Abstract
Optimizing the performance of multimodal freight transport networks involves adequately balancing the interplay between costs, volumes, times of departure and arrival, and times of travel. In order to study this interplay, we propose an assignment model that is able to efficiently determine flows and costs in a multimodal network. The model is based on a so-called user equilibrium principle, which is at the basis of Dynamic Traffic Assignment problems. This principle takes into account transport demands to be shipped using vehicles that transport single freight units (such as trucks) or multiple freight units (such as trains and barges, where demand should be bundled to reach efficient operations). Given a particular demand, the proposed model provides an assignment of the demand over the available modes of transport. The outcome of the model, i.e., the equilibrium point, minimizes users’ generalized costs, expressed as a function of mode, travel time and related congestion, and waiting time for bundling sufficient demand in order to fill a vehicle. The model deals with these issues across a doubly-dynamic time scale and in an integrated manner. One dynamic involves a learning dynamic converging towards an equilibrium (day-to-day) situation, reflecting the reaction of the players towards the action of the others. Another dynamic considers the possible departure time that results in minimum expected costs, also due to the fact that players mutually influence each other on the choice of departure times, due to congestion effects and costs for early/late arrival of freight units. This is a choice within a given time horizon such as a day or a week. We present a study on the influence and sensitivity of different model parameters, in order to analyse the implications on strategic decisions, fostering a target modal share for freight transportation. We also study under which conditions the different modes can be substitutes for each other.
Highlights
Freight transport is an important building block of a supply chain, and a key process for reducing costs and environmental emissions for logistic systems
One time scale involves a learning dynamic over a long number of rounds; the other time scale involves a departure time choice within a given time horizon. The combination of these scales results in what can be called a within-day dynamic intertwined with a day-to-day equilibrium in Dynamic Traffic Assignment terms (Tampere et al 2010)
This paper proposes a Dynamic Traffic Assignment model for multimodal freight networks
Summary
Freight transport is an important building block of a supply chain, and a key process for reducing costs and environmental emissions for logistic systems. From a general perspective such approaches result in an effective way to reduce the number of vehicles on the road, contributing to relieving of networks from congestion, and in turn saving fuel and reducing pollution For this reason, policies are steadily discouraging truck distribution in favour of railway and waterway distribution, which are seen as more environmentally sustainable (European Commission 2001). In this study we develop a model that is able to study multimodal networks and determine factors leading to the given modal share, which might, for instance, identify policies that favour rail and barges to trucks To this end, we address the problem of assigning freight flows to multimodal freight transport networks.
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