<![CDATA[Increased public mobility during the homecoming period causes a surge in traffic volume, impacting traffic density and the complexity of transportation management. Inaccuracies in estimating the number of movements often hamper the effectiveness of policies implemented by stakeholders. This study aims to implement the Exponential Smoothing method in forecasting the homecoming flow as a basis for supporting transportation management policies. Historical data on the number of passenger movements in the previous period is analyzed using a time series forecasting approach with the determination of optimal smoothing parameters to produce accurate predictions. Model performance evaluation is carried out using forecast error measurements to ensure the reliability of the prediction results. The results show that the Exponential Smoothing method is able to provide stable estimates of the number of homecoming flows and is responsive to data change patterns, thus assisting in transportation capacity planning, scheduling, and resource distribution. Thus, the application of this method has the potential to improve the accuracy of transportation management policy-making during the homecoming period and minimize the risk of congestion and service imbalances]]>
Copyrights © 2025
