IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v49y2022i6d10.1007_s11116-021-10219-y.html
   My bibliography  Save this article

Effects of spatial units and travel modes on urban commuting demand modeling

Author

Listed:
  • Fan Gao

    (Central South University)

  • Jinjun Tang

    (Central South University)

  • Zhitao Li

    (Central South University)

Abstract

Understanding the relationships between commuting demand and built environment (BE) features is advantageous in alleviating travel pressure during the commuting period. Numerous works have explored these relationships under either a single spatial unit or travel modes. However, few efforts have attempted to examine how variations in both spatial units and transport modes affect the relationships. To address the above issues, we investigate the impact of the modifiable areal unit problem (MAUP) on the modeling results across three partitioning schemes (i.e., hexagonal-based, square-based, and administrative boundary) and nine spatial units under four commuting modes (i.e., taxi, bike-sharing, ride-hailing, and bus). A geographically weighted regression is applied to estimate commuting demands considering spatial autocorrelation and heterogeneity. The experiment is conducted using smart card data and vehicle GPS trajectories collected in Shenzhen City, China. The contributions of this study include the following three parts. First, it evaluates the pros and cons of different spatial units for commuting demand modeling. Second, it summarizes the effects of the MAUP on relationships among land use, demographic, and traffic network characteristics and commuting demand according to the partitioning scale and schemes. Third, it examines the similarities and differences in the effect of the MAUP on commuting demand modeling among four travel modes. The findings of this study contribute to the division of traffic analysis zones and the allocation of commuting trips.

Suggested Citation

  • Fan Gao & Jinjun Tang & Zhitao Li, 2022. "Effects of spatial units and travel modes on urban commuting demand modeling," Transportation, Springer, vol. 49(6), pages 1549-1575, December.
    • Handle: RePEc:kap:transp:v:49:y:2022:i:6:d:10.1007_s11116-021-10219-y
    DOI: 10.1007/s11116-021-10219-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-021-10219-y
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-021-10219-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Etminani-Ghasrodashti, Roya & Ardeshiri, Mahyar, 2016. "The impacts of built environment on home-based work and non-work trips: An empirical study from Iran," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 196-207.
    2. Ding, Chuan & Cao, Xinyu & Wang, Yunpeng, 2018. "Synergistic effects of the built environment and commuting programs on commute mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 104-118.
    3. Hu, Yujie & Wang, Fahui, 2015. "Decomposing excess commuting: a Monte Carlo simulation approach," Journal of Transport Geography, Elsevier, vol. 44(C), pages 43-52.
    4. Cervero, Robert, 1996. "Mixed land-uses and commuting: Evidence from the American Housing Survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(5), pages 361-377, September.
    5. Qian Duan & Xin Ye & Jian Li & Ke Wang, 2020. "Empirical Modeling Analysis of Potential Commute Demand for Carsharing in Shanghai, China," Sustainability, MDPI, vol. 12(2), pages 1-18, January.
    6. Chakour, Vincent & Eluru, Naveen, 2016. "Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal," Journal of Transport Geography, Elsevier, vol. 51(C), pages 205-217.
    7. Biehl, Alec & Ermagun, Alireza & Stathopoulos, Amanda, 2018. "Community mobility MAUP-ing: A socio-spatial investigation of bikeshare demand in Chicago," Journal of Transport Geography, Elsevier, vol. 66(C), pages 80-90.
    8. Honghu Sun & Feng Zhen & Yupei Jiang, 2020. "Study on the Characteristics of Urban Residents’ Commuting Behavior and Influencing Factors from the Perspective of Resilience Theory: Theoretical Construction and Empirical Analysis from Nanjing, Chi," IJERPH, MDPI, vol. 17(5), pages 1-17, February.
    9. Jinhyun Hong & Qing Shen & Lei Zhang, 2014. "How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales," Transportation, Springer, vol. 41(3), pages 419-440, May.
    10. Zhongqiang Bai & Juanle Wang & Mingming Wang & Mengxu Gao & Jiulin Sun, 2018. "Accuracy Assessment of Multi-Source Gridded Population Distribution Datasets in China," Sustainability, MDPI, vol. 10(5), pages 1-15, April.
    11. Niedzielski, Michael A. & Horner, Mark W. & Xiao, Ningchuan, 2013. "Analyzing scale independence in jobs-housing and commute efficiency metrics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 58(C), pages 129-143.
    12. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    13. Lee, Changyeon, 2020. "Impacts of two-scale urban form and their combined effects on commute modes in U.S. metropolitan areas," Journal of Transport Geography, Elsevier, vol. 88(C).
    14. Benita, Francisco, 2020. "Carpool to work: Determinants at the county-level in the United States," Journal of Transport Geography, Elsevier, vol. 87(C).
    15. Abdel-Aty, Mohamed & Lee, Jaeyoung & Siddiqui, Chowdhury & Choi, Keechoo, 2013. "Geographical unit based analysis in the context of transportation safety planning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 62-75.
    16. Yi Ren & Tong Xia & Yong Li & Xiang Chen, 2019. "Predicting socio-economic levels of urban regions via offline and online indicators," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-15, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lu, Zhong-Wen & Xu, Yuan-Hao & Chen, Jie & Hu, Mao-Bin, 2023. "Investigation of traffic-driven epidemic spreading by taxi trip data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    2. Zhitao Li & Xiaolu Wang & Fan Gao & Jinjun Tang & Hanmeng Xu, 2024. "Analysis of mobility patterns for urban taxi ridership: the role of the built environment," Transportation, Springer, vol. 51(4), pages 1409-1431, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gao, Fan & Yang, Linchuan & Han, Chunyang & Tang, Jinjun & Li, Zhitao, 2022. "A network-distance-based geographically weighted regression model to examine spatiotemporal effects of station-level built environments on metro ridership," Journal of Transport Geography, Elsevier, vol. 105(C).
    2. Ding, Chuan & Cao, Xinyu & Yu, Bin & Ju, Yang, 2021. "Non-linear associations between zonal built environment attributes and transit commuting mode choice accounting for spatial heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 22-35.
    3. Zhao, Chuyun & Tang, Jinjun & Zeng, Yu & Li, Zhitao & Gao, Fan, 2023. "Understanding the spatio-temporally heterogeneous effects of built environment on urban travel emissions," Journal of Transport Geography, Elsevier, vol. 112(C).
    4. Liu, Jixiang & Wang, Bo & Xiao, Longzhu, 2021. "Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach," Journal of Transport Geography, Elsevier, vol. 92(C).
    5. Ta, Na & Zhao, Ying & Chai, Yanwei, 2016. "Built environment, peak hours and route choice efficiency: An investigation of commuting efficiency using GPS data," Journal of Transport Geography, Elsevier, vol. 57(C), pages 161-170.
    6. Xingang Zhou & Anthony G. O. Yeh, 2021. "Understanding the modifiable areal unit problem and identifying appropriate spatial unit in jobs–housing balance and employment self-containment using big data," Transportation, Springer, vol. 48(3), pages 1267-1283, June.
    7. Li, Mengya & Kwan, Mei-Po & Hu, Wenyan & Li, Rui & Wang, Jun, 2023. "Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 113(C).
    8. Enayat Mirzaei & Dominique Mignot, 2021. "An Empirical Analysis of Mode Choice Decision for Utilitarian and Hedonic Trips: Evidence from Iran," Sustainability, MDPI, vol. 13(12), pages 1-23, June.
    9. Laviolette, Jérôme & Morency, Catherine & Waygood, E.O.D., 2022. "A kilometer or a mile? Does buffer size matter when it comes to car ownership?," Journal of Transport Geography, Elsevier, vol. 104(C).
    10. Yang, Hongtai & Zheng, Rong & Li, Xuan & Huo, Jinghai & Yang, Linchuan & Zhu, Tong, 2022. "Nonlinear and threshold effects of the built environment on e-scooter sharing ridership," Journal of Transport Geography, Elsevier, vol. 104(C).
    11. Ding, Chuan & Cao, Xinyu & Liu, Chao, 2019. "How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds," Journal of Transport Geography, Elsevier, vol. 77(C), pages 70-78.
    12. Zuo, Ting & Wei, Heng & Liu, Hao & Yang, Y. Jeffrey, 2019. "Bi-level optimization approach for configuring population and employment distributions with minimized vehicle travel demand," Journal of Transport Geography, Elsevier, vol. 74(C), pages 161-172.
    13. Faan Chen & Adriano Borges Costa, 2024. "Exploring the causal effects of the built environment on travel behavior: a unique randomized experiment in Shanghai," Transportation, Springer, vol. 51(1), pages 215-245, February.
    14. Yujie Hu & Fahui Wang & Chester Wilmot, 2020. "Commuting Variability by Wage Groups in Baton Rouge 1990-2010," Papers 2006.03498, arXiv.org.
    15. Wu, Pan & Xu, Lunhui & Zhong, Lingshu & Gao, Kun & Qu, Xiaobo & Pei, Mingyang, 2022. "Revealing the determinants of the intermodal transfer ratio between metro and bus systems considering spatial variations," Journal of Transport Geography, Elsevier, vol. 104(C).
    16. Jiangping Zhou & Enda Murphy & Jonathan Corcoran, 2020. "Integrating road carrying capacity and traffic congestion into the excess commuting framework: The case of Los Angeles," Environment and Planning B, , vol. 47(1), pages 119-137, January.
    17. Liya Yang & Lingqian Hu & Zhenbo Wang, 2019. "The built environment and trip chaining behaviour revisited: The joint effects of the modifiable areal unit problem and tour purpose," Urban Studies, Urban Studies Journal Limited, vol. 56(4), pages 795-817, March.
    18. Ao, Yibin & Yang, Dujuan & Chen, Chuan & Wang, Yan, 2019. "Exploring the effects of the rural built environment on household car ownership after controlling for preference and attitude: Evidence from Sichuan, China," Journal of Transport Geography, Elsevier, vol. 74(C), pages 24-36.
    19. Marques, Samuel de França & Pitombo, Cira Souza, 2023. "Local modeling as a solution to the lack of stop-level ridership data," Journal of Transport Geography, Elsevier, vol. 112(C).
    20. Saadi, Ismaïl & Boussauw, Kobe & Teller, Jacques & Cools, Mario, 2016. "Trends in regional jobs-housing proximity based on the minimum commute: The case of Belgium," Journal of Transport Geography, Elsevier, vol. 57(C), pages 171-183.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:transp:v:49:y:2022:i:6:d:10.1007_s11116-021-10219-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.