Rider Re-Route Suggestions Using Demand Forecasting Based on Passenger's Routes and User Behaviour Analysis

Abstract This project explores the optimization of ride-sharing in urban transportation through data analysis and internet networking. It aims to revolutionize commuting habits by connecting riders and passengers via an intelligent platform, suggesting alternative routes based on demand. By addressing the issue of lone commuters, the project aims to enhance urban mobility efficiency, reduce trip costs, and promote sustainability. Divided into components for rider and passenger registration, demand forecasts, matchmaking, and route optimization, the project showcases significant results. The suggested deviations offer comparable travel times, substantial cost savings, and increased customer satisfaction. This initiative aligns with environmental goals by reducing single-occupancy car use. Additionally, the project delves into analyzing user behavior within the ride-sharing platform, utilizing data analytics and vehicle telemetry. Leveraging K-Means clustering, it classifies behavior patterns to anticipate future actions and enhance user experiences while contributing to vehicle health management and safety. The proposed solution integrates feature engineering, behavior classification, and automation modules supported by robust database design and implemented using React JS, Python, and Node.js. The algorithm employs preprocessing, optimal K determination, and K-Means clustering for behavior analysis, leading to actionable insights and targeted interventions. Through simulation tools and real-time implementation, the project demonstrates its potential to optimize user engagement, enhance safety, and boost service efficiency, marking a data-driven shift in urban transportation paradigms.