RouteRover: AI-Enabled Traffic Congestion Prediction and Route Optimization for Indian Urbanites

Here in this paper, a model that has been proposed for predicting and visually representing congestion of traffic using machine learning as well as deep learning. This initiative with the purpose of examining traffic data to obtain insights for traffic regulation and planning. The process initiates with an initial exploration of data through an Exploratory Data Analysis (EDA) and to perceive the distribution and patterns within the dataset. Descriptive statistics, correlation analysis, and visual aids are employed for this purpose. Following the EDA phase, tactics for manifold learning are utilized to simplify the dataset while retaining crucial information. Identifying significant features influencing the prediction of traffic volume is facilitated by methods for feature importance and selection. The obtained features are rigorously evaluated to comprehend traffic patterns and traffic jam levels. Visual representations capture the distribution of traffic volume based on factors like time, day, and month, assisting in pinpointing congestion hotspots and optimizing traffic flow. This project underscores the powerful impact of data analysis and feature extraction in offering practical insights for traffic regulation strategies.