An Adaptable Algorithm for Optimizing Bus Line Distribution Using the Clustering Method

The current fleet of cars requires reevaluation, upgrades, and replacement in response to the expanding human population, the increase in urban traffic volume, and the elevated levels of air pollution attributed to vehicular emissions. In densely populated cities, bus routes effectively compete with private transportation, optimize user accessibility, and provide access to all urban residents. This study proposes a novel method for optimizing bus routes, encompassing four primary processes: preprocessing, site clustering, ridership prediction, and optimization. The clustering process employs the K-means technique to classify available bus stops based on their geographical information. The suggested method utilizes an artificial neural network model to forecast the number of passengers at different locations. Subsequently, the bee colony optimization algorithm is implemented to determine bus frequencies and achieve an optimal distribution of buses across various traffic lines. Results obtained using a real traffic line dataset indicate a 32% increase in traffic fleet capacity and a 22.33% reduction in passenger waiting time upon application of the proposed method.