Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks

Zhu, Lian; Lu, Linjun; Zhang, Wenying; Zhao, Yurou; Song, Meining

doi:10.3390/su11082223

Cited by 34 publications

(19 citation statements)

References 47 publications

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“…Approximately 28 percent of total crashes occurred on horizontal curve segments, which are the second most common place for road crashes [4]. Even though more than half of total crashes happen on straight road segments, crashes on horizontal curves lead to more severe injuries owing to the complicated linear roadway conditions, unstable traffic speed, and dangerous driving conditions on curve segments referred to studies of Zhu et al [5] and Sustainability 2021, 13, 9011 2 of 18 Shi et al [6]. Moreover, previous studies by Hummer et al [7] and FHWA [8] found that the fatality rate from road crashes is three times higher on horizontal curves than other road segments, particularly with narrow lanes.…”

Section: Introductionmentioning

confidence: 99%

Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand

2021

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The number of road crashes continues to rise significantly in Thailand. Curve segments on two-lane rural roads are among the most hazardous locations which lead to road crashes and tremendous economic losses; therefore, a detailed examination of its risk is required. This study aims to develop crash prediction models using Safety Performance Functions (SPFs) as a tool to identify the relationship among road alignment, road geometric and traffic conditions, and crash frequency for two-lane rural horizontal curve segments. Relevant data associated with 86,599 curve segments on two-lane rural road networks in Thailand were collected including road alignment data from a GPS vehicle tracking technology, road attribute data from rural road asset databases, and historical crash data from crash reports. Safety Performance Functions (SPFs) for horizontal curve segments were developed, using Poisson regression, negative binomial regression, and calibrated Highway Safety Manual models. The results showed that the most significant parameter affecting crash frequency is lane width, followed by curve length, traffic volume, curve radius, and types of curves (i.e., circular curves, compound curves, reverse curves, and broken-back curves). Comparing among crash prediction models developed, the calibrated Highway Safety Manual SPF outperforms the others in prediction accuracy.

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Section: Introductionmentioning

confidence: 99%

Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand

2021

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“…Many studies have found that accident frequency is affected by the segment length [21,24,29,32], lane width [29,40], lane numbers [27,32,38], carriageway width [21,29,41], and the shoulder width and type [29,42]. Significant attention has been dedicated to horizontal and vertical road geometric elements, which are found to affect the accident frequency [28,37,43,44]. Shankar et al [45] revealed that an increase in the number of horizontal curves at a road section leads to an increase in accident frequency.…”

Section: Factors Affecting the Accident Frequencymentioning

confidence: 99%

Accident Frequency Prediction Model for Flat Rural Roads in Serbia

Micic¹,

Amidžić

Damjanović

et al. 2022

Sustainability

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Traffic accidents, by their nature, are random events; therefore, it is difficult to estimate the exact places and times of their occurrences and the true nature of their impacts. Although they are hard to precisely predict, preventative actions can be taken and their numbers (in a certain period) can be approximately predicted. In this study, we investigated the relationship between accident frequency and factors that affect accident frequency; we used accident data for events that occurred on a flat rural state road in Serbia. The analysis was conducted using five statistical models, i.e., Poisson, negative binomial, random effect negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. The results indicated that the random effect negative binomial model outperformed the other models in terms of goodness-of-fit measures; it was chosen as the accident prediction model for flat rural roads. Four explanatory variables—annual average daily traffic, segment length, number of horizontal curves, and access road density—were found to significantly affect accident frequency. The results of this research can help road authorities make decisions about interventions and investments in road networks, designing new roads, and reconstructing existing roads.

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“…The results stated factors such as point of impact, site of location, accident side of road, and alcohol/drugs condition. They are very important in crashes in horizontal curves [20]. In 2018, Zhenning Li et al examined the severity of driver injuries at intersections, using cluster analysis and hierarchical Bayesian models.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Factors Affecting the Severity of Single-Vehicle Crashes on Urban Roads using Bayesian Binary Probit Regression

Asil¹,

Bargegol²

2022

CRPASE

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Traffic crashes are a global challenge, and many people lose their lives in crashes every year. Single-vehicle accidents have high injury and death rates and are therefore a major concern. In this study, the effect of various factors on the severity of this type of crash on urban roads of Rasht, in Iran, has been investigated. Rasht is the capital of Guilan province and is one of the cities that many tourists visit every year. Investigating Single-vehicle crashes and predicting the factors affecting them facilitates the possibility of prevention and provision of medical facilities. In this paper, a Bayesian Binary Probit Regression model was used for this purpose. The results of this study showed that Single-vehicle crashes during non-peak traffic hours, which are from 21 to 6 o'clock, increase the severity of the collision and also the results stated that spring and summer seasons, Iranian-made passenger cars and motorcycles, male drivers, and inability to control vehicles increase the severity of a single-vehicle crash.

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Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks

Cited by 34 publications

References 47 publications

Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand

Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand

Accident Frequency Prediction Model for Flat Rural Roads in Serbia

Investigating the Factors Affecting the Severity of Single-Vehicle Crashes on Urban Roads using Bayesian Binary Probit Regression

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