Resumen
The strong demand on human mobility leads to excessive numbers of cars and raises the problems of serious traffic congestion, large amounts of greenhouse gas emissions, air pollution and insufficient parking space in cities. Although ridesharing is a potential transport mode to solve the above problems through car-sharing, it is still not widely adopted. Most studies consider non-monetary incentive performance indices such as travel distance and successful matches in ridesharing systems. These performance indices fail to provide a strong incentive for ridesharing. The goal of this paper is to address this issue by proposing a monetary incentive performance indicator to improve the incentives for ridesharing. The objectives are to improve the incentive for ridesharing through a monetary incentive optimization problem formulation, development of a solution methodology and comparison of different solution algorithms. A non-linear integer programming optimization problem is formulated to optimize monetary incentive in ridesharing systems. Several discrete metaheuristic algorithms are developed to cope with computational complexity for solving the above problem. These include several discrete variants of particle swarm optimization algorithms, differential evolution algorithms and the firefly algorithm. The effectiveness of applying the above algorithms to solve the monetary incentive optimization problem is compared based on experimental results.