LSTM Model for Traffic Prediction
Loads traffic data (Vehicles count at different times).
Preprocesses data: Converts timestamps, normalizes vehicle counts using MinMaxScaler, and creates sequences (last 3 hours → next hour prediction).
Trains an LSTM model with dropout layers to prevent overfitting.
Predicts the next hour's vehicle count using the trained model.
Genetic Algorithm for Traffic Light Optimization
Defines a fitness function that simulates vehicle wait times based on traffic light timings.
Uses a Genetic Algorithm to optimize traffic light durations:
Generates an initial population of random timings.
Evolves through selection, crossover, and mutation.
Outputs optimized signal timings to minimize wait times.
Dijkstra’s Algorithm for Vehicle Rerouting
Implements Dijkstra’s shortest path algorithm to find the most efficient route for vehicles.
Uses a graph representation of a road network.
Computes the shortest distances from a given starting point ('A') to all other nodes.
Output
Predicted vehicle count for the next hour.
Optimized traffic light timings for smooth traffic flow.
Shortest paths in the road network for vehicle rerouting.
This approach helps in traffic congestion management, route optimization, and smart signaling using AI techniques. 🚦🚗
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