GrDBN-GPR: A Next-Gen Road Feature Inference Framework for Traffic Crashes Frequency Prediction

Traffic crashes are a serious problem in modern civilization, causing enormous human and economic costs. Precise modeling of these incidents is vital for assessing road safety. Existing research often focuses on single aspects like accuracy, stability, or resistance to interference, overlooking a holistic approach. This study introduces an innovative Gaussian radial Deep Belief Net-Gaussian Process Regression framework for traffic crashes modeling. It adeptly combines feature engineering and predictive algorithms to elucidate complex traffic dynamics. The GrDBN component utilizes a Gaussian-Bernoulli Restricted Boltzmann Machine as well as Gaussian activation functions for enhanced, stable feature extraction, effectively identifying key patterns in data. The GPR component then provides reliable predictions based on these features. Applied to Highway 401 in Ontario, Canada, the model uses collision data enhanced by advanced communication technologies. Its performance, bench-marked against six prevalent models, showcases superior predictive accuracy, stability, and interference resistance. An additional experiment delves into the GrDBN-GPR model’s resistance mechanisms, revealing its proficiency in filtering interfering features and extracting critical information during feature engineering.