The Alliance between Optimization and Multi-Agent System for the Management of the Dynamic Carpooling

Cheikh, Sondes Ben; Hammadi, Slim

doi:10.1007/978-3-319-07650-8_20

Cited by 3 publications

(5 citation statements)

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“…Zhou et al [210] have proposed and evaluated a sophisticated framework that considers data from various sources to aggregate the user's exact location and activities in order to achieve a real-time intelligent and convenient system that shows improvements in travel time and distance covered by CAV. This optimization can also beneit from a multi-agent-based system considering a multi-criteria approach [28].…”

Section: Other Methodologiesmentioning

confidence: 99%

Carpooling in Connected and Autonomous Vehicles: Current Solutions and Future Directions

et al. 2022

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Owing to the advancements in communication and computation technologies, the dream of commercialized connected and autonomous cars is becoming a reality. However, among other challenges such as environmental pollution, cost, maintenance, security, and privacy, the ownership of vehicles (especially for Autonomous Vehicles (AV)) is the major obstacle in the realization of this technology at the commercial level. Furthermore, the business model of pay-as-you-go type services further attracts the consumer because there is no need for upfront investment. In this vein, the idea of car-sharing ( aka carpooling) is getting ground due to, at least in part, its simplicity, cost-effectiveness, and affordable choice of transportation. Carpooling systems are still in their infancy and face challenges such as scheduling, matching passengers interests, business model, security, privacy, and communication. To date, a plethora of research work has already been done covering different aspects of carpooling services (ranging from applications to communication and technologies); however, there is still a lack of a holistic, comprehensive survey that can be a one-stop-shop for the researchers in this area to, i) find all the relevant information, and ii) identify the future research directions. To fill these research challenges, this paper provides a comprehensive survey on carpooling in autonomous and connected vehicles and covers architecture, components, and solutions, including scheduling, matching, mobility, pricing models of carpooling. We also discuss the current challenges in carpooling and identify future research directions. This survey is aimed to spur further discussion among the research community for the effective realization of carpooling.

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Section: Other Methodologiesmentioning

confidence: 99%

Carpooling in Connected and Autonomous Vehicles: Current Solutions and Future Directions

et al. 2022

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“…A detailed positioning of the paper with respect to the related literature while highlighting the novelty and merits of our approach is reported in the subsequent Section 2. 3 We also note that this paper is an extended version of our previous papers that appeared as (15) and (13). Even if, in the work (13), we adopt the tabu search approach to solve the ridematching problem, it differs from this paper in several aspects.…”

Section: Introductionmentioning

confidence: 99%

“…3 We also note that this paper is an extended version of our previous papers that appeared as (15) and (13). Even if, in the work (13), we adopt the tabu search approach to solve the ridematching problem, it differs from this paper in several aspects. Indeed, in 2 (13), we focus on the alliance between optimization and multi-agent system and we present a distributed multi-agent architecture where highly communicating entities evolve.…”

Section: Introductionmentioning

confidence: 99%

“…Even if, in the work (13), we adopt the tabu search approach to solve the ridematching problem, it differs from this paper in several aspects. Indeed, in 2 (13), we focus on the alliance between optimization and multi-agent system and we present a distributed multi-agent architecture where highly communicating entities evolve. Then, the optimization approach in (13) is only briefly introduced and not formalized, which is here fully accomplished.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, in 2 (13), we focus on the alliance between optimization and multi-agent system and we present a distributed multi-agent architecture where highly communicating entities evolve. Then, the optimization approach in (13) is only briefly introduced and not formalized, which is here fully accomplished. As regards the paper (15), in the present manuscript, we extend our previous work by: (A) performing an in-depth survey of existing carpooling systems, (B) studying the complexity of the dynamic carpooling problem, (C) illustrating in more depth the different steps of our optimization approach, (D) developing the theoretical aspects of our evaluation process where we apply a fuzzification method to normalize the values of the criteria and we adopt the Choquet Integral to take in consideration the interactions among them, (E) presenting new experimental results and highlighting the contribution of the Choquet Integral to ensure a rigorous evaluation of the generated solutions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Tabu Search based metaheuristic for dynamic carpooling optimization

Cheikh-Graiet

Dotoli

Hammadi

2020

Computers & Industrial Engineering

Self Cite

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The carpooling problem consists in matching a set of riders' requests with a set of drivers' offers by synchronizing their origins, destinations and time windows. The paper presents the so-called Dynamic Carpooling Optimization System (DyCOS), a system which supports the automatic and optimal ridematching process between users on very short notice or even en-route. Nowadays, there are numerous research contributions that revolve around the carpooling problem, notably in the dynamic context. However, the problem's high complexity and the real time aspect are still challenges to overcome when addressing dynamic carpooling. To counter these issues, DyCOS takes decisions using a novel Tabu Search based metaheuristic. The proposed algorithm employs an explicit memory system and several original searching strategies developed to make optimal decisions automatically. To increase users' satisfaction, the proposed metaheuristic approach manages the transfer process and includes the possibility to drop off the passenger at a given walking distance from his destination or at a transfer node. In addition, the detour concept is used as an original aspiration process, to avoid the entrapment by local solutions and improve the generated solution. For a rigorous assessment of generated solutions , while considering the importance and interaction among the optimization criteria, the algorithm adopts the Choquet integral operator as an aggregation approach. To measure the effectiveness of the proposed method, we develop a simulation environment based on actual carpooling demand data from the metropolitan area of Lille in the north of France.

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A negotiation model of individual matching and zonal-based travel behavior in carpooling

Hussain,

Janssens,

Elomri

et al. 2023

Pers Ubiquit Comput

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Carpooling is a sustainable and ecologically acceptable transportation mode. Individuals commonly engage in coordination and negotiation processes to find matching partners and typically modify their schedules to enable cooperation. Mutual cooperation between carpooling individuals plays an important role in executing trips. Through cooperation, participants can achieve challenging agreements effectively in a repetitive manner. This paper presents a negotiation mechanism that can match individuals for carpooling using organization and agent-based concepts. It describes a matching model and a carpooling social network. It studies several aspects of multi-zonal individual behavior to identify groups of carpooling candidates. The carpooling social network is simulated on an ongoing basis for each of the following carpooling activities: interaction, negotiation, and trip execution. The interaction process enables communication between individuals within carpooling social groups in order to activate the negotiation process. During the negotiation process, participants typically modify their schedules to support cooperation by considering their personal preferences and constraints. Negotiation leads to matching of individuals based on trip start times, driver selection, detour duration, and carpool group pickup and dropoff sequences. Trip start times are established on travel, social, financial, and schedule-related factors. The carpoolers’ pickup and dropoff sequences that are feasible for an optimal carpool group are projected using specific scoring methods. Carpooling community candidates are recognized via outcomes projected using the FEATHERS activity–based model. The framework is implemented through the Janus multi-agent system.

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The Alliance between Optimization and Multi-Agent System for the Management of the Dynamic Carpooling

Cited by 3 publications

References 1 publication

Carpooling in Connected and Autonomous Vehicles: Current Solutions and Future Directions

Carpooling in Connected and Autonomous Vehicles: Current Solutions and Future Directions

A Tabu Search based metaheuristic for dynamic carpooling optimization

A negotiation model of individual matching and zonal-based travel behavior in carpooling

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