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Freight Transport Modeling: Review And Future Challenges

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  • Agostino Nuzzolo
  • Pierluigi Coppola
  • Antonio Comi

Abstract

This paper reviews the models proposed in the past two decades to forecast freight transport demand resulting from changes in infrastructures, services and regulation. A classification is proposed based on different scales of analysis (e.g. long-distance vs. short-distance freight transport), and on modeling approaches (e.g. aggregate vs. disaggregate). The first part of the paper focuses on the long-distance scale (i.e. national and international). In this context aggregate modeling efforts, using time series data, have either concentrated on the growth pattern for specific commodities or on the traditional “fourstep” model, typically applied to passenger demand. These models address large-scale problems, but are unable to deal explicitly with the micro-mechanisms underlying demand, where a more disaggregate approach is needed. The second part of the paper deals with short-distance models and with the distribution of final products from wholesalers and restocking centers to retailers and end consumers. These models can be cast into two classes: those simulating level and spatial distribution of commodities traded within the study area (Origin- Destination matrices) and those simulating the delivery process with the ultimate goal of estimating commercial vehicle flows on the road network. In particular, the aggregate class of models can be further split based on reference unit (i.e. vehicle, quantity and delivery). Finally, the paper discusses the pros and cons of both types of models (i.e. long and short distance), and describes the research challenges in this area for the near future.

Suggested Citation

  • Agostino Nuzzolo & Pierluigi Coppola & Antonio Comi, 2013. "Freight Transport Modeling: Review And Future Challenges," Articles, International Journal of Transport Economics, vol. 40(2).
  • Handle: RePEc:jte:journl:2013:2:40:2
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    Citations

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    Cited by:

    1. Daniel Kaszubowski, 2019. "A Method for the Evaluation of Urban Freight Transport Models as a Tool for Improving the Delivery of Sustainable Urban Transport Policy," Sustainability, MDPI, vol. 11(6), pages 1-23, March.
    2. Johansson, Magnus & Vierth, Inge & Holmgren, Kristina & Cullinane, Kevin, 2023. "How will electrification and increased use of new fuels affect the effectiveness of freight modal shift policies?," Working Papers 2023:4, Swedish National Road & Transport Research Institute (VTI).
    3. Ahmed, Usman & Roorda, Matthew J., 2022. "Modelling carrier type and vehicle type choice of small and medium size firms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    4. Zhang, M. & Pel, A.J., 2016. "Synchromodal hinterland freight transport: Model study for the port of Rotterdam," Journal of Transport Geography, Elsevier, vol. 52(C), pages 1-10.
    5. Demissie, Merkebe Getachew & Kattan, Lina, 2022. "Estimation of truck origin-destination flows using GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    6. Gatta, Valerio & Marcucci, Edoardo, 2014. "Urban freight transport and policy changes: Improving decision makers' awareness via an agent-specific approach," Transport Policy, Elsevier, vol. 36(C), pages 248-252.
    7. David A. Hensher & Edward Wei & Wen Liu & Loan Ho & Chinh Ho, 2023. "Development of a practical aggregate spatial road freight modal demand model system for truck and commodity movements with an application of a distance-based charging regime," Transportation, Springer, vol. 50(3), pages 1031-1071, June.
    8. Mohammad Zaher Akkad & Tamás Bányai, 2020. "Multi-Objective Approach for Optimization of City Logistics Considering Energy Efficiency," Sustainability, MDPI, vol. 12(18), pages 1-23, September.
    9. Jacek Oskarbski & Daniel Kaszubowski, 2018. "Applying a Mesoscopic Transport Model to Analyse the Effects of Urban Freight Regulatory Measures on Transport Emissions—An Assessment," Sustainability, MDPI, vol. 10(7), pages 1-18, July.
    10. 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).
    11. Al Hajj Hassan, Lama & Mahmassani, Hani S. & Chen, Ying, 2020. "Reinforcement learning framework for freight demand forecasting to support operational planning decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    12. Arencibia, Ana Isabel & Feo-Valero, María & García-Menéndez, Leandro & Román, Concepción, 2015. "Modelling mode choice for freight transport using advanced choice experiments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 252-267.
    13. Sowjanya Dhulipala & Gopal R. Patil, 2023. "Regional freight generation and spatial interactions in developing regions using secondary data," Transportation, Springer, vol. 50(3), pages 773-810, June.
    14. Antonio Comi & Antonio Polimeni, 2020. "Assessing the Potential of Short Sea Shipping and the Benefits in Terms of External Costs: Application to the Mediterranean Basin," Sustainability, MDPI, vol. 12(13), pages 1-17, July.
    15. Aguas, Oriana & Bachmann, Chris, 2022. "Assessing the effects of input uncertainties on the outputs of a freight demand model," Research in Transportation Economics, Elsevier, vol. 95(C).
    16. Hensher, David A. & Teye, Collins, 2019. "Commodity interaction in freight movement models for New South Wales," Journal of Transport Geography, Elsevier, vol. 80(C).
    17. Chen, Feier & Miao, Yuqi & Tian, Kang & Ding, Xiaoxu & Li, Tingyi, 2017. "Multifractal cross-correlations between crude oil and tanker freight rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 344-354.

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