BusBuddy: A Data-Driven Analytics Framework for Smart Bus Route Optimization
Keywords:
Demand forecasting, Dynamic scheduling, Fleet management, Internet of Things (IoT), Machine learning, Predictive analytics, Public bus optimization, Route optimization, Sustainable urban mobilityAbstract
Public bus transportation networks are under tremendous strain due to rapid urbanization and rising passenger mobility, especially in heavily populated nations like India, where more than 70 million people depend on bus services every day. Conventional bus scheduling and allocation techniques rely more on past assumptions than on current demand trends, making them essentially static. As a result, buses on high-demand routes are overcrowded, buses on low-demand routes are underutilized, fuel consumption rises, and revenue inefficiencies occur. In order to dynamically improve fleet allocation, this article suggests BusBuddy, a data-driven bus optimization system that makes use of machine learning-based forecasting, IoT-enabled GPS monitoring, and ticketing data integration. Using a Python-based analytics engine, the system analyzes both historical and current passenger data and applies predictive models to forecast demand per route. An interactive dashboard gives transport administrators real-time monitoring and operational insights, while a dynamic allocation system modifies bus deployment in response to changing passenger volumes. The suggested approach seeks to increase passenger happiness, save operating costs, and increase fleet utilization. According to simulation-based estimates, there may be a 25% increase in revenue, up to 40% reduction in fuel consumption observed under simulated conditions costs, and a far quicker operational planning process. BusBuddy provides a flexible and scalable architecture that is appropriate for sustainable urban mobility development and smart city transportation projects.
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