Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

Jamal, Arshad; Umer, Waleed

doi:10.3390/ijerph17207466

Cited by 37 publications

(15 citation statements)

References 103 publications

(118 reference statements)

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“…The situation analysis of the Road Safety Action Program examines that human-related issues cause most of the road accidents; thus, controlling them becomes the extremely dynamic goal of road safety actions [4][5][6]. Previous findings detected the human factors to be a primary or leading contributing cause in approximately 90% of road traffic crashes [7][8][9][10][11][12]. In addition, the study observed that driving behavior is one of the basic driver-related components that directly affect road safety [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model

Farooq

Moslem

Jamal

et al. 2021

IJERPH

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Frequent lane changes cause serious traffic safety concerns for road users. The detection and categorization of significant factors affecting frequent lane changing could help to reduce frequent lane-changing risk. The main objective of this research study is to assess and prioritize the significant factors and sub-factors affecting frequent lane changing designed in a three-level hierarchical structure. As a multi-criteria decision-making methodology (MCDM), this study utilizes the analytic hierarchy process (AHP) combined with the best–worst method (BWM) to compare and quantify the specified factors. To illustrate the applicability of the proposed model, a real-life decision-making problem is considered, prioritizing the most significant factors affecting lane changing based on the driver’s responses on a designated questionnaire survey. The proposed model observed fewer pairwise comparisons (PCs) with more consistent and reliable results than the conventional AHP. For level 1 of the three-level hierarchical structure, the AHP–BWM model results show “traffic characteristics” (0.5148) as the most significant factor affecting frequent lane changing, followed by “human” (0.2134), as second-ranked factor. For level 2, “traffic volume” (0.1771) was observed as the most significant factor, followed by “speed” (0.1521). For level 3, the model results show “average speed” (0.0783) as first-rank factor, followed by the factor “rural” (0.0764), as compared to other specified factors. The proposed integrated approach could help decision-makers to focus on highlighted significant factors affecting frequent lane-changing to improve road safety.

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Section: Introductionmentioning

confidence: 99%

Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model

Farooq

Moslem

Jamal

et al. 2021

IJERPH

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“…given in ( 6) and randomized quantile residuals (qr) expressed in (7). Further, estimate the mean and standard error of the dr and qr.…”

Section: Algorithm For Charting Constantsmentioning

confidence: 99%

GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance

et al. 2021

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Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals.

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“…Injury Severity. So far, many research methods have been developed to investigate risk analysis and prediction in previous studies [15][16][17], and statistical regression approaches and methods based on machine learning (ML) have been the primary method on investigating the relationship between crash injury severity and risk factors [18,19]. For example, Xie et al developed a random-parameter ordered probit model to explore risk factors with crash severity on two-lane rural roads in China [20].…”

Section: Relevant Studies On the Modelling Approach For Crashmentioning

confidence: 99%

Exploring Factors Contributing to Crash Injury Severity in the Rural-Urban Fringe of the Central City

Wang

Liu

Dong³

et al. 2021

Journal of Advanced Transportation

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To analyze the risk factors influencing the crash injury severity in rural-urban fringes, crash data in rural-urban fringes were collected from Harbin, China. Four risk factors, namely, time of day, vehicle type, road feature, and crash type, were investigated associated with the severity of rural-urban fringe crashes. The crash injury severity was divided into two categories, including fatal and nonfatal crash. The logistic regression was applied to explore the relationships between the severity outcomes and time of day, vehicle type, road feature, and crash type. The test methods of goodness-of-fit and badness-of-fit are conducted to examine the validity of estimation results. The results show considerable matching of the number of different crash types between calculated results and actual data. Compared with the other influencing factors, the time of day is significant factor for crash injury severity based on the study. As such, the proposed calibration procedure and the factors of choice are recommended as a validated approach to analyze and identify the main factors influencing crash injury severity in rural-urban fringes.

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Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

Cited by 37 publications

References 103 publications

Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model

Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model

GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance

Exploring Factors Contributing to Crash Injury Severity in the Rural-Urban Fringe of the Central City

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