K.W. , Szeto, W.Y. , Cui, X. , Wang, X. , 2020. Accessibility to transit, by transit, and property prices: spatially varying relationships. Transport. Res. Transp. Environ. 85, 102387.

[67] Yang, W. , Chen, B.Y. , Cao, X. , Li, T. , Li, P. , 2017. The spatial characteristics and influencing factors of modal accessibility gaps: a case study for Guangzhou, China. J. Transp. Geogr. 60, 21- 32.

[68] Zhang, T. , Zhang, W. , He, Z. , 2021. Measuring positive public transit accessibility using big transit data. Geo-Spatial Inf. Sci. 24 (4), 722- 741.

[69] Zheng, Z. , Xia, H. , Ambinakudige, S. , Qin, Y. , Li, Y. , Xie, Z. , Zhang, L. , Gu, H. , 2019. Spatial accessibility to hospitals based on web mapping API: an empirical study in Kaifeng, China. Sustainability 11 (4), Article 4.

[70] Zhou, Y. , Li, R. , Pan, L. , Chen, Y. , 2020. Public transit accessibility calculation and evaluation of the Beijing South railway station. J. Beijing Univ. Technol. 46 (12), 1365- 1376.

[71] Zuo, Y. , Liu, Z. , Fu, X. , 2020. Measuring accessibility of bus system based on multi-source traffic data. Geo-Spatial Inf. Sci. 23 (3), 248- 257.

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