A bilevel nonlinear mathematical programing model is formulated to determine the optimal pricing and operator-based relocations in a one-way station-based carsharing system in competition with private cars. In the upper level, the carsharing operator determines the vehicle fleet, prices, and relocation operations with the objective of maximizing profits, considering the potential reaction of travelers. In the lower level, travelers choose travel modes from a cost-minimization perspective. Travel utilities are calculated through a logit model. The Karush–Kuhn–Tucker conditions are used to transform the bilevel model into a single-level model and then a genetic algorithm is proposed to solve it. Computational tests in four different scenarios show the combined strategy is the best one. The four scenarios are base, relocations, dynamic pricing, and a combination of relocations and pricing separately. The combined strategy can make the best trade-offs between the operator’s profit and the travelers’ cost.

In this paper, we propose an optimization model to select the charging locations of an automated electric taxi (AET) system. The service provided by this AET system is a seamless door-to-door service connected to the train station, which helps improve the last mile transport. We individualize the vehicles instead of treating them as a flow to track the remaining battery level of each AET. Two types of charging are considered containing depot charging with lower charging speed and opportunity charging with higher charging speed. We formulate a mixed-integer programming model with linear constraints to optimize the locations of depot charging and opportunity charging according to the objective function of maximizing the number ofsatisfied requests. The proposed model is applied to the case study city of Delft, the Netherlands with the travel demand generated by the Delft Zuid train station. Results show that the charging scheme with two types of charging can provide sufficient electrical energy for shared use AETs to serve passengers’ last mile travel demand.

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.

In recent years, technology development has accelerated the future roll-out of vehicle automation. An automated vehicle (AV), also known as a driverless car and a self-driving car is an advanced type of vehicle that can drive itself on existing roads. A possible area of application for AVs is public transport. The concept of automated taxis (ATs) is supposed to offer a seamless door-to-door service within a city area for all passengers. With automation technology maturing, we may be able to see the situation in which hundreds or even thousands of ATs will be on the road replacing private vehicles accounting for the majority of people’s daily trips. However, little attention has been devoted to the usage of a fleet of ATs and their effect on a real-scale road network.
In this thesis, we explore how automated driving can serve mobility and what is the best way to introduce this technology as part of the existing transport networks. This is also the research gap this thesis is going to fill. The objective of this thesis is to contribute to the planning and operational strategies that these AT systems should follow in order to satisfy mobility demand.
This thesis uses mathematical optimization to address the above research problems. A mathematical optimization problem consists of maximizing or minimizing a function by systematically selecting some input values within a defined domain. It aims to find the best available values of the objective function and the corresponding values of the problem input. The purpose of this thesis is to provide a tool to support the decision-making processes both for long-term planning strategies and short-term tactical operations when ATs are going to be applied in the urban transport system.

This paper proposes a method of assigning trips to automated taxis (ATs) and designing the routes of those vehicles in an urban road network, and also considering the traffic congestion caused by this dynamic responsive service. The system is envisioned to provide a seamless door-to-door service within a city area for all passenger origins and destinations. An integer programming model is proposed to define the routing of the vehicles according to a profit maximization function, depending on the dynamic travel times, which varies with the ATs’ flow. This will be especially important when the number of automated vehicles (AVs) circulating on the roads is high enough that their routing will cause delays. This system should be able to serve not only the reserved travel requests, but also some real-time requests. A rolling horizon scheme is used to divide one day into several periods in which both the real-time and the booked demand will be considered together. The model was applied to the real size case study city of Delft, the Netherlands. The results allow assessing of the impact of the ATs movements on traffic congestion and the profitability of the system. From this case-study, it is possible to conclude that taking into account the effect of the vehicle flows on travel time leads to changes in the system profit, the satisfied percentage and the driving distance of the vehicles, which highlights the importance of this type of model in the assessment of the operational effects of ATs in the future.

In this paper, we propose a method of automated vehicle operation in taxi systems that addresses the problem of associating trips to automated taxis (ATs) and assigning those vehicles to paths on an urban road network. This system is envisioned to provide a transport service within a city area with a seamless door-to-door connection for all passengers' origins and destinations. ATs can drive themselves on the roads with reduced direct human input, which allow taxis to satisfy the next trip or park themselves while waiting for a request if needed. We propose an integer programming model to define the routing of the vehicles according to a profit maximization function while depending on dynamic travel times which vary with the flow of the ATs. This will be especially important when the number of automated vehicles circulating on the roads is so high that will cause traffic congestion. The total profit involves the system revenue, vehicle fuel costs, vehicle depreciation costs, parking costs, penalties for unsatisfied trips and passengers' congestion delay. The model is applied to a small case study and the results allow assessing the impact of the ATs movements on traffic congestion and the profitability of the system. Even with a small case study, it is possible to conclude that having in consideration the effect of the vehicle flows on travel time leads to different results in terms of the system profit, the parking cost and the driving distance which points out the importance of this type of models.

We propose two integer programming models for optimizing an automated taxi (AT) system for last mile of train trips. Model S1: trip reservations are accepted or rejected by the operator according to the profit maximization; model S2: any reservation on a selected zone by the model must be satisfied. Models were applied to a case-study. Results indicate that fleet size influences the profitability of the taxi system: a fleet of 40 ATs is optimal in S1 and 60 ATs in S2. Having electric ATs constrains the system for small fleets because ATs will not have time for charging.