Method of Adaptive Neuron Model Utilizing Information of Local Samples for Loss Prediction in the Road Traffic Accident

This paper presents an adaptive neuron model utilizing information of local samples—the AUILS neuron model. Differing from traditional neuron models, the AUILS neuron model fully employs the experience samples information within the local range and well embodies the association and analogy functions of cerebrum. The neural network, which is simple in structure and fast in learning speed, can realize the nonlinear mapping relationship between multi-input and multi-output. Through investigating the properties of AUILS, we build a method based on the neuron model for loss prediction in the road traffic accident, which takes full advantage of expert experiences to estimate the loss value according to the accident number, deaths and wounds. It is verified that using the AUILS model, expert experiences can be well expressed in the prediction results.