Abstract

Urban consolidation centers (UCCs) have a key role in many initiatives in urban logistics, yet few of them are successful in the long run. The high costs have proven a barrier that prevents attracting a sufficient high number of UCC users. In this paper, we study how the user base of a UCC develops under a variety of administrative policies. We perform an agent-based simulation applied to the city of Copenhagen, making use of its real street network and retailer locations. We collect data from a variety of sources to help modeling the agents. Both the data and case setup are validated by means of expert interviews. We test 1,458 schemes that combine several administrative measures and cost settings. The numerical results indicate that most schemes yield significant environmental benefits; many of them are able to reduce the truck kilometers driven by about 65% and emissions by about 70%. The key challenge isto identify schemes that are also financially sustainable. We show that it is essential for the UCC to ensure the commitment of carriers as soon as possible, as the bulk of the revenue can be generated from this target group. Subsequent revenues may be generated by offering value-adding services to receivers. Based on the numerical experiments, we pose various propositions that aid in providing favorable conditions for a UCC, improving its chances of long-term success. Document type: Article

Abstract

Traffic congestion is an issue regarding the vitality of cities and the welfare of citizens. Transportation systems are using various technologies to allow users to adapt and make different decisions towards transportation modes. Modification and improvement of these systems affect the commuters' perspective and social welfare. In this study, the effect of road flow equilibrium on commuters' utilities with different types of transportation modes will be discussed. A simple network with two modes of transportation will be illustrated and three different cost policies were considered to test the efficiency of reinforcement learning in commuters' daily trip decision-making regarding time and mode. The artificial society of agents is simulated to analyse the results. info:eu-repo/semantics/publishedVersion