This study is based on a machine learning algorithm known as XGBoost. We used the XGBoost to forecast capacity of Jakarta's mass transit system. Using preprocessed raw data obtained from Jakarta Open Data website for period 2020-2021 training medium, we achieved mean absolute percentage error 69. However, after model was fine-tuned, MAPE significantly reduced by 28.99% 49.97. The found be effec...

This paper studies the disruption management problem of rapid transit rail networks. Besides optimizing the timetable and the rolling stock schedules, we explicitly deal with the effects of the disruption on the passenger demand. We propose a two-step approach that combines an integrated optimization model (for the timetable and rolling stock) with a model for the passengers' behavior. We repor...

The Fibera 2000A Fiber Optic Monitoring System is a system that identifies defects occurred in a structure that can not be easily detected from its surface. The integrity of structures such as building, nuclear reactor, power plant, rapid transit system, oil pipe, aircraft, and bridge, etc. that are vitally important to our lives, can be closely monitored and issue warnings if a defect is occur...

This paper presents the development of a novel framework that optimizes the evacuation of large cities using multiple modes including vehicular traffic, rapid transit, and mass transit shuttle buses. A large-scale evacuation model is developed for the evacuation of the City of Toronto in cases of emergency. A demand estimation model is first designed to accurately quantify the evacuation demand...