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

Abstract

Although crashes involving hazardous materials (HAZMAT) are rare events compared with other types of traffic crashes, they often cause tremendous loss of life and property, as well as severe hazards to the environment and public safety. Using five-year (2013–2017) crash data (N = 1610) from the Highway Safety Information System database, a two-step machine learning-based approach was proposed to investigate and quantify the statistical relationship between three HAZMAT crash severity outcomes (fatal and severe injury, injury, and no injury) and contributing factors, including the driver, road, vehicle, crash, and environmental characteristics. Random forest ranked the importance of risk factors, and then Bayesian networks were developed to provide probabilistic inference. The results show that fatal and severe HAZMAT crashes are closely associated with younger drivers (age less than 25), driver fatigue, violation, distraction, special roadway locations (such as intersections, ramps, and bridges), higher speed limits (over 66 mph), midnight and early morning (12:00–5:59 a.m.), head-on crashes, more than four vehicles, fire/explosion/spill, poor lighting conditions, and adverse weather conditions. The overall prediction accuracy of 85.8% suggests the effectiveness of the proposed method. This study extends machine learning applications in a HAZMAT crash analysis, which would help develop targeted countermeasures and strategies to improve HAZMAT road transportation safety.

The intelligent decision model for determine the best path of transportation on smart city using random forest algorithm and bayesian optimization (RF-BO)

“…Random Forest is used to predict classification and regression situations on large datasets. This approach involves combining results from each decision tree for classification as well as averaging the regression results [10], [18].…”

Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis

“…However, they usually require certain assumptions in the crash data, and it is challenging to discover the underlying crash patterns and the interaction among various variables. Data mining and machine learning approaches for truck-related crash data processing and analysis, including classification trees [23], taxicab correspondence analysis [24], association rules mining [25], and Bayesian networks [26][27][28], have become increasingly popular in recent years. For example, Das et al [24] utilized taxicab correspondence analysis to uncover the complex interactions between multiple risk factors and large truck-involved fatal crashes.…”

“…According to their findings, the most important attributes were packing and loading of HAZMAT, vehicle and facility-related factors, and human factors. Similarly, Ma et al [27] also utilized Bayesian networks to explore the most probable factors or the combination of factors leading to the crash-a recent HAZMAT crash severity prediction study [28] combined random forest and Bayesian networks. Random forest ranked the importance of risk factors, and Bayesian networks developed the probabilistic inference.…”

Analysis of Factors Affecting Road Transport Accidents of Hazardous Materials Based on PG-BN

“…At present, most scholars combine other methods to preprocess the data before modeling to efectively improve model accuracy. Hashemi et al [10] proposed a multivariate security analysis model based on copula BN, combined with the copula function to consider the correlation between variables, and illustrated the superior performance of the CBN model compared to the traditional BN model through a case; Huang et al [11] combined the explanatory structural model (ISM) and the BN model to construct a BN model for predicting hazardous materials road transport accidents after identifying causal relationships between variables; Sun et al [12] used random forests to rank the importance of risk factors for hazardous materials transport and then developed a BN to provide probabilistic inference, and the results showed that the proposed method was very efcient; Shen et al [13] used fault tree analysis to frst fnd the direct and indirect causes of hazardous transport accidents and constructed a BN model with strong descriptive power; Ding et al [14] combined the credal network and IDM methods to construct a model for analyzing the causes of hazardous materials road transport accidents based on a credal network; Pan et al [15] proposed a risk assessment method based on an improved FBN model, which provides an efective tool for risk management of hazardous material transportation enterprises; Wang et al [16] frst used the grounded theory (GT) to identify infuencing factors and then developed a BN-based model for these collected hazardous materials transport accidents; and Cheng et al [17] used dynamic BN to calculate the likelihood of hazardous chemical spills and explosions. In conclusion, it can be seen that before constructing the BN model, it is important to efectively deal with the relationship among the factors afecting hazardous materials road transport accidents to improve the accuracy and robustness of the model.…”