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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
20.12.2023
DOI
https://doi.org/10.7307/ptt.v35i6.273
ISSUE
2023 (Vol 35), Issue 6
LICENSE
Copyright (c) 2024 Wenjing LIU, Jinbao ZHAO, Jiawei JIANG, Mingxing LI, Yuejuan XU, Keke HOU, Shengli ZHAO
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Investigating the Multiscale Impact of Environmental Factors on the Integrated Use of Dockless Bike-Sharing and Urban Rail Transit

Authors:Wenjing LIU, Jinbao ZHAO, Jiawei JIANG, Mingxing LI, Yuejuan XU, Keke HOU, Shengli ZHAO

Abstract

Dockless bike-sharing (DBS) is an effective solution to the “first and last mile” problem in urban transportation. It can be integrated with urban rail transit (URT) to provide passengers with more convenient travel services. This study focuses on the integrated use of DBS and URT in Shenzhen, utilising a multi-buffer zone approach to identify DBS data within URT station catchment areas. By employing ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models, the spatiotemporal heterogeneity of integrated use and its relationship with environmental factors surrounding URT stations were examined. The empirical findings highlight the superiority of the MGWR model in accurately explaining spatial relationships compared to the OLS and GWR models. Furthermore, the study reveals that the impact of built environment factors on integrated use varies during morning and evening peak periods, as well as in terms of access and egress. Specifically, factors such as catering, shopping, companies, residential buildings, bus stops, minor roads, transfer stations and population density were found to influence the integrated use of DBS and URT. These findings not only contribute to the promotion of the DBS-URT integration but also promote the overall development of urban transportation.

Keywords:bike-sharing, urban rail transit, multiscale geographically weighted regression, environmental factors

References

  1. [1] Liu J, et al. Measures of accessibility incorporating time reliability for an urban rail transit network: A case study in Wuhan, China. Transportation Research Part A. 2022;165:471-489. DOI: 10.1016/J.TRA.2022.09.011.
  2. [2] Wu Y, et al. Estimating anthropogenic heat from an urban rail transit station: A Case study of Qingsheng metro station, Guangzhou, China. Sustainable Cities and Society. 2022;82. DOI: 10.1016/J.SCS.2022.103895.
  3. [3] Chen W, et al. What factors influence ridership of station-based bike sharing and free-floating bike sharing at rail transit stations? International Journal of Sustainable Transportation. 2022;16(4):357-373. DOI: 10.1080/15568318.2021.1872121.
  4. [4] Kim M, Cho G. Examining the causal relationship between bike-share and public transit in response to the COVID-19 pandemic. Cities. 2022;131:104024. DOI: 10.1016/J.CITIES.2022.104024.
  5. [5] Guo D, et al. Exploring the role of passengers’ attitude in the integration of dockless bike-sharing and public transit: A hybrid choice modeling approach. Journal of Cleaner Production. 2023;384. DOI: 10.1016/J.JCLEPRO.2022.135627.
  6. [6] Wu X, et al. The impacts of the built environment on bicycle-metro transfer trips: A new method to delineate metro catchment area based on people's actual cycling space. Journal of Transport Geography. 2021;97. DOI: 10.1016/J.JTRANGEO.2021.103215.
  7. [7] Wang R, Wu J, Qi G. Exploring regional sustainable commuting patterns based on dockless bike-sharing data and POI data. Journal of Transport Geography. 2022;102. DOI: 10.1016/J.JTRANGEO. 2022.103395.
  8. [8] Li A, et al. An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai, China. Journal of Transport Geography. 2020;88. DOI: 10.1016/j.jtrangeo.2020.102828.
  9. [9] Chen Z, van Lierop D, Ettema D. Travel satisfaction with dockless bike-sharing: Trip stages, attitudes and the built environment. Transportation Research Part D. 2022;106. DOI: 10.1016/J.TRD.2022.103280.
  10. [10] Zhuang C, et al. Nonlinear and threshold effects of traffic condition and built environment on dockless bike sharing at street level. Journal of Transport Geography. 2022;102. DOI: 10.1016/J.JTRANGEO.2022.103375.
  11. [11] Guo Y, He SY. The role of objective and perceived built environments in affecting dockless bike-sharing as a feeder mode choice of metro commuting. Transportation Research Part A. 2021;149:377-396. DOI: 10.1016/J.TRA.2021.04.008.
  12. [12] Ahmadreza F, et al. How land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal. Journal of Transport Geography. 2014;41:306-314. DOI: 10.1016/j.jtrangeo.2014.01.013.
  13. [13] Cao M, et al. Analysis of the cycling flow between origin and destination for dockless shared bicycles based on singular value decomposition. ISPRS International Journal of Geo-Information. 2019;8(12):573. DOI: 10.3390/ijgi8120573.
  14. [14] Zhou R, et al. Research on travel characteristics of shared bicycles based on spatio-temporal data. Journal of Wuhan University of Technology (Transportation Science & Engineering). 2019;43:159-163.
  15. [15] Lin P, et al. Revealing spatio-temporal patterns and influencing factors of dockless bike sharing demand. IEEE Access. 2020;8:1. DOI: 10.1109/access.2020.2985329.
  16. [16] Noland R, Smart M, Guo Z. Bikeshare trip generation in New York City. Transportation Research Part A. 2016;94:164-181. DOI: 10.1016/j.tra.2016.08.030.
  17. [17] Zhang Y, et al. Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan, China. Journal of Transport Geography. 2017;58:59-70. DOI: 10.1016/j.jtrangeo.2016.11.014.
  18. [18] Chen Q, et al. Reposition optimization in free-floating bike-sharing system: A case study in Shenzhen City. Physica A: Statistical Mechanics and its Applications. 2022;593. DOI: 10.1016/J.PHYSA.2022.126925.
  19. [19] Ji Y, et al. Comparison of usage regularity and its determinants between docked and dockless bike-sharing systems: A case study in Nanjing, China. Journal of Cleaner Production. 2020;255(C). DOI: 10.1016/j.jclepro.2020.120110.
  20. [20] Fan Y, Zheng S. Dockless bike sharing alleviates road congestion by complementing subway travel: Evidence from Beijing. Cities. 2020;107(5):102895. DOI: 10.1016/j.cities.2020.102895.
  21. [21] Li Y, Zhu Z, Guo X. Operating characteristics of dockless bike-sharing systems near metro stations: Case study in Nanjing City, China. Sustainability. 2019;11(8):2256. DOI: 10.3390/su11082256.
  22. [22] Lin D, et al. The analysis of catchment areas of metro stations using trajectory data generated by dockless shared bikes. Sustainable Cities and Society. 2019;49:101598. DOI: 10.1016/j.scs.20 19.101598.
  23. [23] Liu S, et al. Understanding spatial-temporal travel demand of private and shared e-bikes as a feeder mode of metro stations. Journal of Cleaner Production. 2023;398. DOI: 10.1016/J.JCLEPRO.2023.136602.
  24. [24] Li L, et al. Unbalanced usage of free-floating bike sharing connecting with metro stations. Physica A: Statistical Mechanics and its Applications. 2022;608:128245. DOI: 10.1016/j.physa.2022.128245.
  25. [25] Liu S, et al. Concordance between regional functions and mobility features using bike-sharing and land-use data near metro stations. Sustainable Cities and Society. 2022;84. DOI: 10.1016/J.SCS.2022.104010.
  26. [26] Liu Y, et al. Use frequency of metro–bikeshare integration: Evidence from Nanjing, China. Sustainability. 2020;12(4):1426. DOI: 10.3390/su12041426.
  27. [27] Zhao P, Li S. Bicycle-metro integration in a growing city: The determinants of cycling as a transfer mode in metro station areas in Beijing. Transportation Research Part A: Policy and Practice. 2017;99:46-60. DOI: 10.1016/j.tra.2017.03.003.
  28. [28] Cheng L, et al. Exploring non-linear built environment effects on the integration of free-floating bike-share and urban rail transport: A quantile regression approach. Transportation Research Part A. 2022;162:175-187. DOI: 10.1016/J.TRA.2022.05.022.
  29. [29] Gao Y, et al. Spatial-temporal characteristics and influencing factors of source and sink of dockless sharing bicycles connected to subway station. Journal of Geo-Information Science. 2021;23:155-170. DOI: 10.12082/dqxxkx.2021.200351.
  30. [30] Ma X, et al. Modeling the factors influencing the activity spaces of bikeshare around metro stations: A spatial regression model. Sustainability. 2018;10(11):3949. DOI: 10.3390/su10113949.
  31. [31] Guo Y, et al. Dockless bike-sharing as a feeder mode of metro commute? The role of the feeder-related built environment: Analytical framework and empirical evidence. Sustainable Cities and Society. 2020. DOI: 10.1016/j.scs.2020.102594.
  32. [32] Lu B, et al. Uncovering drivers of community-level house price dynamics through multiscale geographically weighted regression: A case study of Wuhan, China. Spatial Statistics. 2023;53:100723. DOI: 10.1016/j.spasta.2022.100723.
  33. [33] Li Z, et al. Exploring the multiscale relationship between the built environment and the metro-oriented dockless bike-sharing usage. International Journal of Environmental Research and Public Health. 2022;19(4):2323. DOI: 10.3390/IJERPH19042323.
  34. [34] Ayodeji EI, Tolulope O. Is there a relationship between economic indicators and road fatalities in Texas? A multiscale geographically weighted regression analysis. GeoJournal. 2020;86(6). DOI: 10.1007/s10708-020-10232-1.
  35. [35] Bureau ST. Assessment report on the development of Internet rental bicycles in Shenzhen. http://jtys.sz.gov.cn/zwgk/ztzl/msss/2021gjcxxc/jbqk/content/post_9143091.html.
  36. [36] Bureau STP. A reminder! Shenzhen officially resumes morning and evening peak traffic restrictions! http://szjj.sz.gov.cn/m/YD_TPXW/content/post_7915693.html.
  37. [37] Shenzhen Metro: Subway lines, operation time & ticketing. https://www.szmc.net/shentieyunying/yunyingfuwu/zhandianchaxun/.
  38. [38] Li W, et al. Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros. Journal of Transport Geography. 2021;92:103032. DOI: 10.1016/j.jtrangeo.2021.103032.
  39. [39] Bivina GR, Gupta A, Parida M. Walk accessibility to metro stations: An analysis based on meso- or micro-scale built environment factors. Sustainable Cities and Society. 2020;55:102047. DOI: 10.1016/j.scs.2020.102047.
  40. [40] Cheng L, et al. Exploring non-linear built environment effects on the integration of free-floating bike-share and urban rail transport: A quantile regression approach. Transportation Research Part A. 2022;162. DOI: 10.1016/j.scs.2020.102047.
  41. [41] Anselin L. Spatial econometrics: Methods and models. Springer Science & Business Media. 1988.
  42. [42] Brunsdon C, et al. Geographically weighted regression—Modelling spatial non-stationarity. The Statistician. 1998;47:431-443. DOI: 10.1111/1467-9884.00145.
  43. [43] An R, et al. How the built environment promotes public transportation in Wuhan: A multiscale geographically weighted regression analysis. Travel Behaviour and Society. 2022;29:186-199. DOI: 10.1016/J.TBS.2022.06.011.
  44. [44] Chao X, et al. Spatial variation of green space equity and its relation with urban dynamics: A case study in the region of Munich. Ecological Indicators. 2018;93:512-523. DOI: 10.1016/j.ecolind.2018.05.024.
  45. [45] Wu C, Kim I, Chung H. The effects of built environment spatial variation on bike-sharing usage: A case study of Suzhou, China. Cities. 2021;110. DOI: 10.1016/J.CITIES.2020.103063.
  46. [46] 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.
  47. [47] Liu J, Chau KW, Bao Z. Multiscale spatial analysis of metro usage and its determinants for sustainable urban development in Shenzhen, China. Tunnelling and Underground Space Technology Incorporating Trenchless Technology Research. 2023;133. DOI: 10.1016/J.TUST.2022.104912.
  48. [48] Wang X, et al. Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China. Land Use Policy. 2022;119. DOI: 10.1016/J.LANDUSEPOL.2022.106162.
  49. [49] Guo Y, He SY. Built environment effects on the integration of dockless bike-sharing and the metro. Transportation Research Part D: Transport and Environment. 2020;83:102335. DOI: 10.1016/j.trd.2020.102335.
  50. [50] 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.
  51. [51] Gao F, et al. A network-distance-based geographically weighted regression model to examine spatiotemporal effects of station-level built environments on metro ridership. Journal of Transport Geography. 2022;105. DOI: 10.1016/J.JTRANGEO.2022.103472.
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