Modeling crash severity by considering risk indicators of driver and roadway: A Bayesian network approach

Song, Yanchao; Kou, Siyuan; Wang, Chen

doi:10.1016/j.jsr.2020.11.006

Cited by 21 publications

(11 citation statements)

References 25 publications

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“…However, many of these studies are not inferential as the one performed here, but descriptive [ 8 , 9 , 25 , 26 , 27 , 28 ], so no statistical inference can be obtained, i.e., their results should not be extrapolated. Among the inferential studies, some of them consider all types of crashes [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ], which is not convenient to our purpose, as we are studying the crashes that can be avoided by “2+1” road transformation. Other interesting studies consider non geometric risk factors, such as driver behavior [ 10 ] or environmental conditions [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

“…Table 1 contains a non-exhaustive collection of contributions and findings. Some of these contributions come from descriptive studies [ 8 , 9 ], while others come from inferential ones, using different models and schemes [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. However, there are not so many results concerning “2+1” roads and they are mainly descriptive [ 25 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Geometric Risk Factors Affecting Head-On Collisions through Multiple Logistic Regression: Improving Two-Way Rural Road Design via 2+1 Road Adaptation

Cáceres

Fernández

Gordaliza

et al. 2021

IJERPH

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This study aims to characterize locations on two-way rural roads where head-on crashes are more likely to occur, attending to geometric road design factors. For this purpose, a case-control study was carried out using multiple logistic regression models with variables related to road design parameters, considering several scenarios. The dataset corresponding to cases (places where crashes have occurred) was collected on Spanish “1+1” rural roads over a four-year period. The controls (places where no crashes have occurred in the period) where randomly selected through a specific ad hoc designed method. The obtained model identifies risk factors and allows the computation of the odds of a head-on collision on any specific road section: width of the pavement (when it exceeds 6 m), width of the lanes (for intermediate widths between 3.25 and 3.75 m) and tight curves (less than 250 m of radius) are identified as factors significantly increasing the odds of a crash, whereas a paved shoulder is a protective factor. The identified configurations on two-way rural roads may be susceptible to transformation into “2+1” roads to decrease the odds of a head-on crash, thus preventing possible serious injuries and enhancing transportation safety.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Geometric Risk Factors Affecting Head-On Collisions through Multiple Logistic Regression: Improving Two-Way Rural Road Design via 2+1 Road Adaptation

Cáceres

Fernández

Gordaliza

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Several authors have discussed accident severity analysis during the last decade (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). Statistical models are typically used to address accident severity prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to the literature, several factors contribute to road-accident severity. These factors can be categorized as (1) infrastructure attributes, (2) weather conditions, (3) vehicle attributes, and (4) road user characteristics (6)(7)(8)(9)(10). Therefore, this study combined four datasets that contain road user information, vehicle characteristics, weather, and infrastructure attributes to capture all factors that contribute to accident severity.…”

mentioning

confidence: 99%

Efficient Histogram-Based Gradient Boosting Approach for Accident Severity Prediction With Multisource Data

Kashifi

Ahmad

2022

Transportation Research Record: Journal of the Transportation R

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Many people lose their lives in road accidents because they do not receive timely treatment after the accident from emergency medical services; providing timely emergency services can decrease the fatality rate as well as the severity of accidents. In this study, we predicted the severity of car accidents for use by trauma centers and hospitals for emergency response management. The predictions of our model could be used to decide whether an ambulance unit should be dispatched to the crash site or not. This study used histogram-based gradient boosting (HistGBDT), a modification of the gradient boosting (GBDT) classifier that accelerates the learning process and increases a model’s prediction power. The HistGBDT model was compared with seven state-of-the-art machine learning models: logistic regression, multilayer perceptron, random forest, extremely randomized trees, bagging, AdaBoost, and GBDT. The experiments were conducted on French accident data from 2005 to 2018. The HistGBDT model, with an overall accuracy of 82.5%, recall of 76.7%, and precision of 81.9%, outperformed other models. An analysis of feature importance indicated that safety equipment was the most important feature and vehicle category, department, localization, and region were other significant features. The Fβ measure (i.e., the weighted harmonic mean of recall and precision) was optimized with different weights on recall for the four best performing models to compare the tradeoff between the two crucial performance measures.

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“…Xu used a priori algorithm to analyse the main factors leading to road traffic accidents and their association relationships [26]. Song applied a Bayesian network to explicitly explore statistical associations between crash severity and significant variables [27]. In addition, grey comprehensive correlation degree, artificial neural network (ANN), random forests (RF), K-nearest neighbour (KNN), hidden Markov model (HMM), and other algorithms are also used to analyse spare parts [28], turn-back faults in urban rail [29], cause of traffic accident [30], human unsafe factors [31], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach Based on Historical Hazard Records for Supporting Risk Analysis in Complex Workplaces

Wang

Huang²,

Ying-ju³

et al. 2021

Mathematical Problems in Engineering

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Risk analysis, as an important prerequisite of risk management, is critical to reducing occupational injuries and other related losses. However, suffering greatly from incomplete hazard identification and inaccurate probability analysis, risk analysis is considered the weakest link in risk management, which seriously affects risk evaluation and control in complex workplaces. To improve the performance of hazard identification and analysis, a data-driven risk analysis approach is established, which consists of an improved equivalent class transformation (Eclat) algorithm, a sliding window model, and a change pattern mining algorithm. Through this approach, a large number of historical hazard records are transformed into association rules composed of object keywords and deviation keywords, and information such as potential keyword combinations, conditional probabilities of potential deviations, and the change pattern of potential hazards can be extracted. The function of the approach is threefold. Firstly, the data-driven risk analysis process is designed to identify the association rules between different hazard keywords. Secondly, Eclat algorithm is optimized to calculate the frequency and probability of potential hazards, which is conducive to improving the accuracy of probability estimation. Thirdly, the change pattern is developed to analyse the hazard change trend to support the cause analysis. A practical application in a Chinese hazardous chemical manufacturer is presented. Case studies have shown that the efficiency of the improved algorithm is increased by 13.68%, and 59.66% of potential hazards can be identified in advance, and relevant information can be extracted to support risk analysis.

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Modeling crash severity by considering risk indicators of driver and roadway: A Bayesian network approach

Cited by 21 publications

References 25 publications

Detection of Geometric Risk Factors Affecting Head-On Collisions through Multiple Logistic Regression: Improving Two-Way Rural Road Design via 2+1 Road Adaptation

Detection of Geometric Risk Factors Affecting Head-On Collisions through Multiple Logistic Regression: Improving Two-Way Rural Road Design via 2+1 Road Adaptation

Efficient Histogram-Based Gradient Boosting Approach for Accident Severity Prediction With Multisource Data

A Data-Driven Approach Based on Historical Hazard Records for Supporting Risk Analysis in Complex Workplaces

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