Conference Agenda

The continued transition towards electric mobility will lead to a substantial decrease in energy tax revenue worldwide, which has implications for government funds. At the same time, demand for transportation is ever increasing, which in turn increases congestion problems. Combining both challenges, this paper assesses the effectiveness of congestion pricing as a substitute for fuel taxes under the assumptions of a dynamic diffusion of electric vehicles. A congestion-based toll that is road-and-time-variant was simulated for the Berlin-Brandenburg region in Germany in 2030 using MATSim. This paper quantifies the impacts of the toll on the governmental revenue, traffic management, environment, social welfare, and the distribution effects. We find that the revenue from a congestion toll can compensate the reduction in passenger car fuel tax. Furthermore, a remarkable welfare surplus is observed, especially when the congestion pricing is coupled with investing in upgrading the roads and subsidising the rails. The toll also shows success in incentivising transport users to adjust their travel behaviour, which reduces traffic delay time by 28–45%. CO2 emissions as a key metric for decarbonisation of the transport sector decreased substantially. The analysis on the distribution effects suggests that a redistribution plan with a focus on the middle-low-income residents and the outer boroughs could help the policy gain more public acceptance.

This paper discusses the effect of road pricing on the spatial distribution of traffic flow. The traffic flow density is derived for a circular city with a radial-arc network. The traffic flow density describes the amount of traffic as a function of position and allows us to identify the location of potential congestion areas. The analytical expression for the traffic flow density demonstrates how the size of the toll area and the toll level affect the spatial distribution of traffic flow. As the size of the toll area increases, the decrease in traffic flow inside the toll area becomes smaller. As the toll level increases, the increase in traffic flow at the boundary of the toll area becomes greater. The effect of the travel cost on the spatial distribution of traffic flow is also examined. These findings can be used to determine the size of the toll area and the toll level required to achieve a certain level of traffic congestion.

For the cost-efficient supply of stores, it is common in retail practice to rely on third-party logistics providers (3PL) instead of their own fleet. A 3PL carries out the retailers' delivery tours and commonly bills according to a zone-based tariff. This tariff assigns each store to one zone that reflects the travel distances for the delivery. Stores further from the depot are assigned to higher zones. The cost of a tour depends on the furthest zone visited and the volume, subject to discounts. Additionally, 3PLs limit the detour of a tour to ensure economic feasibility. The detour limitation prevents excessive driving within the zones visited. While using 3PLs and their zone tariffs reduces the complexity for retailers, the question arises of how retailers can plan cost-minimal tours that are economical for 3PLs. We address this issue and formalize the problem as a Capacitated Vehicle Routing Problem with a Zone Tariff. The nonlinear tariff and the non-monotonically increasing detour drive the complexity. We provide the first mixed-integer formulation (MIP) for the problem, which we strengthen with valid inequalities. We develop a Branch-and-Check (BAC) decomposition algorithm to solve larger instances and improve it with problem-specific acceleration techniques. In our numerical experiments, we solve a real-world case and adapted instances from the literature. The BAC approach reduces the runtime compared to the MIP by up to three orders of magnitude in selected cases.

Urban transportation systems are increasingly overloaded by growing populations, which intensify congestion and subsequently lead to user dissatisfaction, environmental pollution, and health hazards. These challenges highlight the urgent need for more sustainable and efficient mobility solutions. Intermodal autonomous mobility-on-demand systems offer a promising solution to alleviate urban congestion by combining the broad coverage of public transit with the flexibility of on-demand services. Against this background, we study an integer minimum-cost multi-commodity network flow problem with a nonlinear congestion function. We propose an algorithmic framework to route large-scale passenger flows, aiming to determine the system optimum of an intermodal transportation network. This framework employs a layered, time-expanded graph with each layer representing a different transportation mode, and a linearized version of a nonlinear congestion function in the on-demand layer. This structure allows us to apply a column generation approach to derive optimal solutions for the linearized continuous problem and then utilizes a branch-and-price method to obtain integer solutions. Additionally, we develop a rapid rounding algorithm as a faster alternative to the branch-and-price method, aiming to enhance computational efficiency. We validate the performance of our algorithmic framework on a comprehensive urban mobility dataset from Munich, where we solve instances with up to 20618 passengers to optimality. Compared to the branch-and-price method, our rapid rounding algorithm can not only find near-optimal solutions but is significantly faster while ensuring all capacity constraints are satisfied.