Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data
Journal
Accident Analysis and Prevention
Journal Volume
150
Date Issued
2021
Author(s)
Abstract
Traffic violations and improper driving are behaviors that primarily contribute to traffic crashes. This study aimed to develop effective approaches for predicting at-fault crash driver frequency using only city-level traffic enforcement predictors. A fusion deep learning approach combining a convolution neural network (CNN) and gated recurrent units (GRU) was developed to compare predictive performance with one econometric approach, two machine learning approaches, and another deep learning approach. The performance comparison was conducted for (1) at-fault crash driver frequency prediction tasks and (2) city-level crash risk prediction tasks. The proposed CNN-GRU achieved remarkable prediction accuracy and outperformed other approaches, while the other approaches also exhibited excellent performances. The results suggest that effective prediction approaches and appropriate traffic safety measures can be developed by considering both crash frequency and crash risk prediction tasks. In addition, the accumulated local effects (ALE) plot was utilized to investigate the contribution of each traffic enforcement activity on traffic safety in a scenario of multicollinearity among predictors. The ALE plot illustrated a complex nonlinear relationship between traffic enforcement predictors and the response variable. These findings can facilitate the development of traffic safety measures and serve as a good foundation for further investigations and utilization of traffic violation data. ? 2020 Elsevier Ltd
Subjects
Accident prevention; Civil defense; Law enforcement; Recurrent neural networks; Regression analysis; Risk assessment; Traffic control; Convolution neural network; Effective approaches; Frequency prediction; Non-linear relationships; Performance comparison; Prediction accuracy; Predictive performance; Traffic safety measures; Forecasting; article; convolutional neural network; deep learning; gated recurrent unit network; prediction; response variable; traffic safety
Other Subjects
Accident prevention; Civil defense; Law enforcement; Recurrent neural networks; Regression analysis; Risk assessment; Traffic control; Convolution neural network; Effective approaches; Frequency prediction; Non-linear relationships; Performance comparison
Type
journal article