Joint estimation of freight transportation decisions under nonrandom sampling

McFadden, Daniel; Winston, Clifford; Boersch-Supan, Axel

doi:10.1017/cbo9780511895913.007

Cited by 56 publications

(46 citation statements)

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“…Selectivity bias can be accounted for in this usage model by linking the model to a discrete type-choice model, and adding into the structural equation system a correction term variable involving a transformation of the household's predicted type vehicle choice 22 probabilities (McFadden et al, 1985;Mannering and Winston, 1985;Train, 1986;Hensher, et al, 1992). It is doubtful that such a correction term would have any pronounced effect on the results.…”

Section: Estimation Methodologymentioning

confidence: 99%

“…If the error terms of the discrete vehicle type choice model and the vehicle usage model are correlated, the parameter estimates will be biased. A linear correction term involving a transformation of predicted vehicle type choice probabilities can be applied to the usage model to account for self selectivity bias (McFadden et al, 1985;Mannering and Winston, 1985;Train, 1986;Hensher, et al, 1992). Empirically, however, the selectivity corrections applied in utilization models to account for endogeneity bias have not had substantial effects on estimation results (Train, 1986;Hensher, 1992).…”

Section: Introductionmentioning

confidence: 99%

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How households use different types of vehicles: A structural driver allocation and usage model

Kim

Ren

1996

Transportation Research Part A: Policy and Practice

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The vehicle miles of travel for each vehicle in multi-vehicle households is modeled as a function of household characteristics, vehicle characteristics, and the matches of vehicle to driver in the satisfaction of travel desires. A structural equations model is developed in which principal driver characteristics, as well as vehicle miles of travel, are endogenous. There are links between how each vehicle is used and who in the household is each vehicle's principal driver. Each vehicle's usage can then be expressed in reduced-form equations as a function of exogenous household and vehicle type variables for forecasting purposes. The model is estimated on a 1993 sample of approximately 2000 multi-vehicle households in California.2

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Section: Estimation Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How households use different types of vehicles: A structural driver allocation and usage model

Kim

Ren

1996

Transportation Research Part A: Policy and Practice

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“…Disaggregate joint shipment size and mode choice models have been around since McFadden et al (1985), as pointed out in Regan and Garrido (2001). The model developed by de Jong and Ben-Akiva (2007) expands on the mode choice aspect into a set of commodity distribution chain choices.…”

Section: Joint Shipment Size and Transport Chain Choice Modelmentioning

confidence: 99%

State-of-the art of freight forecast modeling: lessons learned and the road ahead

2010

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In this paper we review freight forecasting models and current advances and needs with respect to data and model development. We then present a case study to suggest which models should be developed for the State of California in the US. We suggest several alternatives including an aggregate commodity flow model, a disaggregate regional logistics model and a hybrid regional logistics model with a truck touring model. We point out however, that the data requirements for the latter model would be extensive. In addition, the development of hybrid models, for example progress in the integration of regional logistics models with urban truck touring models, will introduce new problems such as reconciling the outputs of multiple models for consistency.

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“…It is well-documented in the literature that shipment size determines the choice of mode/vehicle and vice versa (see for example, McFadden et al 1986;Inaba and Wallace, 1989;Abdelwahab and Sargious, 1992;Holguin-Veras 2002;Johnson and de Jong, 2011). In addition to recognizing this simultaneous decision process, these studies show that various haul, carrier, and commodity characteristics are important factors that affect the decision on the optimal shipment size and vehicle size.…”

Section: Introductionmentioning

confidence: 99%

The optimal shipment size and truck size choice – The allocation of trucks across hauls

Abate

Jong

2014

Transportation Research Part A: Policy and Practice

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There has been a growing interest in understanding how firms allocate their trucks across hauls, and how this allocation changes under various economic environments. This study investigates how variations in route/haul, carrier and vehicle characteristics affect the optimal vehicle size choice and the associated choice of shipment size. We show that the two choices are derived from the same optimization problem. There can be a continuum of shipment sizes, but decision-makers in freight transport have to choose from a limited number of vehicle alternatives. Therefore, we use a discrete-continuous econometric model where shipment size is modeled as a continuous variable, and vehicle size/type choice as a discrete variable. The results indicate that when faced with higher demand, and during longer trips firms are more likely to use heavier vehicles and ship in larger quantities which suggest that firms are realizing economies of scale and economies of distance. The study also discusses the effect of vehicle operating cost on the vehicle selection process and its policy implications.* We are very grateful to Ole Kveiborg and Bertel Schjerning for helpful comments.

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Joint estimation of freight transportation decisions under nonrandom sampling

Cited by 56 publications

References 0 publications

How households use different types of vehicles: A structural driver allocation and usage model

How households use different types of vehicles: A structural driver allocation and usage model

State-of-the art of freight forecast modeling: lessons learned and the road ahead

The optimal shipment size and truck size choice – The allocation of trucks across hauls

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