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As urbanization increases, municipalities across the world have become aware of the negative impacts of road-based transportation, which include traffic congestion and air pollution. As a result, several cities have introduced tolling schemes to discourage vehicles from entering the inner city. However, little research has been done to examine the impact of tolling schemes on the routing of commercial fleets, especially on the resulting costs and emissions. In this study, we investigate a vehicle routing problem considering different congestion charge schemes for several city types. We design comprehensive computational experiments to investigate whether different types of tolling schemes work in the way municipalities expect and what factors affect the performance of the congestion charge schemes. We compare the impact on a company's total costs, fuel usage (which drives emissions), and delivery tour plans. Our experimental results demonstrate that some congestion pricing schemes may even increase the emissions in the city center, and higher congestion charges may not necessarily lead to lower emissions., Working paper series, Band 2018, Heft 8

Throughout the last decade, the advent of novel mobility services such as ride-hailing, car-sharing, and ride-sharing has shaped urban mobility. While these types of services offer flexible on-demand transportation for customers, they may also increase the load on the, already strained, road infrastructure and exacerbate traffic congestion problems. One potential way to remedy this problem is the increased usage of dynamic ride-sharing services. In this type of service, multiple customer trips are combined into share a vehicle simultaneously. This leads to more efficient vehicle utilization, reduced prices for customers, and less traffic congestion at the cost of slight delays compared to direct transportation in ride-hailing services. In this thesis, we consider the planning and operation of such dynamic ride-sharing services. We present a wider look at the planning context of dynamic ride-sharing and discuss planning problems on the strategical, tactical, and operational level. Subsequently, our focus is on two operational planning problems: dynamic vehicle routing, and idle vehicle repositioning. Regarding vehicle routing, we introduce the vehicle routing problem for dynamic ridesharing and present a solution procedure. Our algorithmic approach consists of two phases: a fast insertion heuristic, and a local search improvement phase. The former handles incoming trip requests and quickly assigns them to suitable vehicles while the latter is responsible for continuously improving the current routing plan. This way, we enable fast response times for customers while simultaneously effectively utilizing available computational resources. Concerning the idle vehicle repositioning problem, we propose a mathematical model that takes repositioning decisions and adequately reflects available vehicle resources as well as a forecast of the upcoming trip request demand. This model is embedded into a real-time planning algorithm that regularly re-optimizes the movement of idle vehicles. Through an adaptive parameter calculation process, our algorithm dynamically adapts to changes in the current system state. To evaluate our algorithms, we present a modular simulation-based evaluation framework. We envision that this framework may also be used by other researchers and developers. In this thesis, we perform computational evaluations on a variety of scenarios based on real-world data from Chengdu, New York City, and Hamburg. The computational results show that we are able to produce high-quality solutions in real-time, enabling the usage in high-demand settings. In addition, our algorithms perform robustly in a variety of settings and are quickly adapted to new application settings, such as the deployment in a new city.