Spatial Transplantation for Modeling of Freeway Traffic Crash Risk Based on Dynamic Traffic Flow

This research aims at exploring the influence of multi-scale data sets on real-time traffic crash risk modelingtowards freeways, and realizing the real-time risk model transplantation for freeways with spatial differences. First, multi-scale data sets were constructed via extracting freeway sections with different detector characteristics: highresolution data sets, small-sample data sets, low-resolution data sets, and data sets with spatial differences under the same scale conditions (both are high precision and large sample size); Furthermore, the influence of various sample sizes on the prediction performance of the traffic crash risk model was quantified by Bayesian Logistic regression, and statistical methods and machine learning methods were introduced to model the high and low resolution data sets respectively. Finally, the real-time traffic crash risk migration model based on the Bayesian updating method wasestablished, and the freeway real-time crash risk prediction model was spatially transplanted, simultaneously its reliability was verified. The results show that: the performance of the model based on Bayesian Logistic regression improves with the increasing sample size; under the condition of high resolution data, the Area Under Curve (AUC) values of the Bayesian Logistic regression model and Random Forest-Support Vector Machine (RF-SVM) model are 0.092 and 0.037 higher than those under the condition of low resolution data, respectively; in the spatial migration with various data resolution, the AUC value of the low-resolution road segment model can be improved from 0.645 to 0.714 by the Bayesian updating method, and in the spatial migration with the same data scale, the AUC value of the updated road segment model can be improved from 0.737 to 0.751 by applying the Bayesian updating method. The conclusions indicate that: the model from the freeway section with a big sample size can boost the mode classification accuracy but cannot significantly improve the performance of the prediction model, the results have some fluctuations, while the model from the freeway section with high data resolution can have higher accuracy of classification and prediction of the model; statistical methods have more advantages in model interpretation, and machine learning has better prediction performance under the condition of low resolution data; The Bayesian updating model can improve the accuracy of model spatial transplantation to a certain extent. © 2023 Science Press. All rights reserved.