In this study, we formulate positive and negative externalities caused by changes in the supply of shared vehicles as ride sharing games. The study aims to understand the price of anarchy (PoA) and its improvement via a coordination technique in ride sharing games. A critical question is whether ride sharing games exhibit a pure Nash equilibrium (pNE) since the PoA bound assumes it. Our result shows a sufficient condition for a ride sharing game to have a finite improvement property and a pNE similar to potential games. This is the first step to analyze PoA bound and its improvement by coordination in ride sharing games. We also show an example of coordinating players in ride sharing games using signaling and evaluate the improvement in the PoA.
According to a review of sharing studies in transportation areas @cite_14 , there are few game theory studies on externalities and coordination of players' moves in vehicle sharing problems. Traditional studies on sharing include the optimization of vehicle routes for picking up all passengers (the Dial-a-ride problem ), problems of splitting passengers' fares according to their riding distances, optimization of locations of carpool stations, and problems of relocation of carpool vehicles among stations. Those studies mainly focus on optimization problems under given moves (origins and destinations) of passengers and fixed drivers of vehicles, and therefore, do not analyze the PoA when passengers strategically choose their moves in response to the moves of other passengers and vehicles. The review states that there are no studies on coordination of players' moves to improve the efficiency of sharing or reduce the PoA.
@cite_14
2031128290
Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societ al impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that ( i ) scales to large numbers of passengers and trips and ( ii ) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems. In the dial-a-ride problem, users formulate requests for transportation from a specific origin to a specific destination. Transportation is carried out by vehicles providing a shared service. The problem consists of designing a set of minimum-cost vehicle routes satisfying capacity, duration, time window, pairing, precedence, and ride-time constraints. This paper introduces a mixed-integer programming formulation of the problem and a branch-and-cut algorithm. The algorithm uses new valid inequalities for the dial-a-ride problem as well as known valid inequalities for the traveling salesman, the vehicle routing, and the pick-up and delivery problems. Computational experiments performed on randomly generated instances show that the proposed approach can be used to solve small to medium-size instances. This paper is the second part of a comprehensive survey on routing problems involving pickups and deliveries. Basically, two problem classes can be distinguished. The first part dealt with the transportation of goods from the depot to linehaul customers and from backhaul customers to the depot. The second part now considers all those problems where goods are transported between pickup and delivery locations, denoted as Vehicle Routing Problems with Pickups and Deliveries (VRPPD). These are the Pickup and Delivery Vehicle Routing Problem (PDVRP – unpaired pickup and delivery points), the classical Pickup and Delivery Problem (PDP – paired pickup and delivery points), and the Dial-A-Ride Problem (DARP – passenger transportation between paired pickup and delivery points and user inconvenience taken into consideration). Single as well as multi vehicle mathematical problem formulations for all three VRPPD types are given, and the respective exact, heuristic, and metaheuristic solution methods are discussed. This study examines how conversing with passengers in a vehicle differs from conversing on a cell phone while driving. We compared how well drivers were able to deal with the demands of driving when conversing on a cell phone, conversing with a passenger, and when driving without any distraction. In the conversation conditions, participants were instructed to converse with a friend about past experiences in which their life was threatened. The results show that the number of driving errors was highest in the cell phone condition; in passenger conversations more references were made to traffic, and the production rate of the driver and the complexity of speech of both interlocutors dropped in response to an increase in the demand of the traffic. The results indicate that passenger conversations differ from cell phone conversations because the surrounding traffic not only becomes a topic of the conversation, helping driver and passenger to share situation awareness, but the driving condition also has a direct influence on the complexity of the conversation, thereby mitigating the potential negative effects of a conversation on driving.