Freight Transport Modelling 2013
DOI: 10.1108/9781781902868-010
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Endogenous Shipment Size in Freight Mode Choice Models: Theory and Empirical Testing

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Cited by 3 publications
(4 citation statements)
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“…This is in line with several empirical analyses (Liedtke, 2012;McCann, 2001). Possible explanations for this effect are the less than proportional fuel/time costs for larger vehicles (Abate and de Jong, 2014), a higher unreliability of transports with bigger haulage distance (implying higher inventories) (Piendl et al, 2017) or the decoupling of production locations and of regional retail centres by regional distribution centres (Combes, 2009). The two variables related to the storage costs have negative effects.…”
Section: Discrete Shipment Size Modelsupporting
confidence: 81%
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“…This is in line with several empirical analyses (Liedtke, 2012;McCann, 2001). Possible explanations for this effect are the less than proportional fuel/time costs for larger vehicles (Abate and de Jong, 2014), a higher unreliability of transports with bigger haulage distance (implying higher inventories) (Piendl et al, 2017) or the decoupling of production locations and of regional retail centres by regional distribution centres (Combes, 2009). The two variables related to the storage costs have negative effects.…”
Section: Discrete Shipment Size Modelsupporting
confidence: 81%
“…In line with other authors (e.g. Abate and de Jong (2014); Combes (2009); Holguín-Veras ( 2002)), we also treat shipment size as a continuous variable. Doing so, we find very similar results: All coefficients are significant, and their signs are consistent with those shown in Table 7, especially for the volume density whose consideration represents a novelty with respect to the previous literature.…”
Section: Robustness Testsmentioning
confidence: 97%
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“…For example, there is no freight equivalent to the theory of the allocation of time of Becker (1965), which explains why passengers have a willingness to pay for travel time savings. A number of recent works, mainly inspired by inventory theory, are progressively bridging this gap, such as Combes (2010) which analyses a classic model describing a just-in-time supply chain (Arrow et al, 1958) from a microeconomic perspective, in order to identify the logistics drivers of shippers' value of time.…”
Section: Introductionmentioning
confidence: 99%