Conference Agenda

Electric vehicle (EV) penetration in public bus and shuttle systems is rising due to EVs' favorable tail-pipe emissions. However, conventional conductive charging limits vehicle utilization and requires drivers to leave the vehicle to connect to the grid. Inductive charging presents a solution to these challenges by allowing charging while driving and eliminating the need for drivers. Against this background, we study the stationary and dynamic inductive charging segment location problem for electric shuttle fleets within the scope of the project, MILAS, that deploys such shuttles accessible to the public in Bad Staffelstein, Germany. We assume fixed public transportation stop sequences and timetables, and determine cost-optimal charging segment configurations with respect to energy demand and construction costs, while avoiding conflicting stationary charging operations. We present an iterative local search with a structured perturbation procedure to keep the number of explored configurations tractable. We solve the resulting subproblems as resource-constrained shortest path problems via an efficient implementation of a label setting algorithm Herein, we check dominance in a lazy fashion and accelerate the dominance check by maintaining and exploiting local bounds. We outperform a commercial solver on a set of benchmark instances. Furthermore, we study the value of decentral energy storage leading to time-dependent energy prices with high volatility and investigate the value of shared charging segments that require deviations from some shuttles' shortest paths for the network in Bad Staffelstein. We present results for the actual project scenario in which two shuttles are deployed and for scenarios with larger fleets.

Autonomous mobility-on-demand (AMoD) systems offer a solution to urban congestion through a centrally controlled fleet of autonomous vehicles that provide door-to-door mobility services. By replacing individual human-driven vehicles with a centrally coordinated autonomous fleet, AMoD systems can enhance traffic flow following two strategies: balanced routing and staggered routing.

Balanced routing distributes AMoD traffic across alternative road segments of the street network to spatially even out traffic. In contrast, staggered routing strategically postpones vehicle departures to smooth out peak demands on certain routes, thereby balancing travel demand over time.

While a consistent body of literature on balanced routing exists, staggered routing is a relatively new area of research. Preliminary investigations have examined the potential for congestion reduction through coordinated AMoD vehicle departures assuming centrally pre-determined paths, thus leveraging balanced routing in a sequential approach.

In this study, we propose an integrated framework for balanced and staggered routing. First, we formalize the problem of simultaneously determining the optimal routes and departure times for AMoD trips to minimize induced congestion. Second, we introduce a novel metaheuristic algorithm designed to solve large-scale problem instances. Finally, using real-world taxi data, we conduct a case study in Manhattan, New York, to examine the trade-offs between altering routes and adjusting departure times. Moreover, we quantify the additional congestion that an integrated approach to balanced and staggered routing mitigates compared to sequential strategies.

Transportation networks are integral for the movement of people and goods. The recent pandemic showed the challenges of maintaining and managing infrastructure, especially passenger transportation networks, to preserve essential basic services and limit economic harm while confining epidemic spreading. Herein, designing adaptable networks based on epidemic states to balance optimal functionality and reduce infections will become crucial in future outbreaks.

In this work, we address this balancing issue by studying an epidemic-aware network design problem. We assume passenger flows are infection-level agnostic and aim for the quickest route through the available network. We introduce epidemic-spreading aware constraints on arc utilization.For the network resources, we include decisions on transit line availability, including vehicle quantities, dimensions, and frequencies. Our goal is to find a network configuration to minimize the total passenger travel time while respecting capacity limitations and accounting for different epidemic infection scenarios.

We devise a robust branch-and-price approach using a path-based reformulation to solve city-scale instances and analyze the impact of the epidemic-aware formulation. We apply our solver to a real-world case study for the city of Munich and derive managerial insights.