Analysis of Identification Algorithm for Urban Road Network Operation Status under Multisource Information

In order to improve the safety and energy utilization of vehicles, a combination of vehicle stability criterion models and traffic flow models is proposed to plan vehicle paths from the level of path planning to avoid extreme and inefficient working conditions, enabling fast and safe driving under slippery road conditions. The traffic flow model is used to predict the future changes in the traffic environment that the vehicle will face, and then the stability criterion model is used to assess the safety of future traffic in order to plan the fastest and safest path for the hybrid vehicle. Specifically, the generalized Aw–Rascle–Zhang (GARZ) macroscopic traffic flow model is solved using the flux vector splitting format in order to predict the future changes in speed and traffic density that the hybrid vehicle will face. In addition, the front-wheel steering angle responses given by the same driver at different speeds and different distances relative to the vehicle in front were collected using the driver-in-the-loop simulation platform Prescan. Simulink models based on front-wheel drive (FWD) front-wheel steering (FWS) vehicles and all-wheel steering (AWS) distributed drive vehicles (DDVs) give the force saturation factor [Formula: see text] response corresponding to different front-wheel steering angles. The stability criterion model of the vehicle was established by using artificial neural network (ANN) to train [Formula: see text] corresponding to different speeds and traffic densities. The parameters predicted by the traffic flow model (vehicle speed and traffic density) were evaluated for stability using the newly established stability criterion model. The vehicle traveling paths were optimized based on the above methods to ensure the safety of vehicle traveling on slippery road surfaces. Finally, real US-101 traffic flow data were used to verify the predictions of the traffic flow model.