Automated Bus Scheduling and Route Optimization Platform

Public bus transportation systems are an important part of urban mobility; however, conventional scheduling and route planning approaches are for the most part static, and rely greatly on manual intervention. These methods are not good enough for dealing with real time traffic fluctuations and dynamic passenger demands, often leading to service delays and inefficient fleet utilization and increased operational costs. Recent research shows the importance of dynamical scheduling and datadriven optimization techniques to increase the efficiency of public transportation systems in smart cities. This paper proposes about Automated Bus Scheduling and Route Optimization Platform that generates optimized bus schedules based on consideration of important operational factors such as fleet availability, route assignments and predefined time windows. The system incorporates data from a Geographic Information System (GIS) to find efficient path in congested urban environment, thereby reducing the travel time and making service more reliable. Real time monitoring is made possible using GPS based vehicle tracking, enabling transport administrators to see bus movements and address delays or problems at the operation level. The platform is implemented as a web-based application employing a scalable client server architecture. Backend services are managed using Spring Framework, while MySQL is used for reliable data storage. In addition, role-based access control ensures safe and structured interaction between administrators, drivers, conductors, consistent with assuring security today requirements for smart transportation systems. Experimental evaluations show that the proposed system provides substantial lessening of manual scheduling efforts, enhances operational transparency, and enhances fleet use when as compared to traditional methods. The platform is designed to support the smart city initiatives and provides a basis for future improvements, including demand learning based on machine learning predicting and transportation services using IoT.