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The influence of aggregation level and category construction on estimation quality for freight trip generation models

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  • Gonzalez-Feliu, Jesus
  • Sánchez-Díaz, Iván

Abstract

This paper analyzes the impacts of aggregation level and category construction on the relevance and quality of freight trip generation (FTG) models. More precisely, constant generations and functional form models are compared, as well as activity and activity-workforce categories. The paper proposes a method to compare constant generation and functional form models on different category classifications based on MAPE estimations. Functional forms are assessed via linear regression and compared using Pearson coefficient. Results show that the aggregation level has not always a positive impact on the model’s accuracy and the choice of suitable functional form leads to more accurate models.

Suggested Citation

  • Gonzalez-Feliu, Jesus & Sánchez-Díaz, Iván, 2019. "The influence of aggregation level and category construction on estimation quality for freight trip generation models," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 121(C), pages 134-148.
    • Handle: RePEc:eee:transe:v:121:y:2019:i:c:p:134-148
    DOI: 10.1016/j.tre.2018.07.007
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    11. Jesus Gonzalez-Feliu & Josep-Maria Salanova Grau & Adrien Beziat, 2014. "A location-based accessibility analysis to estimate the suitability of urban consolidation facilities," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(2), pages 166-185, July.
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    13. Guido Gentile & Daniele Vigo, 2013. "Movement generation and trip distribution for freight demand modelling applied to city logistics," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 54, pages 1-6.
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    Cited by:

    1. 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).
    2. 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).
    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. Puente-Mejia, Bernardo & Palacios-Argüello, Laura & Suárez-Núñez, Carlos & Gonzalez-Feliu, Jesus, 2020. "Freight trip generation modeling and data collection processes in Latin American cities. Modeling framework for Quito and generalization issues," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 226-241.
    5. 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).
    6. Usman Ahmed & Matthew J. Roorda, 2023. "Joint and sequential models for freight vehicle type and shipment size choice," Transportation, Springer, vol. 50(5), pages 1613-1629, October.
    7. Pani, Agnivesh & Sahu, Prasanta K. & Chandra, Aitichya & Sarkar, Ashoke K., 2019. "Assessing the extent of modifiable areal unit problem in modelling freight (trip) generation: Relationship between zone design and model estimation results," Journal of Transport Geography, Elsevier, vol. 80(C).
    8. 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.
    9. 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).
    10. 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.
    11. 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).
    12. Gonzalez-Calderon, Carlos A. & Moreno-Palacio, Diana Patricia & Posada-Henao, John Jairo & Quintero-Giraldo, Ricardo & Múnera, César Chavarría, 2022. "Service trip generation modeling in urban areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    13. 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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