A logit model for shipment size choice with latent classes – Empirical findings for Germany

Decisions on shipment size in freight transport are often seen to represent a whole set of logistics decisions made by shippers and recipients. Also, shipment sizes have a large impact on transport mode choice. Therefore, they are an important aspect in the modeling of freight transport demand, as they allow to display the reactions of various stakeholders on policy measures. In this article, a model for the discrete choice of shipment sizes is applied to interregional road freight transport. Preferences of actors are reflected by a total logistics cost expression. Furthermore, a Latent Class Analysis approach is applied to identify groups of transport cases with similar logistics requirements. The classification reduces significantly heterogeneity in behavior. Reactions of actors on external influences such as policy measures could be predicted more accurately

Building latent segments of goods to improve shipment size modelling: Confirmatory evidence from France

Freight transport demand models are generally based on administrative commodity type segmentation which are usually not tailored to behavioral freight transport demand modelling. Recent literature has explored new approaches to segment freight transport demand, notably based on latent class analysis, with promising results. In particular, empirical evidence from road freight transport modelling in Germany hints at the importance of conditioning and handling constraints as a sound basis for segmentation. However, this literature is currently sparse and based on small samples. Before it can be accepted that conditioning should be integrated in the state-of-the-art doctrine of freight data collection and model specification, more evidence is required. The objective of this article is to contribute to the issue. Using detailed data on shipments transported in France, a model of choice of shipment size with latent classes is estimated. The choice of shipment size is modelled as a process of total logistic cost minimization. Latent class analysis leverages the wide range of variables available in the dataset, to provide five categories of shipments which are both contrasted, internally homogenous, and directly usable to update freight collection protocols. The groups are: "‘standard temperature-controlled food products"’, "‘special transports"’, "‘bulk cargo"’, "‘miscellaneous standard cargo in bags"’, "‘palletised standard cargo"’. This segmentation is highly consistent with the empirical evidence from Germany and also leads to better estimates of shipment size choices than administrative segmentation. As a conclusion, the finding that conditioning and handling information is essential to understanding and modelling freight transport can be regarded as more robust

A discrete shipment size choice model with latent classes of shippers’ attributes.

Modelling the choice of shipment size is an important aspect in developing a freight transport model which also considers logistic choices. We developed a discrete shipment size choice model for road transports as a first step enabling a consistent conjunction between logistic choices and the discrete mode choice. Based on the categorization of the shipment sizes into three classes describing piece goods, partial loads and (multiple) full loads, the common Economic-Order-Quantity-model (EOQ-model) was validated for the discrete case. As there exist a huge variety of shippers the induced behavioral heterogeneity should be taken into account. Due to the insufficient explanatory power of the underlying industrial sectors and an inflation of the model in the statistical sense by adding the logistical attributes an attribute-based latent-class-analysis approach has been applied. Including latent classes improved the discrete choice model in comparison to the pure EOQ-Model as well as in comparison to the EOQ-model with shippers’ attributes

A machine learning approach for operationalization of latent classes in a discrete shipment size choice model

This paper elaborates a novel approach for implementation of latent segments concerning behaviorally sensitive shipment size choice in strategic interregional freight transport models. Discrete shipment size choice models are estimated for different homogenous segments formed by latent class analysis. A machine learning technique called Bayesian classifier is applied to link segments obtained from a sample to data of commodity flows being available on a national level. Finally, in an exemplary scenario, the impact of information and communication technologies on shipment size distributions is calculated, revealing moderate elasticities and a predominant substitution of less than truck loads by full truck loads

A discrete shipment size choice model with latent classes of shipments' attributes

Behavioral heterogeneity with respect to actors and shipments is a ubiquitous topic in the context of freight transport models. The necessity of consideration of these aspects and the challenges going along with the implementation into a freight transport model framework form a conflicting setting that needs to be analyzed in-depth in conjunction with solution approaches satisfying both aspects. In this study, we model the shipment size choice behavior by using a discretized total logistic costs approach as high-level logistics decision and account for the exceeding heterogeneity by forming segments of similarly behaving actors. This methodology enables the application in freight transport models as well as the analyses and prediction of policy impacts. We also give an outline how the latent segments can possibly be implemented into a German freight transport model

Transferability of models for logistics behaviors: A cross-country comparison between France and Germany for shipment size choice

Transferability is seen as one important rationale for justifying the application of behavioral transport demand models. Concerning freight transportation, behavioral models of shipment size choice gain more and more attention by modelers because of two reasons: First, shipment size models could explain a major proportion of heterogeneity caused by the multitude of actors and shipments; secondly, they link firms’ behaviors to their logistics activities. However, the transferability of prevalent shipment size choice models is complicated due to different functional approaches used in econometric estimates and due to the varying occurrence of different variables in the underlying surveys. This clearly limits the applicability of the models for other case studies with related contexts. In this article, the transferability of continuous shipment size choice models is investigated by applying the same functional approach to a dataset from France and an equivalent dataset for Germany. In this way, we check the robustness of this approach in regard to different logistics variables and we analyze potential similarities in logistics behavior. Starting with an analytical model for shipment size choice, a descriptive analysis follows which compares for both countries the central figures related to shipment size choice. Afterwards, elasticities gathered from the estimated models are checked against each other and the transferability issue is empirically questioned. Finally, possible reasons for differences in behavior, probably caused by differences in transport cost or in inventory cost, are discussed. It turns out, that the flow of goods exchanged between a shipper and its client explains a major proportion of heterogeneity in France and in Germany, and that the impact of this variable is very similar. Furthermore, differences in the storage costs approximated by the value density are obtained. Logistics variables have similar impacts; they can, however, be neglected in a strategical forecast model. Concluding, for the example France and Germany, the behavior models could be transferre