Planning the public transportation system of a city is a complex process that depends on various factors, including transportation modes, origin–destination demands, service quality and reliability, and operational costs. The vehicle frequency setting (FS) problem is a particularly challenging aspect of this planning process. This work proposes a novel methodology, based on biased random-key genetic algorithms (BRKGA), for optimizing the FS of a bus-based public transport system. The proposed approach considers two optimization models that aim to address the following key metrics: (i) passengers’ waiting time, and (ii) the operational cost for the concessionaire company, specifically the distance covered by buses. We apply our BRKGA methodology to a real case study using bus transport data from the city of Maceió (AL, Brazil). Our results demonstrate that, for each metric, the proposed methodology improves the performance of the city’s public transport system by over 10%, compared to the current configuration.

中文翻译：

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使用有偏随机密钥遗传算法优化公共交通系统

城市公共交通系统规划是一个复杂的过程，取决于多种因素，包括交通方式、始发地到目的地的需求、服务质量和可靠性以及运营成本。车辆频率设置 (FS) 问题是此规划过程中一个特别具有挑战性的方面。这项工作提出了一种基于偏置随机密钥遗传算法 (BRKGA) 的新颖方法，用于优化基于公交车的公共交通系统的 FS。所提出的方法考虑了两种优化模型，旨在解决以下关键指标：（i）乘客的等待时间，以及（ii）特许公司的运营成本，特别是公交车行驶的距离。我们使用马塞约市（巴西阿拉巴马州）的公交车交通数据，将 BRKGA 方法应用于实际案例研究。我们的结果表明，对于每个指标，与当前配置相比，所提出的方法将城市公共交通系统的性能提高了 10% 以上。