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Optimizing public transport system using biased random-key genetic algorithm
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AI
DOI：10.1016/j.asoc.2024.111578.png)
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Abstract
En 中文
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 Macei & oacute; (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.
Keywords:
Biased random-key genetic algorithms
Planning of public traffic system
Heuristics
Optimization
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
