Public vehicle (PV) systems are promising transportation systems for future smart cities which provide dynamic ride-sharing services according to passengers' requests. PVs are driverless self-driving electric vehicles which require frequent recharging from smart grids. For such systems, the challenge lies in both the efficient scheduling scheme to satisfy transportation demands with service guarantee and the cost-effective charging strategy under the real-time electricity pricing. In this paper, we study the joint transportation and charging scheduling for PV systems to balance the transportation and charging demands, ensuring the long-term operation. We adopt a cake cutting game model to capture the interactions among PV groups, the cloud and smart grids. The cloud announces strategies to coordinate the allocation of transportation and energy resources among PV groups. All the PV groups try to maximize their joint transportation and charging utilities. We propose an algorithm to obtain the unique normalized Nash equilibrium point for this problem. Simulations are performed to confirm the effects of our scheme under the real taxi and power grid data sets of New York City. Our results show that our scheme achieves almost the same transportation performance compared with a heuristic scheme, namely, transportation with greedy charging; however, the average energy price of the proposed scheme is 10.86 lower than the latter one.
Several novel pricing mechanisms and scheduling strategies have been proposed in on-demand mobility systems. Drwal al @cite_41 propose pricing mechanisms to balance the demands of different parking stations, reduce the cost of manual relocations of vehicles, and maximize the operator's revenue. However, this solution only considers the transportation performance, and does not consider the energy demands of vehicles from smart grids. Rigas al @cite_6 study the scenario where EVs are hired in on-demand mobility systems and there exist multiple battery swap facility points. Its aim is to maximize the number of passengers that are served. They focus on the transportation performance with limited travel ranges and simplify the charging problem as swapping batteries in facility points, ignoring the effects of the real-time pricing of smart grids, e.g., how to reduce the charging costs with the guarantee of providing transportation services. Our solution proposed in this paper considers the joint transportation and charging strategies of vehicles over one day and multiple days under the scenario of real-time electricity pricing.
@cite_41 @cite_6
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We consider on-demand car rental systems for public transportation. In these systems, demands are often unbalanced across different parking stations, necessitating costly manual relocations of vehicles. To address this so-called "deadheading" effect and maximise the operator's revenue, we propose two novel pricing mechanisms. These adaptively adjust the prices between origin and destination stations depending on their current occupancy, probabilistic information about the customers' valuations and estimated relocation costs. In so doing, the mechanisms incentivise drivers to help rebalance the system and place a premium on trips that lead to costly relocations. We evaluate the mechanisms in a series of experiments using real historical data from an existing on-demand mobility system in a French city. We show that our mechanisms achieve an up to 64 increase in revenue for the operator and at the same time up to 36 fewer relocations. We study a setting where electric vehicles (EVs) can be hired to drive from pick-up to drop-off points in a mobility-on-demand (MoD) scheme. Each point in the MoD scheme is equipped with a battery swap facility that helps cope with the EVs' limited range. The goal of the system is to maximise the number of customers that are serviced. Thus, we first model and solve this problem optimally using Mixed-Integer Programming (MIP) techniques and show that the solution scales up to medium sized problems. Given this, we develop a greedy heuristic algorithm that is shown to generate near-optimal solutions and can scale to thousands of consumer requests and EVs. Both algorithms are evaluated in a setting using data of real locations of shared vehicle pick-up and drop-off stations and the greedy algorithm is shown to be on average 90 of the optimal in terms of average task completion.