Predictive Route Suggestion System for Smart Traffic Management

The “Traffic Route Prediction” project provides an operational system which selects the best alternative routes basing on current traffic condition data. With real-time congestion data as input the system creates new backup routes. The implementation uses Support Vector Machine (SVM) as its methodology. Decision Trees and Random Forests from our model assist identification of the best passenger itineraries. A complete data analysis period reveals how traffic flows and congestion affects different routes ahead of project commencement. This analysis investigates adjustments to the issue according to potential future developments. The described techniques will be used in this ordered sequence. The research identifies different route options PRIOR to algorithm evaluation through precision, accuracy and recall score measurements and the production of confusion matrix visualizations. The research identifies different route options PRIOR to algorithm evaluation through precision, accuracy and recall score measurements and the production of confusion matrix visualizations. F-score and the confusion matrix graphs. The dataset contains directional information along with geographic coordinates that include latitude and longitude and congestion measurement information. The training procedures relied on these data records