Carpooling involves sharing your personal vehicle to make a common trip in order to share the costs of fuel, toll, or simply to exchange. The goal of this work is to adopt an ideal strategy for carpooling. The problem is to find the best groups between a fixed set of individuals who make the same trip every day and in a regular way. In order to reach the goal, the authors adapted a bio-inspired meta-heuristic (firefly algorithm). This technique allowed them to have very satisfactory results.
TopIntroduction
The idea of carpooling is to share a car with several people making the same trip. Unlike hitchhiking, transportation costs are shared by everyone in the vehicle (Teal, 1987). It can be casual (travel, music festivals, etc.), or regular, such as carpooling with colleagues. Due to its economic and ecological benefits, carpooling is becoming more and more popular (Vanoutrive et al, 2012).
In most cases, carpooling reduces the cost of car trips. In fact, all the passengers of the vehicle share the expenses related to the displacement, such as the fuel cost and toll fees. The cost of this solution is significantly lower than that of taking public transportation or traveling alone by car.
Keeping in mind the time constraints of the operation, this work aims to minimize the number of vehicles used and the total distance travelled by all users.
Carpooling can be seen as a combination of a clustering and routing problem.
Using the Firefly algorithm, this work seeks to solve the problem of regular large carpools and extensions in a more effective way.
Numerous government agencies and employers have used carpooling as an effective strategy to address a wide range of climate, environmental, and congestion mitigation goals, while simultaneously increasing roadway and parking capacity for decades. (Shaheen et al, 2018).
The authors of this study are interested in regular carpooling. Finding the best groups under different constraints is the challenge.
The main objective of this study is to provide companies with efficient use of transport increase their returns. As a result, several questions are required:
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How the best group be properly determined?
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How the distances be minimized?
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How should we proceed to reduce transport costs?
The rest of the chapters are organized as follows: section 2 provides an overview of the subject area. Sections 3, 4, and 5 describe the method used to develop the contribution. In section 6, the authors present the results obtained using the proposed approach. These results are discussed in section 7. The authors finish with a conclusion, including future possibilities.
TopBackground
This section presents an overview of the methods used to solve carpooling problems.
In 2021, Kaleche (Kaleche et al, 2021) presented An Improved Biogeography Based Optimization for the Long Term Carpooling Problem.
Unlike the popularity of its related problems, little literature exists on carpooling. In the literature, different approaches have been proposed to solve the problem of regular carpooling, including an algorithm based on recording functions (Ferrari, 2003), The ANTS algorithm (Akka, 2018), a simulation-based approach (Viegas, 2010), a multi-matching system (Yan, 2011), and the Bird swarm algorithm for solving the long-term carpooling problem (Bendaoud, 2018). In this section, the authors classify resolution methods into two main categories: heuristics and metaheuristics.