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Designing freight traffic analysis zones for metropolitan areas: identification of optimal scale for macro-level freight travel analysis

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  • Prasanta K. Sahu
  • Aitichya Chandra
  • Agnivesh Pani
  • Bandhan Bandhu Majumdar

Abstract

This paper contributes to the emerging literature on freight studies by identifying the optimal freight traffic analysis zone (FTAZ) system at which to conduct macro-level freight travel analysis. To arrive at the optimal scale, we develop alternate zone systems by grouping census wards with similar freight-related characteristics (industrial characteristics, commercial land use characteristics, locational characteristics and socio-demographic characteristics). The resultant zone systems are analysed at multiple geographic scales and the optimal scale of each zone system is determined by performing the Brown–Forsythe test. Results suggest that a 1:3 aggregation ratio (24–28 zones) is the optimal scale for Metropolitan FTAZs, whereas the publicly available ad-hoc zone system and prior literature on National FTAZs follow 1:10 aggregation. The study findings suggest that Metropolitan planning organizations need to reconsider their existing data collection strategy, consider a larger aggregation ratio and, by extension, adopt smaller zones to ensure that both local and global freight travel characteristics are captured in freight travel analyses.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:transp:v:43:y:2020:i:6:p:620-637
    DOI: 10.1080/03081060.2020.1780711
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    Cited by:

    1. Agnivesh Pani & Prasanta K. Sahu & Furqan A. Bhat, 2021. "Assessing the Spatial Transferability of Freight (Trip) Generation Models across and within States of India: Empirical Evidence and Implications for Benefit Transfer," Networks and Spatial Economics, Springer, vol. 21(2), pages 465-493, June.
    2. 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).
    3. Regal, Andrés & Gonzalez-Feliu, Jesús & Rodriguez, Michelle, 2023. "A spatio-functional logistics profile clustering analysis method for metropolitan areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    4. 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.
    5. Reda, Abel Kebede & Tavasszy, Lori & Gebresenbet, Girma & Ljungberg, David, 2023. "Modelling the effect of spatial determinants on freight (trip) attraction: A spatially autoregressive geographically weighted regression approach," Research in Transportation Economics, Elsevier, vol. 99(C).
    6. Balla, Bhavani Shankar & Sahu, Prasanta K., 2023. "Assessing regional transferability and updating of freight generation models to reduce sample size requirements in national freight data collection program," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    7. Pani, Agnivesh & Mishra, Sabya & Sahu, Prasanta, 2022. "Developing multi-vehicle freight trip generation models quantifying the relationship between logistics outsourcing and insourcing decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).

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