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A time-dependent freight tour synthesis model

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  • Sánchez-Díaz, Iván
  • Holguín-Veras, José
  • Ban, Xuegang (Jeff)

Abstract

This paper introduces a model of urban freight demand that seeks to estimate tour flows from secondary data sources e.g., traffic counts, to bypass the need for expensive surveys. The model discussed in this paper, referred as Freight Tour Synthesis (FTS), enhances current techniques by incorporating the time-dependent tour-based behavior of freight vehicles, and the decision maker’s (e.g., metropolitan planning agency planner) preferences for different sources of information. The model, based on entropy maximization theory, estimates the most likely set of tour flows, given a set of freight trip generation estimates, a set of traffic counts per time interval, and total freight transportation cost in the network. The type of inputs used allows the assessment of changes in infrastructure, policy and land use. The ability of the model to replicate actual values is assessed using the Denver Region (CO) as a case study.

Suggested Citation

  • Sánchez-Díaz, Iván & Holguín-Veras, José & Ban, Xuegang (Jeff), 2015. "A time-dependent freight tour synthesis model," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 144-168.
    • Handle: RePEc:eee:transb:v:78:y:2015:i:c:p:144-168
    DOI: 10.1016/j.trb.2015.04.007
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    5. Pani, Agnivesh & Sahu, Prasanta K. & Tavasszy, Lóránt & Mishra, Sabya, 2023. "Freight activity-travel pattern generation (FAPG) as an enhancement of freight (trip) generation modelling: Methodology and case study," Transport Policy, Elsevier, vol. 144(C), pages 34-48.
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    7. Malik, Leeza & Tiwari, Geetam & Biswas, Udayin & Woxenius, Johan, 2021. "Estimating urban freight flow using limited data: The case of Delhi, India," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    8. Xiaoxuan Wei & Meng Ye & Liang Yuan & Wei Bi & Weisheng Lu, 2022. "Analyzing the Freight Characteristics and Carbon Emission of Construction Waste Hauling Trucks: Big Data Analytics of Hong Kong," IJERPH, MDPI, vol. 19(4), pages 1-21, February.
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    10. Diana P. Moreno-Palacio & Carlos A. Gonzalez-Calderon & John Jairo Posada-Henao & Hector Lopez-Ospina & Jhan Kevin Gil-Marin, 2022. "Entropy-Based Transit Tour Synthesis Using Fuzzy Logic," Sustainability, MDPI, vol. 14(21), pages 1-25, November.
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    12. Kalahasthi, Lokesh & Holguín-Veras, José & Yushimito, Wilfredo F., 2022. "A freight origin-destination synthesis model with mode choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    13. Sanchez-Diaz, Ivan, 2020. "Assessing the magnitude of freight traffic generated by office deliveries," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 279-289.
    14. Gonzalez-Calderon, Carlos A. & Holguín-Veras, José, 2019. "Entropy-based freight tour synthesis and the role of traffic count sampling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 121(C), pages 63-83.
    15. Galrão Ramos, A. & Oliveira, José F. & Gonçalves, José F. & Lopes, Manuel P., 2016. "A container loading algorithm with static mechanical equilibrium stability constraints," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 565-581.

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