Long-term Car Pooling Problem Research Articles

An Improved Biogeography-Based Optimization for the Long-Term Carpooling Problem

ABSTRACT The increasing number of vehicles on the road produces negative effects for health, the environment, quality of life, and the economy, among other areas. To address this problem, an important key is carpooling private vehicles from different homes to a common destination. This paper specifically addresses the long-term carpooling problem, which is an NP-complete problem. The proposed approach is a modified biogeography-based optimization metaheuristic, which is hybridized with a variable neighborhood search. Comparisons with efficient known approaches indicate the effectiveness of the proposed approach for large-scale long-term carpooling problems.

An artificial bee colony algorithm with variable neighborhood search and tabu list for long-term carpooling problem with time window

Carpooling is an effective sharing economy mode that can increase utilization ratio of vehicle and relieve traffic pressure in cities. A carpooling system needs to provide feasible car-sharing solution under the constraint on time window of each passenger. Moreover, fairness is important to a long-term car sharing problem. A person will not willing to share his/her car if he/she has more contribution than others. In this paper, a long-term carpooling problem with time window is investigated for a group of people with a common destination. In order to guarantee fairness, a person can be chosen as a driver only for a limited number of consecutive days. An artificial bee colony algorithm combining variable neighbor search and tabu list (ABC-VNSTL) are proposed. We designed five neighbor search strategies: one-swapping strategy, all-swapping strategy, moving strategy, passenger to driver strategy, and solution exchange strategy. The experiments results demonstrate that ABC-VNSTL can obtain better solution quality than other six algorithms in the literature. As the number of participants increases, the advantages of ABC-VNSTL increase. In addition, ABC-VNSTL algorithm is more efficient than the compared algorithms under the condition of achieving same solution quality.

Tabu search for the real-world carpooling problem

Carpooling is a flexible shared transportation system which can effectively reduce the vehicle numbers and fuel consumption. Although many carpooling systems have been proposed, most of them lack practicality, veracity, and efficiency. In this paper, we propose a new useful variant model of the long-term carpooling problem which involves multiple origins and one destination. Such problems commonly occur in a wide number of carpooling situations in real-world scenarios. Our work is motivated by the practical needs to solve environmental pollution, parking problems, traffic jams and low utilization of resources. A Tabu search algorithm is proposed in this paper to solve the carpooling problem. The proposed algorithm aims at a wide range of passenger distribution and routing problems. The computational results based on real world user data show the effectiveness of the proposed algorithm. Moreover, we developed a mobile application based on our carpooling model.

Retraction Note to: A Multi-agent Based Self-adaptive Genetic Algorithm for the Long-term Car Pooling Problem

Retraction Note to: A Multi-agent Based Self-adaptive Genetic Algorithm for the Long-term Car Pooling Problem

A Clustering Ant Colony Algorithm for the Long-Term Car Pooling Problem

Along with the increase of population and the dispersion of habitation, the use of private cars has been increasing drastically. More and more vehicles on the road have caused significant traffic congestion, noise, and energy waste. Car pooling, which is based on the idea that sets of car owners having the same travel destination share their vehicles, has emerged to be a viable possibility for reducing private car usage around the world. This paper describes a clustering ant colony algorithm for solving the long-term car pooling problem. Computational results are given to show the superiority of the authors’ approach compared with other metaheuristics.

RETRACTED ARTICLE: A Multi-agent Based Self-adaptive Genetic Algorithm for the Long-term Car Pooling Problem

RETRACTED ARTICLE: A Multi-agent Based Self-adaptive Genetic Algorithm for the Long-term Car Pooling Problem