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How to improve urban transportation planning in big data era? A practice in the study of traffic analysis zone delineation

Author

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  • Yang, Binyu
  • Tian, Yuan
  • Wang, Jian
  • Hu, Xiaowei
  • An, Shi

Abstract

Traffic analysis zone (TAZ) is the basic unit of urban transportation planning. The appropriateness of TAZ delineation will affect the rationality of transportation planning and the accuracy of transportation analysis, and thus affect the final planning decision-making. However, in the big data era, there is still a lack of methods to integrate multi-source data for TAZ delineation. How to effectively fuse multi-source heterogeneous data in the TAZ delineation process is still an unsolved technical difficulty. To fill this gap, this paper designs a multi-source data-driven stepwise strategy to solve the TAZ delineation problem by creating a zoning system with zones showing homogeneous mobility behaviors and containing homogeneous land use characteristics. Firstly, mining the spatial-temporal travel features and land use information of transit stations and parcels from multi-source data, including transit smart card data, ride-hailing data, bike-sharing trip data, and point-of-interest (POI) data. Then, a core parcel determination algorithm which mainly consists of Fuzzy C-Means station clustering and a constructed parcel core degree function is proposed to generate the core parcels of each potential TAZ. After that, parcels are clustered around the core ones into TAZs through a multi-feature driven clustering algorithm, in the process of which the homogeneity within TAZs is guaranteed. Finally, the optimal zoning system is obtained by comparing the information loss calculation result of multiple zoning schemes. Taking Beijing as a case study area, 624 TAZs are obtained by applying the proposed method. The delineation result is comparatively analyzed with TAZs obtained by the traditional delineation method and a baseline method by applying multi-indicator measurement. The result reveals that the presented approach can promote the rationality of TAZ delineation.

Suggested Citation

  • Yang, Binyu & Tian, Yuan & Wang, Jian & Hu, Xiaowei & An, Shi, 2022. "How to improve urban transportation planning in big data era? A practice in the study of traffic analysis zone delineation," Transport Policy, Elsevier, vol. 127(C), pages 1-14.
    • Handle: RePEc:eee:trapol:v:127:y:2022:i:c:p:1-14
    DOI: 10.1016/j.tranpol.2022.08.002
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    as
    1. Kasraian, Dena & Raghav, Shivani & Miller, Eric J., 2020. "A multi-decade longitudinal analysis of transportation and land use co-evolution in the Greater Toronto-Hamilton Area," Journal of Transport Geography, Elsevier, vol. 84(C).
    2. Chowdhury, Tufayel & Scott, Darren M., 2020. "An analysis of the built environment and auto travel in Halifax, Canada," Transport Policy, Elsevier, vol. 94(C), pages 23-33.
    3. Li, Yongling & Geertman, Stan & Hooimeijer, Pieter & Lin, Yanliu & Yang, Haoran, 2021. "Do migrants and locals differ in commuting behavior? A case study of Xiamen, China," Transport Policy, Elsevier, vol. 108(C), pages 1-10.
    4. Menon, Aditya Krishna & Cai, Chen & Wang, Weihong & Wen, Tao & Chen, Fang, 2015. "Fine-grained OD estimation with automated zoning and sparsity regularisation," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 150-172.
    5. Huang, Ruihong, 2020. "Transit-based job accessibility and urban spatial structure," Journal of Transport Geography, Elsevier, vol. 86(C).
    6. Chandra, Aitichya & Sharath, M.N. & Pani, Agnivesh & Sahu, Prasanta K., 2021. "A multi-objective genetic algorithm approach to design optimal zoning systems for freight transportation planning," Journal of Transport Geography, Elsevier, vol. 92(C).
    7. Pan, Yingjiu & Chen, Shuyan & Li, Tiezhu & Niu, Shifeng & Tang, Kun, 2019. "Exploring spatial variation of the bus stop influence zone with multi-source data: A case study in Zhenjiang, China," Journal of Transport Geography, Elsevier, vol. 76(C), pages 166-177.
    8. Zhou, Jiangping & Murphy, Enda & Long, Ying, 2014. "Commuting efficiency in the Beijing metropolitan area: an exploration combining smartcard and travel survey data," Journal of Transport Geography, Elsevier, vol. 41(C), pages 175-183.
    9. Prasanta K. Sahu & Aitichya Chandra & Agnivesh Pani & Bandhan Bandhu Majumdar, 2020. "Designing freight traffic analysis zones for metropolitan areas: identification of optimal scale for macro-level freight travel analysis," Transportation Planning and Technology, Taylor & Francis Journals, vol. 43(6), pages 620-637, August.
    10. Lee, Jaeyoung & Abdel-Aty, Mohamed & Jiang, Ximiao, 2014. "Development of zone system for macro-level traffic safety analysis," Journal of Transport Geography, Elsevier, vol. 38(C), pages 13-21.
    11. De Vos, Jonas & Cheng, Long & Kamruzzaman, Md. & Witlox, Frank, 2021. "The indirect effect of the built environment on travel mode choice: A focus on recent movers," Journal of Transport Geography, Elsevier, vol. 91(C).
    12. Yanyan Chen & Zheng Zhang & Tianwen Liang, 2019. "Assessing Urban Travel Patterns: An Analysis of Traffic Analysis Zone-Based Mobility Patterns," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    13. Ma, Xiaolei & Liu, Congcong & Wen, Huimin & Wang, Yunpeng & Wu, Yao-Jan, 2017. "Understanding commuting patterns using transit smart card data," Journal of Transport Geography, Elsevier, vol. 58(C), pages 135-145.
    14. Keunhyun Park & Sadegh Sabouri & Torrey Lyons & Guang Tian & Reid Ewing, 2020. "Intrazonal or interzonal? Improving intrazonal travel forecast in a four-step travel demand model," Transportation, Springer, vol. 47(5), pages 2087-2108, October.
    15. H. M. Abdul Aziz & Nicholas N. Nagle & April M. Morton & Michael R. Hilliard & Devin A. White & Robert N. Stewart, 2018. "Exploring the impact of walk–bike infrastructure, safety perception, and built-environment on active transportation mode choice: a random parameter model using New York City commuter data," Transportation, Springer, vol. 45(5), pages 1207-1229, September.
    16. Luis Martínez & José Viegas & Elisabete Silva, 2009. "A traffic analysis zone definition: a new methodology and algorithm," Transportation, Springer, vol. 36(5), pages 581-599, September.
    17. Atkinson-Palombo, Carol & Kuby, Michael J., 2011. "The geography of advance transit-oriented development in metropolitan Phoenix, Arizona, 2000–2007," Journal of Transport Geography, Elsevier, vol. 19(2), pages 189-199.
    18. Dawen Xia & Binfeng Wang & Yantao Li & Zhuobo Rong & Zili Zhang, 2015. "An Efficient MapReduce-Based Parallel Clustering Algorithm for Distributed Traffic Subarea Division," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-18, September.
    19. Milne, Dave & Watling, David, 2019. "Big data and understanding change in the context of planning transport systems," Journal of Transport Geography, Elsevier, vol. 76(C), pages 235-244.
    20. You-Jin Jung & Jeffrey M. Casello, 2020. "Assessment of the transit ridership prediction errors using AVL/APC data," Transportation, Springer, vol. 47(6), pages 2731-2755, December.
    21. Arefeh Nasri & Carlos Carrion & Lei Zhang & Babak Baghaei, 2020. "Using propensity score matching technique to address self-selection in transit-oriented development (TOD) areas," Transportation, Springer, vol. 47(1), pages 359-371, February.
    22. Linn, Joshua & Wang, Zhongmin & Xie, Lunyu, 2016. "Who will be affected by a congestion pricing scheme in Beijing?," Transport Policy, Elsevier, vol. 47(C), pages 34-40.
    23. Ghadiri, Mehdi & Rassafi, Amir Abbas & Mirbaha, Babak, 2019. "The effects of traffic zoning with regular geometric shapes on the precision of trip production models," Journal of Transport Geography, Elsevier, vol. 78(C), pages 150-159.
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