In this paper, we study the dial-a-ride problem of ride-sharing automated taxis (ATs) in an urban road network, considering the traffic congestion caused by the ATs. This shared automated mobility system is expected to provide a seamless door-to-door service for urban travellers, much like what the existing transportation network companies (TNC) do, but with decreased labour cost and more flexible relocation operations due to the vehicles’ automation. We propose an integer non-linear programming (INLP) model that optimizes the routing of the ATs to maximize the system profit, depending on dynamic travel times, which are a non-linear function of the ATs’ flows. It is important to involve traffic congestion in such a routing problem since for a growing number of ATs circulating in the city their number will lead to delays. The model is embedded within a rolling horizon framework, which divides a typical day into several horizons to deal with the real-time travel demand. In each horizon, the routing model is solved with the demand at that interval and assuring the continuity of the trips between horizons. Nevertheless, each horizon model is hard to solve given its number of constraints and decision variables. Therefore, we propose a solution approach based on a customized Lagrangian relaxation algorithm, which allows identifying a near-optimal solution for this difficult problem. Numerical experiments for the city of Delft, The Netherlands, are used to demonstrate the solution quality of the proposed algorithm as well as obtaining insights about the AT system performance. Results show that the solution algorithm can solve the proposed model for hard instances. Ride-sharing makes the AT system more capable to provide better service regarding delay time and the number of requests that can be attended by the system. The delay penalty on the profit objective function is an effective control parameter on guaranteeing the service quality while maintaining system profitability.@en

This paper presents a method for determining the deployment of one-way electric carsharing services within a designated region that maximizes the total profit of the operator. A mixed integer non-linear program model is built, with a strategic planning level that decides the fleet size and the station capacity and an operational level that decides on the required relocation operations. The state of charge (SOC) of the vehicles parked in one station is assumed to follow a continuous distribution. A rolling horizon method is used to optimize the operational decisions over the course of a day, considering demand fluctuations and the limited battery capacity of the vehicles. A golden section line search method and a shadow price algorithm are developed to optimize the fleet size and station capacity, with the results feeding back to the carsharing operations. To demonstrate the applicability of the formulated models and solution algorithms, a large-scale case study is conducted for Suzhou Industrial Park, China as the region of operation. A two-step verification method that combines an optimization model via tracking of individual vehicle SOC and a discrete event simulation, demonstrates the accuracy of the SOC distribution model. Managerial insights from the application are also presented.@en