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Modal choice models applied to interregional or international freight transportation network models are often based on rather coarse origin-destination matrices, containing annual transported tonnages between (sub)regions, for instance. Generally, only basic (sometimes constructed) independent variables (transportation costs or transit times) are used because other variables such as shipment sizes, service frequencies, etc. are not available. Using origin-destination matrices and an assignment model, it is also possible to compute spatial accessibility measures that can further be used as additional explanatory variables. Indeed, several published studies have identified network accessibility as an important element in the mode-choice decision. This paper also shows that the inclusion of an accessibility measure in the utility functions of a logit model substantially improves the performance of a transportation network model, both in the modal choice and the assignment levels of the classical four-step model. Consequently, the assignment of the estimated modal demands results in more accurate estimated traffic on the networks. The model presented in this paper is to be considered as a proof of concept because its workflow should further be streamlined to make it easily useable by modelers.

Modal choice models for strategic freight transportation studies covering large inter-regional or international areas generally rely on basic explanatory variables such as transportation costs and transit times. Using origin-destination matrixes, it is also possible to compute an accessibility measure that can further be used as an additional explanatory variable. This paper shows that the inclusion of an accessibility measure in the utility functions used for a logit model significantly improves its predictive power. Moreover, when the refined model is used to compute cost and transit time elasticities, the obtained (absolute) values are somewhat lower. The use of an accessibility measure in the modal choice model has thus a double advantage for policymakers: it improves the predictive power of the freight transport model, giving more accurate traffics on the modal networks, and it avoids overestimations of own and cross-elasticities.

Modal choice models used in the context of strategic freight transportation studies are generally difficult to set up because of lack of explanatory data. Costs, transit times and trip lengths are often the only figures that can be gathered. While cost and transit time are known as being very important in mode choice decisions, the estimators computed for these variables used in a logit model can be nonsignificant or even have unexpected signs. In this context, Box-Cox transformations of the independent variables can help. Used to improve the log-likelihood of the estimated model, this technique also has the side effect to correct the unexpected signs of the estimators. However, the estimation of the transform parameters can be tricky. Mixing econometrics and operational research, this paper presents a specific shotgun hill climbing meta-heuristic with backtracking capabilities, able to quickly identify Box-Cox  parameters to use when multiple variables must be transformed.

Modal choice models used for freight transportation studies covering inter-regional or international areas are difficult to set up because of the dearth of information about explanatory factors. While cost and transit time are known as being important explanatory variables, they are generally correlated to each other, and their coefficient computed with a Logit model can have unexpected signs. Box-Cox transformations (BCT) of the independent variables can help to overcome this problem. If solutions to identify the BCT parameter  that maximises the likelihood of a model are well known, the process is not straightforward once it must respect the constraints that the variables’ coefficient estimators take the expected signs. This paper presents a shotgun hill climbing meta-heuristic with backtracking capabilities, able to quickly identify Box-Cox  parameters to use when multiple variables must be transformed. The algorithm appears to be efficient and effective and produces stable and statistically valid solutions.

This Ph.D. thesis aims at assessing the benefits of horizontal cooperation in logistics and at analyzing how these benefits depend on the markets and partners characteristics. In the past recent years, researchers have presented horizontal cooperation as an efficient and unique way to reduce costs (e.g., transportation, inventory, facility, stock-out) and CO2 emissions. The literature on horizontal cooperation demonstrates a high variation in benefits related to the collaboration, regarding the markets and the partners characteristics as they impact the design of the supply chain network. These benefits are therefore difficult to predict, which can be a significant obstacle for companies considering forming a cooperation. In order to promote horizontal cooperation to companies, it was thus crucial to assess these benefits for each partner. Moreover, while horizontal cooperation has mainly been analyzed at an operational level with transportation decisions in the current literature, the present thesis examines horizontal cooperation at tactical and strategic levels, using location-inventory models to decide the number and location of facilities, the delivery network and the inventory decisions. From different sets of experiments, we present useful insights to companies wishing to collaborate. This Ph.D. thesis also provides valuable tools to select the more favorable partners and to define markets characteristics that should motivate companies to collaborate. Finally, we integrate companies’ individual expectations in our models to understand why some companies leave a collaboration and, therefore, try to improve the long-term stability of collaboration., (ECGE - Sciences économiques et de gestion) -- UCL, 2021

The paper presents a set of freight demand elasticities for road, inland waterways and rail transport with respect to a change of total cost of transport, transit time and speed. They can be used in the context of transport policy decisions for estimating the impacts of changes in the cost of transport or the impacts of new infrastructure on traffic or on the modal split for instance. The calculations are based on origin-destination matrixes and networks made available by ETISPlus, a European transport policy information system. The transportation costs and transit times are computed, for each origin-destination relation, each group of commodities and each transportation mode of interest using a transportation network model. A Box-Cox approach transform the explanatory variables for an aggregated conditional logit analysis. If the estimation and validation steps of the models receive a special attention, the most important added value of this paper is probably the in-depth analysis and interpretation of the estimated parameters, and more specifically the optimal  values used for the Box-Cox transformations. Own and cross elasticities are calculated for costs and transit time changes. All these values are computed at the trans-European level, but also for a large region around the Benelux countries, where there is more competition between the three modes. For this region, the impact of the geographical aggregation level of the O-D matrix (NUTS-2 vs NUTS-3) is also examined. Beside these classical analytical computations, the network model makes it also possible to compute arc elasticities. The obtained values are quasi-identical to the former, but this method allows the computation of ‘composite’ elasticities, which estimate the impact of a variation of a component present in more than one explanatory variables, such as travel speed, that influences both costs and transit times.

En parodiant le titre d’un célèbre essai de Jacques Monod (1971), cet article tente de synthétiser un certain nombre de travaux de recherche qui, ces dernières années, ont examiné la pertinence d’ouvrir (ou non) de nouveaux terminaux à conteneurs sur le territoire belge. Ces terminaux sont des éléments essentiels dans un réseau de transport intermodal, mais ils ont un coût. Pour être rentable, le coût additionnel engendré par les transbordements doit être compensé par un gain dans les coûts de transport. Or, rien ne garantit a priori que l’opération soit rentable, alors que l’ouverture de nouveaux terminaux est très régulièrement présentée comme une opportunité incontournable dans les discours politiques préélectoraux…

Land use and transport interactions (LUTI) models are widely employed by planners, to forecast future urban patterns and to test the influence of land use or transport policies. This thesis examines their sensitivity to two types of spatial bias, the choice of the study area (“spatial extent”) and of the areal units (“spatial resolution”). The first part of this thesis focuses on econometric components inside LUTI models. We propose two extensions to the literature on the Modifiable Areal Unit Problem (MAUP). First, the sensitivity of regression models to spatial extent is examined. Secondly, the influence of the spatial resolution on discrete choice models, used by most state-of-the-art LUTI models to forecast location choices of agents, is assessed. Significant variations are found in parameter estimates, leading to changes in the behaviour of the model. The second part of the thesis assesses the influence of these spatial biases on LUTI models’ outputs. Two applications of the UrbanSim model are developed. We rely on a synthetic case study to examine the sensitivity of the final situation predicted by the model. Then, a model of the urban region of Brussels is used to explore the implications of this sensitivity for policy evaluation. The results show that variations induced by spatial bias on LUTI models’ outputs are larger than those due to transport or land use scenarios, and that they affect the estimation of the sustainability of these scenarios. The last chapter offers recommendations to reduce the sensitivity of LUTI models to these spatial biases, consisting in the adoption of “best spatial practices” and of potential technical development, together with alternative approach of increasing the applicability of LUTI models., (SC - Sciences) -- UCL, 2016

Strategic transportation network models are often used as support tools in the framework of decisions to be taken at the policy level, such as the Trans-European Network projects. These models are mostly setup using aggregated or limited data. If their calibration is regularly mentioned in the literature, their validation is barely discussed. In this paper, several modal choice model specifications that make only use of explanatory variables available at the network level are described and applied to a large scale case. A validation exercise is performed at three levels of aggregation. The paper is designed from a strategic transport planning perspective, and does not present new modal choice formulations or assignment procedures. Its main added value is the focus on calibration and validation considerations. Despite the limited explanatory information used, the global performance of the best models can be considered as satisfactory. However, the quality of the models varies from mode to mode, the use of railway transport being the most difficult to predict without more specific input.

Elasticities for freight transport in the context of mode choice are hardly available for markets in which intermodal container transport competes with truck only transport. These elasticities are expected to be different, however, from values found in the literature for traditional freight transport, as trucking is a complement to rail or inland waterway transport when used for pre- or post-haulage, but a substitute to these modes when used from the origin to the final destination. This paper presents direct and cross (generalized) cost elasticities for road and rail transport demand for continental intermodal container transport and constitutes an attempt to compare the elasticities for intermodal transport with those for general freight transport. We first look into the sensitivity of elasticities to the total length of the haul and to pre- and post-haulage distances by road using a stylized, theoretical model. The assumption about pre- and post-haulage distances in mode choice appears to be important, as elasticities for road transport in the context of intermodal transport chains can be half of the values currently used in a conventional mode choice situation without access or egress by road. Next we provide empirical values for the market of container transport using a European multimodal network model. The absolute values of the estimated elasticities follow a double trend when the cost for trucking is modified: they increase with the distance between the origin and the destination, but decrease with the length of pre- or post-haulage. In all cases, the values are estimated to be inelastic. Overall, the “complement” component of the computed elasticities for rail-road transport is estimated to be approximately 20% of the total impact.

The paper presents a detailed comparative review of price/cost elasticity estimates published in a number of studies on multi-mode freight transport demands. It attempts to determine which factors could explain the wide diversity of estimates: data aggregation, diversity of markets, and methodology. It also presents new estimates for rail, road, and waterway modes, derived from a multimodal freight network model of the Rhine area market. Direct and cross-elasticities are estimated for 11 groups of commodities and per distance category. The results are critically assessed by comparison with the reviewed studies. The paper concludes with a few recommendations about meaningful uses of existing estimates and the need for additional experiments with different methodologies applied on a common data basis.

This thesis aims at developing an agent-based micro-simulation framework for (large) population evolution and mobility behaviour. More specifically we focus on the agents generation and the traffic simulation parts of the platform, and its application to Belgium. Hence we firstly develop a synthetic population generator whose main characteristics are its sample-free nature, its ability to cope with moderate data inconsistencies and different levels of aggregation. We then generate the traffic demand forecasting with a stochastic and flexible activity-based model relying on weak data requirements. Finally, a traffic simulation is completed by considering the assignment of the generated demand on the road network. We give the initial developments of a strategic agent-based alternative to the conventional simulation-based dynamic traffic assignment models., (DOCSC00) -- FUNDP, 2014