부산대학교 도서관

Sustainable urban mobility requires optimizing public transit passenger experiences. This system integrates cloud technologies and the Random Forest algorithm with bus fare ticketing systems to improve efficiency and customer pleasure. Cloud systems provide real-time data processing for price structure and route optimization in research. Cloud computing lets transportation providers quickly adjust to shifting demand, enhancing service dependability and responsiveness. The Random Forest algorithm is integrated into ticketing procedures to estimate passenger demand and optimize prices. Historical ridership data, weather, and special events are used to provide reliable fare estimates and suggestions using machine learning. This case study shows that the suggested approach improves passenger experience, waiting times, and system efficiency in a metropolitan transport network. Scalable, cloud-based infrastructure adapts to different transportation system sizes and configurations. Findings show that cloud computing and machine learning algorithms may make public transportation systems more responsive and passenger-centric. As cities seek smarter, more environmentally friendly transportation solutions, such breakthrough technology offers potential for public transit.