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Spatial interaction between the truck flows through the Mexico-Texas border

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  • Garrido, Rodrigo A.

Abstract

This paper presents an analysis of the spatial and temporal interactions between truck flows across the Texas-Mexico border. The system of flows is modeled as a multivariate stochastic process with interactions both in time and space dimensions. A novel modeling approach, the space time autoregressive moving average (STARMA) model, is applied to capture the autocorrelations between the flows. These autocorrelations are the base to compute direct and crossed demand elasticities between the flows. The demand elasticities are used to estimate the degree of dependence between the truck flows both southbound and northbound, revealing that uneven adjustments take place in the truck flows pattern for southbound and northbound traffic. In fact, the southbound flows are more sensitive to changes in their own demand over time than the corresponding spatial changes. On the other hand, the northbound flows are more sensitive to changes in the demand for the main flow (Laredo's bridge) rather than their own variations in previous periods.

Suggested Citation

  • Garrido, Rodrigo A., 2000. "Spatial interaction between the truck flows through the Mexico-Texas border," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(1), pages 23-33, January.
    • Handle: RePEc:eee:transa:v:34:y:2000:i:1:p:23-33
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    Cited by:

    1. Parent, Olivier & LeSage, James P., 2010. "A spatial dynamic panel model with random effects applied to commuting times," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 633-645, June.
    2. Svetlana Borovkova & Hendrik P. Lopuhaä & Budi Nurani Ruchjana, 2008. "Consistency and asymptotic normality of least squares estimators in generalized STAR models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(4), pages 482-508, November.
    3. Dhulipala, Sowjanya & Patil, Gopal R., 2020. "Freight production of agricultural commodities in India using multiple linear regression and generalized additive modelling," Transport Policy, Elsevier, vol. 97(C), pages 245-258.
    4. Reis, Vasco, 2014. "Analysis of mode choice variables in short-distance intermodal freight transport using an agent-based model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 100-120.
    5. Zhao, Yuan & Hao, Li-Sha & Wan, Lu, 2007. "Research on the spatial structure of crude oil flow and the characteristics of its flow field in China," Energy Policy, Elsevier, vol. 35(10), pages 5035-5050, October.
    6. Edoardo Marcucci, 2013. "Logistics Managers' Stated Preferences For Freight Service Attributes: A Comparative Research Method Analysis," Working Papers 0213, CREI Università degli Studi Roma Tre, revised 2013.
    7. Hensher, David A. & Teye, Collins, 2019. "Commodity interaction in freight movement models for New South Wales," Journal of Transport Geography, Elsevier, vol. 80(C).

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