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

The turnaround process constitutes an important part of the air transportation system. Airports often represent bottlenecks in air traffic management (ATM), thus operations related to the preparation of the aircraft for the next flight leg have to be executed smoothly and in a timely manner. The ATM significantly depends on a reliable turnaround process. Future paradigm changes with respect to airplane energy sources, aircraft design or propulsion concepts will also influence the airport layout. As a consequence, operational processes associated with the turnaround will be affected. Airlines aim for efficient and timely turnaround operations that are correlated with higher profits. This case study discusses an approach to investigate a new aircraft design with respect to the implications on the turnaround. The boarding process, as part of the turnaround, serves as an example to evaluate the consequences of new design concepts. This study is part of an interdisciplinary research to investigate future energy, propulsion and designs concepts and their implications on the whole ATM system. Due to these new concepts, several processes of the turnaround will be affected. For example, new energy storage concepts will influence the fueling process on the aircraft itself or might lead to a new infrastructure at the airport. This paper aims to evaluate the applied methodology in the case of a new boarding process, due to a new aircraft design, by means of a generic example. An agent-based boarding simulation is applied to assess passenger behavior during boarding, particularly with regard to cabin layout and seat configuration. The results of the generic boarding simulation are integrated into a simplified, deterministic and generic simulation of the turnaround process. This was done to assess the proposed framework for future investigations which on the one hand address the ATM system holistically and on the other, incorporate additional or adapted processes of the turnaround. 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

Abstract

This work describes the development of an agent-based simulation that is an Adoption Dynamics Analysis Model for Electric Vehicles (ADAM & EV). ADAM & EV supports stakeholders in understanding the electric mobility landscape and the possible effects on policies. The model takes a consumer perspective by focusing in-depth on the consumer decision making process following the Theory of Planned Behaviour. In this work, we discuss the structure and interface of the model, and shortly discuss a few initial outcomes such as the consequence that stimulating BEVs increases emissions in the short-term when taking into account manufacturing emissions.

Abstract

The domain of urban freight transport is becoming increasingly complex. Many urban supply chains are composed of small and independent actors that cannot efficiently organize their highly fragmented supply chains, thereby negatively affecting the quality of life in urban areas. Both companies and local administrators try to improve transport efficiency and reduce external costs, but the effects of such interventions are difficult to predict, especially when applied in combination with each other (an urban logistics scheme). This paper presents an agent-based simulation model to quantify the effects of urban logistics schemes on multiple actors. We provide a detailed mathematical representation in the form of a Markov decision process. Based on an extensive literature study, we aggregate data to represent various actors in typical Western European cities. We perform numerical experiments to obtain insights into urban logistics schemes. The results show that most schemes yield significant environmental improvements but that achieving long-term financial viability is challenging for urban consolidation centers in particular. We also demonstrate that interventions, such as subsidies and access restrictions, do not always yield the intended effect. In a backcasting experiment, we identify conditions and schemes to achieve a financially viable urban consolidation center.

Abstract

Electric vehicles offer the potential of climate-friendly driving and have received more and more attention in diffusion research recently. However, the effect of the availability of home charging options on the market potential of electric vehicles has not been targeted in detail in the diffusion process so far, although it represents a beneficial differentiation to conventional vehicles. Therefore, we present an agent-based simulation to address the buying behavior of German consumers regarding electric and plug-in hybrid electric vehicles using empirical data primarily derived from a choice-based conjoint study. We explicitly consider individual possibilities of home charging in our model, which appears to have an important influence on the diffusion of both, electric and plug-in hybrid electric vehicles. However, its importance decreases with faster charging times at public charging stations. Furthermore, our findings reveal that in the longer term, technological progress in charging time, range, and charging station density of electric vehicles is presumably cannibalizing plug-in hybrid electric vehicles’ market shares more than that of conventional vehicles. However, with still lower technological capabilities of electric vehicles, a governmental subsidy can initially promote plug-in hybrid electric vehicles, but electric vehicles will benefit later from that promotional effect.

Abstract

Traffic congestion, especially during peak hours, has become a challenge for transportation systems in many metropolitan areas, and such congestion causes delays and negative effects for passengers. Many studies have examined the prediction of congestion however, these studies focus mainly on road traffic, and subway transit, which is the main form of transportation in densely populated cities, such as Tokyo, Paris, and Beijing and Shenzhen in China, has seldom been examined. This study takes Shenzhen as a case study for predicting congestion in a subway system during peak hours and proposes a hybrid method that combines a static traffic assignment model with an agent-based dynamic traffic simulation model to estimate recurrent congestion in this subway system. The homes and work places of the residents in this city are collected and taken to represent the traffic demand for the subway system of Shenzhen. An origin-destination (OD) matrix derived from the data is used as an input in this method of predicting traffic, and the traffic congestion is presented in simulations. To evaluate the predictions, data on the congestion condition of subway segments that are released daily by the Shenzhen metro operation microblog are used as a reference, and a comparative analysis indicates the appropriateness of the proposed method. This study could be taken as an example for similar studies that model subway traffic in other cities. Document type: Article