Calendar- and Weather-Sensitive Short-Term Forecasting of Urban Bus \Ridership: Evidence from the Transjakarta Bus Rapid Transit System

Urban public transport systems are required to respond to pronounced temporal variations in passenger demand driven by calendar effects, weather conditions, and evolving mobility patterns.Reliable short-term demand forecasts have therefore become an important role in supporting operational planning and service management in large-scale transit systems.This study examines the daily ridership dynamics of the Transjakarta bus rapid transit system and evaluates the forecasting performance of three modeling approaches: seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), multilayer perceptron (MLP), and a dynamic moving-window model.The analysis is based on 851 daily observations from January 1, 2023 to April 30, 2025, with rainfall, working days, and national holidays included as exogenous variables.Each model is estimated using a training dataset and evaluated on a hold-out test set over a 30-day forecasting horizon.Forecast accuracy is assessed using the mean absolute percentage error (MAPE).The results indicate that the MLP model achieves the highest forecasting accuracy, with a MAPE of 8.547%, while SARIMAX and the dynamic model yield higher error levels of 33.345% and 37.754%, respectively.The findings suggest that non-linear modeling approaches are better suited to capturing the complex and irregular demand patterns observed in daily urban bus ridership data.The study provides empirical evidence that can support short-term planning and demand-aware operational decision-making in urban public transportation systems.