Abstract:
Having a growing population and increasing urbanization, Dhaka City has a lot of obstacles when it comes to keeping up a dependable and effective public transit system. With the goal of offering a comprehensive knowledge of the variables driving variability in bus travel times throughout the city, this thesis explores the crucial topic of bus travel time dependability. In order to do a thorough study, the research uses a complex strategy that integrates real-time data, historical trip time records, and sophisticated statistical modeling approaches. The study begins with a thorough analysis of the body of prior research, constructing a theoretical framework that outlines the critical components influencing the dependability of bus journey times in intricate urban settings. Then, in order to find patterns, trends, and causes of trip time variability, large datasets from the bus fleet of Dhaka City are examined. Next, predictive models are created using machine learning techniques, which enable the prediction of journey time reliability in a variety of operating and environmental scenarios. The study findings have noteworthy consequences for Dhaka City politicians, transportation authorities, and urban planners. The thesis aids in the creation of focused interventions and policies meant to improve the effectiveness of the city's bus transportation system by offering useful insights into the factors influencing bus trip time reliability. The results are anticipated to spark enhancements in service dependability, resulting in a rise in public confidence, ridership, and a more sustainable urban transportation environment. The study's methods and conclusions go beyond Dhaka to add to the larger body of knowledge on urban transportation planning by providing a repeatable framework for evaluating and enhancing bus service.
