ANN Estimate Model on Impact Speed of Car-Bicycle Accidents Based on the Complete Information

Through the investigation on car-to-bicycle accidents of Beijing city, the characteristic impact parameters of human, bicycle and vehicle are collected. The impact speeds are classified by statistic results and law regulation. Above that, based on the Artificial Neural Network (ANN) method, relevant typical parameters are selected to build an estimate model on vehicle impact speed of car-to-bicycle accidents. The input layer of the model contains 45 nodes, including human parameters, bicycle parameters, car parameters, road parameters and other information. The output layer of the model represents the forecasting data and different classification results of vehicle impact speed respectively. Using the credible data from real accidents, the model can be trained and applied in vehicle-speed analysis and accident reconstruction.