Carpooling consists in sharing one's personal vehicles with one or several passengers in order to share the related costs but also reduce traffic and CO 2 emissions. Today, there are several studies that revolve around dynamic carpooling. The problem is how to assign every new passenger's request to one or more vehicles. This assignment must be done in real time. However, most of these studies still remain in embryonic stage regarding automation and real time aspects. In addition, there is another big handicap, due to the problem's high complexity, concerning the way to make the process perform efficiently. Therefore, this study adopts a Multi-criterion Tabu search (MTS), which is an evolutionary method based on explicit memory systems and several searching strategies designed to avoid the entrapments by local solutions. Then, to obtain a rigorous and meaningful evaluation of our solution, we use an original aggregative approach based on Choquet Integral which take into account the interaction, the compensation and avoid redundancy between criteria. Finally, to assess the merits of our approach we present a simulation study based on travel demand data from metropolitan of Lille-France. The simulation results indicate that the use of metaheuristic optimization methods improve the performance of our dynamic carpooling system. Index Terms-dynamic carpooling, tabu search, choquet integral I.

No abstract

No abstract

The multihop ridesharing system generates a ridematching solution with an arbitrary number of transfers that respects personal preferences of the users and their time constraints with detour willingness. As it is considered to be NP-complete, an efficient metaheuristic is required in the application to solve the dynamic multihop ridematching problem. In this context, a novel approach, called Metaheuristics Approach Based on Controlled Genetic Operators (MACGeO), which is supported by an original dynamic coding, is developed to address the multihop ridematching problem. The performance of the proposed approach is measured via simulation scenarios, which feature various numbers of carpool drivers (vehicles) and riders (passengers). Experimental results show that the multihop ridematching could greatly increase the number of matched requests while minimizing the number of vehicles required.

A diverse range of architectures and concepts has been suggested by academic researchers within the theme of ridesharing. Most of these studies have tried to join together two main elements: the need for mobility and the resources used to achieve this action. Based on empty seats in the private cars, rideshare systems allow a substantial number of people to share car rides. If a request can be matched with only one offer, then the problem is called singlehop ridematching. It is called multihop ridematching, if a request can be matched with severel offers at different times. In this paper, we focus on the Multi-Hop Ridematching Problem ( ) which offers more flexible than other forms of ridesharing and proposes more choices for the rider. As a transportation service, the is generally geographically distributed in dynamic changing environments. With this in mind, and aiming at setting up a distributed architecture through which an optimizing decentralized process should be performed, we propose a distributed software architecture based on communicating agents with different behaviors. The reason for the growing success of agents behaviors technology especially in performing complex tasks and operating with each other to achieve a desired global goal.