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A Comparative Study of Several Metaheuristic Algorithms to Optimize Monetary Incentive in Ridesharing Systems
ISPRS International Journal of Geo-Information ( IF 3.4 ) Pub Date : 2020-10-08 , DOI: 10.3390/ijgi9100590
Fu-Shiung Hsieh

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.

中文翻译:

Ridesharing系统中几种优化货币激励的元启发式算法的比较研究

对人类机动性的强烈需求导致汽车数量过多,并带来严重的交通拥堵,大量的温室气体排放,空气污染以及城市停车位不足的问题。尽管拼车是通过拼车解决上述问题的一种潜在的运输方式,但仍未被广泛采用。大多数研究都考虑了非货币激励绩效指标,例如出行距离和拼车系统的成功匹配。这些性能指标无法为共享乘车提供强有力的动力。本文的目的是通过提出一项货币激励绩效指标来改善乘车共享激励措施来解决这个问题。目标是通过制定货币激励优化问题来改善乘车共享的激励,解决方案方法的开发以及不同解决方案算法的比较。制定了非线性整数规划优化问题,以优化乘车共享系统中的货币激励。为了解决上述问题,开发了几种离散的元启发式算法来应对计算复杂性。其中包括粒子群优化算法,差分进化算法和萤火虫算法的几个离散变体。根据实验结果,比较了应用上述算法解决货币激励优化问题的有效性。为了解决上述问题,开发了几种离散的元启发式算法来应对计算复杂性。其中包括粒子群优化算法,差分进化算法和萤火虫算法的几个离散变体。根据实验结果,比较了应用上述算法解决货币激励优化问题的有效性。为了解决上述问题,开发了几种离散的元启发式算法来应对计算复杂性。其中包括粒子群优化算法,差分进化算法和萤火虫算法的几个离散变体。根据实验结果,比较了应用上述算法解决货币激励优化问题的有效性。
更新日期:2020-10-08
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