Optimization urban transportation system based on trends using suitable algorithm

Abstract

Abstract The operation of public transportation, especially within the scope of urban transportation, has become a highly significant issue in maintaining the smoothness of urban mobility. Nevertheless, situations during peak hours and the occurrence of special events tend to lead to an increase in the number of passengers utilizing urban transportation services, thereby inducing varying demand. The primary focus of this paper is to present a more adaptive approach by developing updates to conditions at each stop, which can provide flexibility and periodic implementation of optimization. To achieve this goal, we propose the application of the Holt-Winters Exponential Smoothing method, considering aspects of level, trend, and seasonal components when conducting forecasts. This method evaluates actual data, previous component values, and predetermined parameters, aiming to generate more accurate forecasts and adeptly address the existing seasonal patterns and trends within the time series data. The findings of this research will offer practical guidance to decision-makers in optimizing the operational schedule of urban transportation, while taking dynamic factors into account. Overall, this paper holds the potential for a positive impact on service enhancement by adjusting the timing of urban transportation deliveries according to prevailing conditions.