Fuzzy Deep Learning based Urban Traffic Incident Detection

Traffic incident detection (TID) is an important part of any modern traffic control because it offers an opportunity to maximise road system performance. For the complexity and the nonlinear characteristics of traffic incidents, this paper proposes a novel fuzzy deep learning based TID method which considers the spatial and temporal correlations of traffic flow inherently. Parameters of the deep network are initialized using a Stacked Auto-Encoder (SAE) model following a layer by layer pre-training procedure. To conduct the fine tuning step, the back-propagation algorithm is used to precisely adjust the parameters in the deep network. Fuzzy logic is employed to control the learning parameters where the objective is to reduce the possibility of overshooting during the learning process, increase the convergence speed and minimize the error. To find the best architecture of the deep network, we used a separate validation set to evaluate different architectures generated randomly based on the Mean Squared Error (MSE). Simulation results show that the proposed incident detection method has many advantages such as higher detection rate and lower false alarm rate.