An Alternative Method for Traffic Accident Severity Prediction: Using Deep Forests Algorithm

Abstract

Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models. Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the first time by a research team of Nanjing University. This algorithm was proved to be more accurate and robust in comparison with other machine learning algorithms. Motivated by this benefit, this study employs the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm. To verify the superiority of our proposed method, several other machine learning algorithm-based perdition models were implemented to predict traffic accident severity with the same dataset, and the prediction results show that the Deep Forests algorithm present good stability, fewer hyper-parameters, and the highest accuracy under different level of training data volume. It is expected that the findings from this study would be helpful for the establishment or improvement of effective traffic safety system within a sustainable transportation system, which is of great significance for helping government managers to establish timely proactive strategies in traffic accident prevention and effectively improve road traffic safety.

Prediction of Traffic Accident Severity Based on Random Forest

“…Compared with the predicted results, Gan et al [25] predicted that the accuracy rate was 75% when the data was 10,000. Te model of this study achieved 80% prediction results when the data was 2800.…”

“…Te fnal prediction results show that the trafc accident factors proposed in this paper have superior performance in predicting the severity of trafc accidents. In terms of trafc accident characteristics, Gan et al [25] selected 8 trafc accident data features by random forest algorithm to predict the degree of trafc accident validation. Including engine capacity, hour of day, the age of the vehicle, the month of the year, day of week, age band of drivers, vehicle maneuver, and speed limit.…”

Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data

“…Although conventional statistical methods have been proven effective in examining the relationships between HAZMAT crash severity and explanatory variables, they cannot reveal the underlying patterns and interplay of various factors [ 16 ]. In recent years, machine learning techniques, such as Bayesian networks [ 16 , 17 , 18 , 19 , 20 ], clustering [ 21 , 22 ], support vector machines [ 23 ], decision trees [ 4 , 24 ], random forests [ 25 , 26 ], and association mining rules [ 27 , 28 ] have been widely used for crash data analysis. These non-parameter approaches do not require assumption among explanatory variables, and they have been identified as having greater flexibility in supporting in-depth crash analysis and safety decision-making.…”

An Improved Particle Swarm Optimization for Prediction of Accident Severity

“…In sustainable transportation system, traffic accident prediction and safety assessment are an important factor. Gan et al [21] considered the aforementioned issue of sustainable transportation system and developed a prediction model by considering the deep forest algorithm. Several other ML techniques are also adopted for comparing the simulation results of deep forest technique.…”