Classification modeling approach for vehicle dynamics model‐integrated traffic simulation assessing surrogate safety

Abstract

SUMMARYTo assess safety impacts of untried traffic control strategies, an earlier study developed a vehicle dynamics model-integrated (i.e., VISSIM-CarSim-SSAM) simulation approach and evaluated its performance using surrogate safety measures. Although the study found that the integrated simulation approach was a superior alternative to existing approaches in assessing surrogate safety, the computation time required for the implementation of the integrated simulation approach prevents it from using it in practice. Thus, this study developed and evaluated two types of models that could replace the integrated simulation approach with much faster computation time, feasible for real-time implementation. The two models are as follows: (i) a statistical model (i.e., logit model) and (ii) a nonparametric approach (i.e., artificial neural network). The logit model and the neural network model were developed and trained on the basis of three simulation data sets obtained from the VISSIM-CarSim-SSAM integrated simulation approach, and their performances were compared in terms of the prediction accuracy. These two models were evaluated using six new simulation data sets. The results indicated that the neural network approach showing 97.7% prediction accuracy was superior to the logit model with 85.9% prediction accuracy. In addition, the correlation analysis results between the traffic conflicts obtained from the neural network approach and the actual traffic crash data collected in the field indicated a statistically significant relationship (i.e., 0.68 correlation coefficient) between them. This correlation strength is higher than that of the VISSIM only (i.e., the state of practice) simulation approach. The study results indicated that the neural network approach is not only a time-efficient way to implementing the VISSIM-CarSim-SSAM integrated simulation but also a superior alternative in assessing surrogate safety.

Fuel Consumption Comparison between Hydraulic Mechanical Continuously Variable Transmission and Stepped Automatic Transmission Based on the Economic Control Strategy

“…CarSim is a commercial software designed to study vehicle dynamics, and its vehicle models are widely recognized [37,38]. This paper takes the pickup truck model provided by CarSim as the research object.…”

An improved estimation method for vehicle velocity of distributed drive electric vehicle

“…In Carsim the driver model can be directly set to analyze the influence of different driving behaviour on vehicle. At the same time, it can reflect the vehicle's response to different road conditions and air conditions in real time, and reflect the driving state of vehicle [15][16][17]. However, the electric vehicle models have not yet been developed in Carsim , so it is necessary to add the in-wheel motors to its vehicle models.…”

Accident risk assessment and prediction using surrogate indicators and machine learning

“…Surrogate measures are widely utilised in TCT as an alternative to actual crash data for safety assessment (e.g. Zheng et al, 2014 (Chai and Wong, 2015a;So et al, 2015), but such studies were based mostly on simulation and designed experiments (Chai and Wong, 2014). In practice, FHWA developed a Surrogate Safety Assessment Model (SSAM) as a post processor to determine the number and severity of conflicts obtained from simulation packages (Sobhani et al, 2013).…”