# A solution methodology for carpooling systems based on double auctions and cooperative coevolutionary particle swarms

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## Abstract

Although carpooling provides a way to achieve higher efficiency and lower costs of a transport system by grouping people and sharing cars or vehicles in use, it is still not widely accepted. Several studies indicate that cost savings and timeliness are the main incentives for acceptance of the carpooling business model. However, existing studies are deficient in supporting operations of carpooling business model from specifying the requirements of drivers and passengers, matching drivers with passengers to allocating cost savings among ridesharing participants. A pragmatic solution methodology to maximize cost savings while respecting timing constraints and allocate cost savings in carpooling systems is a critical factor for ensuring the success of carpooling business model. The problem to maximize cost savings subject to capacity constraints and timing constraints can be formulated as an integer programming problem. Although there are several works for solving carpooling problems based on metaheuristic approaches, existing studies on application of metaheuristic approaches in carpooling problems are still limited. Motivated by deficiencies of existing studies discussed above, this paper aims to (1) propose a carpooling model based on double auctions, (2) formulate the carpooling problem and develop a discrete cooperative coevolving particle swarm optimization (DCCPSO) algorithm to solve the problem (3) propose a cost savings allocation scheme to benefit ridesharing participants (4) study the influence of detour distance constraints on performance and (5) study the effectiveness of the proposed algorithm by comparing with other metaheuristic algorithms through experiments. Simulation results indicate that the proposed DCCPSO algorithm significantly outperforms several existing algorithms in solving the carpooling problem.

## Keywords

Carpool Auctions Meta-heuristic algorithm Particle swarm optimization## Notes

### Acknowledgements

This paper is currently supported in part by Ministry of Science and Technology, Taiwan under Grant MOST 106-2410-H-324-002-MY2.

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