Traffic incident detection using particle swarm optimization

This paper proposes a new approach to automatic incident detection on traffic highways using particle swarm optimization (PSO). The rampant growth in traffic incidents, which is high cost incurring, has led to significant interest in the development of effective incident detection techniques in recent years. Various techniques have been proposed to effectively address this problem, the most promising of which are artificial neural networks (ANN) based methods. Backpropagation (BP) has proven to be one of the best methods to train weights of ANN for incident detection. However it has several limitations including slow convergence, heuristic determination of parameters and possibility of getting stuck in a local minima. This paper overcomes these problems by using particle swarm optimization to train a neural network in place of BP. Actual data from a highway was used for training and testing of this method. Simulation results show that PSO performed better than the backpropagation algorithm.