Incorporating Traffic Management Information into Logistics Planning

Abstract

Today, logistics and traffic management are two important elements for the urban population. Logistic service providers enable the availability of consumer goods to city residents and the traffic management optimizes traffic flows of the city by operating the traffic infrastructure (i.e. traffic signals). This dissertation investigates if logistics and traffic management can achieve a mutual benefit through an information sharing cooperation. The main motivation for a logistics company, which operates in the high cost pressure delivery industry, is a possible reduction of operational costs for their delivery fleet. The motivation of the traffic management to enter a cooperation is a possible reduction of the high traffic burden induced by freight vehicles. In such a cooperation the traffic management could transfer information about the state of the operable traffic infrastructure, and therefore their related current travel time impact, to the logistics company. With this information the logistics company can optimize their delivery tours. This dissertation assesses the benefits of cooperation by analyzing how the delivery tours can be improved and how the freight traffic burden can be reduced. This question is investigated in a case study of an environmental-sensitive traffic management system in the city of Braunschweig, Germany and a city logistics service provider. The air pollution in Braunschweig does not comply with the EU air pollution limit. If the air pollution is high in a hot-spot area, the traffic management changes the traffic infrastructure to reduce the traffic flow into the hot-spot areas, which affects the surrounding traffic situation. In the case study, the degree of information from the traffic management and the tasks of the logistics company are varied and formulated in different dynamic Vehicle Routing Problems. The evaluation of the case study shows that a mutual benefit of the cooperation can be discovered over all variations of the experiments.