Volume 26 Issue 2
Feb.  2026
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CHEN En-hui, JI Ke-yu, CHENG Long, ZHANG Wen-bo, TENG Jing. Method for identifying low-accessibility areas and their contributing factors in public transit accessibility to integrated passenger transportation hubs[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 44-60. doi: 10.19818/j.cnki.1671-1637.2026.142
Citation: CHEN En-hui, JI Ke-yu, CHENG Long, ZHANG Wen-bo, TENG Jing. Method for identifying low-accessibility areas and their contributing factors in public transit accessibility to integrated passenger transportation hubs[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 44-60. doi: 10.19818/j.cnki.1671-1637.2026.142
shu

Method for identifying low-accessibility areas and their contributing factors in public transit accessibility to integrated passenger transportation hubs

doi: 10.19818/j.cnki.1671-1637.2026.142
  • 1.

    Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China

  • 2.

    College of Transportation, Tongji University, Shanghai 201804, China

  • 3.

    School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China

Funds:

National Natural Science Foundation of China 52402388

National Natural Science Foundation of China 52432011

Science and Technology Innovation Plan of Shanghai Science and Technology Commission 24692105600

Fundamental Research Funds for the Central Universities 22120250285

More Information
  • Corresponding author: TENG Jing, professor, PhD, E-mail: tengjing@tongji.edu.cn
  • Received Date: 2025-04-28
  • Accepted Date: 2025-11-27
  • Rev Recd Date: 2025-09-20
  • Publish Date: 2026-02-28
    Abstract
  • Regarding the challenge of identifying low-accessibility areas and their contributing factors in public transit accessibility to large-scale integrated passenger transportation hubs, a diagnostic framework was proposed for hub transit services based on accessibility. The spatial coverage and population served by multimodal networks were measured across different accessibility time thresholds. The Lorenz curve model was applied to assess the equity of spatial and population coverage across different hubs. A method was established to identify low-accessibility areas in hub public transit services, examining their spatial extent and distribution patterns. A gradient boosting decision tree model was introduced to analyze how various travel chain components affect hub accessibility from a behavioral perspective. An affinity propagation clustering algorithm targeting low-accessibility areas was designed to categorize distinct types of poorly connected zones. A case study was conducted using multimodal transportation networks at Hongqiao and Pudong hubs in Shanghai. Research results show that although Hongqiao hub generally has better public transit accessibility than Pudong hub, it performs worse in terms of equity, with more pronounced spatial disparities. Low-accessibility areas to Hongqiao exhibit a multi-core, dispersed pattern, while those to Pudong show a linear, clustered distribution. Regarding the contributing factors to low accessibility, walking distance has the greatest relative impact on Hongqiao hub (31%), followed by the road network detour index (29%) and the number of surface bus stops (21%). For Pudong hub, walking distance's influence increases to 37%, followed by the number of bus stops (26%) and the public transit network detour index (18%). Based on these key factors, the low-accessibility areas to both hubs were categorized into three types: first-and-last-mile walking-constrained, bus-dependent, and long-distance detouring rail transit.

     

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    • References(53)
  • [1]
    HUANG Shi-yu, YANG Min, WANG Zhe-yuan, et al. Dynamic and static accessibility analysis of high-speed rail hub connecting urban transport network[J]. Transportation Standardization, 2023, 9(3): 57-72.
    [2]
    GAO Wei, OUYANG Yu-xin, ZHAO Mei, et al. A review on accessibility measurements of public service facilities[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2023, 59(2): 344-354.
    [3]
    LIU Ji-ping, CAO Yuan-hui, WANG Yong, et al. Evaluating the accessibility of medical services in the 15 min life circle using Internet pan-map resources: A case study in Shanghai[J]. Geomatics and Information Science of Wuhan University, 2022, 47(12): 2054-2063.
    [4]
    MARWAL A, SILVA E. Literature review of accessibility measures and models used in land use and transportation planning in last 5 years[J]. Journal of Geographical Sciences, 2022, 32(3): 560-584. doi: 10.1007/s11442-022-1961-1
    [5]
    MILLER E J. Accessibility: Measurement and application in transportation planning[J]. Transport Reviews, 2018, 38(5): 551-555. doi: 10.1080/01441647.2018.1492778
    [6]
    TAO T, CAO J. Exploring nonlinear and collective influences of regional and local built environment characteristics on travel distances by mode[J]. Journal of Transport Geography, 2023, 109: 103599. doi: 10.1016/j.jtrangeo.2023.103599
    [7]
    FU X, LAM W H K, CHEN B Y, et al. Maximizing space-time accessibility in multi-modal transit networks: An activity-based approach[J]. Transportmetrica A: Transport Science, 2022, 18(2): 192-220. doi: 10.1080/23249935.2020.1806372
    [8]
    TAO T, WANG J Y, CAO X Y. Exploring the non-linear associations between spatial attributes and walking distance to transit[J]. Journal of Transport Geography, 2020, 82: 102560. doi: 10.1016/j.jtrangeo.2019.102560
    [9]
    LIU R, CHEN Y, WU J P, et al. Mapping spatial accessibility of public transportation network in an urban area—A case study of Shanghai Hongqiao transportation hub[J]. Transportation Research Part D: Transport and Environment, 2018, 59: 478-495. doi: 10.1016/j.trd.2018.01.003
    [10]
    HUANG Jie, SHI Wen-qian, CHEN Yu. Accessibility of Beijing dual hub airports from the perspective of residential travel[J]. Journal of Geo-Information Science, 2022, 24(5): 914-924.
    [11]
    CHENG L, CHEN Y H, HE S Y, et al. Comparing accessibility to high-speed rail stations by public transit and cars: A national-scale analysis[J]. Journal of Transport Geography, 2025, 123: 104105. doi: 10.1016/j.jtrangeo.2024.104105
    [12]
    YU Bo-ning, CHEN Xue-wu. Evaluation of high-speed rail station bus dispersal efficiency based on accessibility analysis[C]// China Highway and Transportation Society, China Institute of Navigation, China Railway Society. Proceedings of the 2024 World Transport Convention (WTC2024)—Traffic Engineering. Beijing: China Communications Press, 2024: 306-311.
    [13]
    ZHENG Shu-jie, CHEN Xue-wu, QI Chao. Research on improving public transport accessibility of high-speed rail hubs in megacities[C]//China Association for Science and Technology, Ministry of Transport, Chinese Academy of Engineering, et al. Proceedings of the 2022 World Transport Convention (WTC2022)—Traffic Engineering and Air Transport. Beijing: China Communications Press, 2022: 1782-1786.
    [14]
    MARTÍN J C, ROMÁN C, GARCÍA-PALOMARES J C, et al. Spatial analysis of the competitiveness of the high-speed train and air transport: The role of access to terminals in the Madrid-Barcelona corridor[J]. Transportation Research Part A: Policy and Practice, 2014, 69: 392-408. doi: 10.1016/j.tra.2014.09.010
    [15]
    MOYANO A, MOYA-GÓMEZ B, GUTIÉRREZ J. Access and egress times to high-speed rail stations: A spatiotemporal accessibility analysis[J]. Journal of Transport Geography, 2018, 73: 84-93. doi: 10.1016/j.jtrangeo.2018.10.010
    [16]
    ASHIK F R, ISLAM M S, ALAM M S, et al. Dynamic equity in urban amenities distribution: An accessibility-driven assessment[J]. Applied Geography, 2024, 164: 103199. doi: 10.1016/j.apgeog.2024.103199
    [17]
    SHARMA G, PATIL G R. Urban spatial structure and equity for urban services through the lens of accessibility[J]. Transport Policy, 2024, 146: 72-90. doi: 10.1016/j.tranpol.2023.10.017
    [18]
    YANG Peng, HUANG Jie, WANG Jiao-e, et al. Measuring accessibility and equity of rural logistics terminal facilities in China[J]. Acta Geographica Sinica, 2024, 79(11): 2739-2753.
    [19]
    ZINIA F A, BHANDARI P, TUFFOUR J P, et al. Evaluating social equity of transit accessibility: A case of Salt Lake County, U.S. [J]. Transportation Research Record: Journal of the Transportation Research Board, 2023, 2677(12): 806-814. doi: 10.1177/03611981231170005
    [20]
    GIANNOTTI M, BARROS J, TOMASIELLO D B, et al. Inequalities in transit accessibility: Contributions from a comparative study between Global South and North Metropolitan regions[J]. Cities, 2021, 109: 103016. doi: 10.1016/j.cities.2020.103016
    [21]
    SHAVER H, KIM J, DIAB E, et al. Bridging the gap: A social equity analysis of intra-city transit access to inter-city rail in Canada[J]. Cities, 2025, 163: 106041. doi: 10.1016/j.cities.2025.106041
    [22]
    ZHAO X L, YAN X, YU A L, et al. Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models[J]. Travel Behaviour and Society, 2020, 20: 22-35. doi: 10.1016/j.tbs.2020.02.003
    [23]
    CHEN E H, YE Z R, WU H. Nonlinear effects of built environment on intermodal transit trips considering spatial heterogeneity[J]. Transportation Research Part D: Transport and Environment, 2021, 90: 102677. doi: 10.1016/j.trd.2020.102677
    [24]
    DING C, CAO X J, NSS P. Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo[J]. Transportation Research Part A: Policy and Practice, 2018, 110: 107-117. doi: 10.1016/j.tra.2018.02.009
    [25]
    DING C, CAO X Y, LIU C. How does the station-area built environment influence metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds[J]. Journal of Transport Geography, 2019, 77: 70-78. doi: 10.1016/j.jtrangeo.2019.04.011
    [26]
    DING C, CAO X Y, WANG Y P. Synergistic effects of the built environment and commuting programs on commute mode choice[J]. Transportation Research Part A: Policy and Practice, 2018, 118: 104-118. doi: 10.1016/j.tra.2018.08.041
    [27]
    MA X W, TIAN X L, CUI H J, et al. What influences intermodal choices: Metro-centric, bus-centric, hybrid? Insights from machine learning approaches[J]. Transportation Research Part D: Transport and Environment, 2024, 136: 104407. doi: 10.1016/j.trd.2024.104407
    [28]
    CHEN Qi-xiang, LYU Bin, LI Xian-lin. Nonlinear effect of station-area built environment on taxi-metro combined travel[J]. Journal of Traffic and Transportation Engineering, 2024, 24(5): 285-300. doi: 10.19818/j.cnki.1671-1637.2024.05.019
    [29]
    LEI J Y, HE M, SHI Z B, et al. How does the built environment affect intermodal transit demand across different spatiotemporal contexts[J]. Journal of Transport Geography, 2024, 121: 104033. doi: 10.1016/j.jtrangeo.2024.104033
    [30]
    DELBOSC A, CURRIE G. Using Lorenz curves to assess public transport equity[J]. Journal of Transport Geography, 2011, 19(6): 1252-1259. doi: 10.1016/j.jtrangeo.2011.02.008
    [31]
    SHAFIQ M, LOBO A, COUTO A. Equity of access to rail services by complementary motorized and active modes[J]. Journal of Transport Geography, 2024, 121: 104007. doi: 10.1016/j.jtrangeo.2024.104007
    [32]
    GAO W X, CUI M C, PAN E T, et al. Green commuting within the x-minute city: Towards a systematic evaluation of its feasibility[J]. Journal of Transport Geography, 2024, 121: 104003. doi: 10.1016/j.jtrangeo.2024.104003
    [33]
    ACHEAMPONG R A, ASABERE S B. Urban expansion and differential accessibility by car and public transport in the Greater Kumasi city-region, Ghana: A geospatial modelling approach[J]. Journal of Transport Geography, 2022, 98: 103257. doi: 10.1016/j.jtrangeo.2021.103257
    [34]
    FRIEDMAN J H. Greedy function approximation: A gradient boosting machine[J]. The Annals of Statistics, 2001, 29(5): 1189-1232. doi: 10.1214/aos/1013203450
    [35]
    SHKERA A, PATANKAR V. Assessing non-linear influences of urban layout on driving travel distance: A mumbai case study[J]. The Open Transportation Journal, 2024, 18: e26671212348646. doi: 10.2174/0126671212348646241003061532
    [36]
    XIAO L Z, LO S, LIU J X, et al. Nonlinear and synergistic effects of TOD on urban vibrancy: Applying local explanations for gradient boosting decision tree[J]. Sustainable Cities and Society, 2021, 72: 103063. doi: 10.1016/j.scs.2021.103063
    [37]
    WANG J B, JASON CAO X, LIU K, et al. Exploring the nonlinear effects of built environment characteristics on customized bus service[J]. Transportation Research Part D: Transport and Environment, 2023, 114: 103523. doi: 10.1016/j.trd.2022.103523
    [38]
    ZHANG Y R, HAGHANI A. A gradient boosting method to improve travel time prediction[J]. Transportation Research Part C: Emerging Technologies, 2015, 58: 308-324. doi: 10.1016/j.trc.2015.02.019
    [39]
    YAN Ying, WANG Yu-ying, ZHOU Xuan, et al. Analysis of autonomous vehicle accident severity factors based on hybrid model[J]. Journal of Traffic and Transportation Engineering, 2025, 25(1): 184-196. doi: 10.19818/j.cnki.1671-1637.2025.01.013
    [40]
    FREY B J, DUECK D. Mixture modeling by affinity propagation[J]. Advances in Neural Information Processing Systems, 2005, 18: 1-8.
    [41]
    GUO X, YANG Z X, LI C, et al. Combining the classic vulnerability index and affinity propagation clustering algorithm to assess the intrinsic aquifer vulnerability of coastal aquifers on an integrated scale[J]. Environmental Research, 2023, 217: 114877. doi: 10.1016/j.envres.2022.114877
    [42]
    WEI Z X, HE D Q, JIN Z Z, et al. Density-based affinity propagation tensor clustering for intelligent fault diagnosis of train bogie bearing[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(6): 6053-6064. doi: 10.1109/TITS.2023.3253087
    [43]
    ZHAO Y L, YANG L T, ZHANG R H. A tensor-based multiple clustering approach with its applications in automation systems[J]. IEEE Transactions on Industrial Informatics, 2018, 14(1): 283-291. doi: 10.1109/TII.2017.2748800
    [44]
    DENG Z L, LI L N, DU Y J. Can polycentric urban morphology improve transportation carbon emission efficiency? Evidence from 285 Chinese cities, 2005—2020[J]. Transportation, 2024: 1-28.
    [45]
    MENG M X, SHANG Y P, YANG Y. Did highways cause the urban polycentric spatial structure in the Shanghai metropolitan area?[J]. Journal of Transport Geography, 2021, 92: 103022. doi: 10.1016/j.jtrangeo.2021.103022
    [46]
    YUAN Xiao-ling, WANG Shu-bei, HE Bin. The evolution and driving factors of polycentric development pattern in Chinese cities[J]. Urban Problems, 2023(12): 60-69.
    [47]
    ZHANG T L, CHEN WW, SUN B D. The mechanism for the intra-city employment growth: The role of employment centers[J]. Cities, 2025, 160: 105810. doi: 10.1016/j.cities.2025.105810
    [48]
    XU Y M, YAN X, LIU X Y, et al. Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships[J]. Transportation Research Part A: Policy and Practice, 2021, 144: 170-188. doi: 10.1016/j.tra.2020.12.005
    [49]
    LIU X, CHEN X H, POTOGLOU D, et al. Travel impedance, the built environment, and customized-bus ridership: A stop-to-stop level analysis[J]. Transportation Research Part D: Transport and Environment, 2023, 122: 103889. doi: 10.1016/j.trd.2023.103889
    [50]
    TU M T, LI W X, ORFILA O, et al. Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu[J]. Transportation Research Part D: Transport and Environment, 2021, 93: 102776. doi: 10.1016/j.trd.2021.102776
    [51]
    GAN Z X, YANG M, FENG T, et al. Examining the relationship between built environment and metro ridership at station-to-station level[J]. Transportation Research Part D: Transport and Environment, 2020, 82: 102332. doi: 10.1016/j.trd.2020.102332
    [52]
    HU Di. Layout of multi-level rail transit network in Shanghai southern region[J]. Urban Mass Transit, 2023(S2): 19-22.
    [53]
    HU Kang-qiong. Shanghai rail transit integration development strategy based on Yangtze River Delta integration[J]. Urban Mass Transit, 2024, 27(1): 6-10, 16.
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