Entropy-based freight tour synthesis and the role of traffic count sampling

This paper describes a Freight Tour Synthesis (FTS) model designed to infer aggregate pick-up/delivery tour flows using secondary data, such as traffic counts and zonal freight trip generation estimates. The formulation combines an entropy maximization demand model together with the secondary data c...

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Bibliographic Details
Published in:Transportation research. Part E, Logistics and transportation review Logistics and transportation review, 2019-01, Vol.121, p.63-83
Main Authors: Gonzalez-Calderon, Carlos A., Holguín-Veras, José
Format: Article
Language:English
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Summary:This paper describes a Freight Tour Synthesis (FTS) model designed to infer aggregate pick-up/delivery tour flows using secondary data, such as traffic counts and zonal freight trip generation estimates. The formulation combines an entropy maximization demand model together with the secondary data constraints. The entropy function is maximized subject to the system constraints to estimate the most likely freight tours that best fit the secondary data. To assess the role of traffic counts, the authors design four different heuristics to identify the locations of the traffic counts to be used in the estimation, and assess their performance under different scenarios of traffic counts availability.
ISSN:1366-5545
1878-5794
DOI:10.1016/j.tre.2017.10.010