Analysis of Crash Severity for Hazard Material Transportation Using Highway Safety Information System Data

Song, Xiuguang; Wu, Jianqing; Pi, Rendong

doi:10.1177/2158244020939924

Cited by 9 publications

(11 citation statements)

References 28 publications

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“…Random Forest (RF), Support Vector Machine (SVM), and Back-Propagation Neural Network (BPNN) had all been widely and successfully used in predicting the possibility of injury-severity outcome [ 25 , 26 , 27 , 28 ]. Choosing a suitable method for the crash prediction is critical.…”

Section: Methodsmentioning

confidence: 99%

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

Song

et al. 2021

IJERPH

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Multi-vehicle (MV) crashes, which can lead to great damages to society, have always been a serious issue for traffic safety. A further understanding of crash severity can help transportation engineers identify the critical reasons and find effective countermeasures to improve transportation safety. However, studies involving methods of machine learning to predict the possibility of injury-severity of MV crashes are rarely seen. Besides that, previous studies have rarely taken temporal stability into consideration in MV crashes. To bridge these knowledge gaps, two kinds of models: random parameters logit model (RPL), with heterogeneities in the means and variances, and Random Forest (RF) were employed in this research to identify the critical contributing factors and to predict the possibility of MV injury-severity. Three-year (2016–2018) MV data from Washington, United States, extracted from the Highway Safety Information System (HSIS), were applied for crash injury-severity analysis. In addition, a series of likelihood ratio tests were conducted for temporal stability between different years. Four indicators were employed to measure the prediction performance of the selected models, and four categories of crash-related characteristics were specifically investigated based on the RPL model. The results showed that the machine learning-based models performed better than the statistical models did when taking the overall accuracy as an evaluation indicator. However, the statistical models had a better prediction performance than the machine learning models had considering crash costs. Temporal instabilities were present between 2016 and 2017 MV data. The effect of significant factors was elaborated based on the RPL model with heterogeneities in the means and variances.

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Section: Methodsmentioning

confidence: 99%

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

Song

et al. 2021

IJERPH

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“…Statistical analyses and regression models are the most commonly used approaches to identify the crash characteristics. These methods include crash survey and descriptive analyses [ 7 , 8 , 9 ], Poisson regression models [ 10 ], fixed and random parameters ordered probit models [ 11 , 12 ], ordered logistic models [ 13 , 14 ], and mixed logit models [ 15 ]. For example, Oggero et al [ 7 ] investigated 1932 HAZMAT transportation crashes by road and rail from the beginning of the 20th century to 2004.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Sun

Zhou

Jiao

et al. 2022

IJERPH

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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.

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“…This paper used a data processing procedure introduced by Wu to process the roadside LiDAR data [31]. The data processing algorithms developed by Wu were further improved by other researchers [32][33][34][35]. This part briefly introduced the major parts of the data processing procedure.…”

Section: Lidar Data Processingmentioning

confidence: 99%

Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network

Zhang

Ma³

et al. 2021

Electronics

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Object classification is important information for different transportation areas. This research developed a probabilistic neural network (PNN) classifier for object classification using roadside Light Detection and Ranging (LiDAR). The objective was to classify the road user on the urban road into one of four classes: Pedestrian, bicycle, passenger car, and truck. Five features calculated from the point cloud generated from the roadside LiDAR were selected to represent the difference between different classes. A total of 2736 records (2062 records for training, and 674 records for testing) were manually marked for training and testing the PNN algorithm. The data were collected at three different sites representing different scenarios. The performance of the classification was evaluated by comparing the result of the PNN with those of the support vector machine (SVM) and the random forest (RF). The comparison results showed that the PNN can provide the results of classification with the highest accuracy among the three investigated methods. The overall accuracy of the PNN for object classification was 97.6% using the testing database. The errors in the classification results were also diagnosed. Discussions about the direction of future studies were also provided at the end of this paper.

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Analysis of Crash Severity for Hazard Material Transportation Using Highway Safety Information System Data

Cited by 9 publications

References 28 publications

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

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

Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network

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