Exploring factors affecting the severity of night-time vehicle accidents under low illumination conditions

Liu, Jing; Li, Jingyu; Wang, Kun; Zhao, Jianyou; Cong, Haozhe; He, Ping

doi:10.1177/1687814019840940

Cited by 31 publications

(10 citation statements)

References 24 publications

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“…The probability of a more severe collision between vehicle–pedestrian/cyclists is 7.508 times that of a vehicle–vehicle collision, which is consistent with the results of previous studies [ 34 , 35 , 36 ]. Pedestrians or cyclists are extremely vulnerable to injury or even death in the event of a collision because they lack protective equipment.…”

Section: Discussionsupporting

confidence: 91%

Exploring Influential Factors Affecting the Severity of Urban Expressway Collisions: A Study Based on Collision Data

Wang

Feng

2022

IJERPH

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When traffic collisions occur on urban expressways, the consequences, including injuries, the loss of lives, and damage to properties, are more serious. However, the existing research on the severity of expressway traffic collisions has not been deeply explored. The purpose of this research was to investigate how various factors affect the severity of urban expressway collisions. The severity of urban expressway collisions was set as the dependent variable, which could be divided into three categories: slight collisions, severe collisions, and fatal collisions. Ten variables, including individual characteristics, collision characteristics, and road environment conditions, were selected as independent factors. Based on 975 valid urban expressway collisions, an ordered logistic regression model was established to evaluate the impacts of influence factors on the severity of these crashes. The results show that gender, collision modality, road pavement conditions, road surface conditions, and visibility are significant factors that affect the severity of urban expressway collisions. Females were more likely to be involved in more severe urban expressway collisions than males. For collisions involving pedestrians and non-motorized vehicles, the risk of more severe injury was 7.508 times higher than that associated with vehicle–vehicle collisions. The probability of more severe collisions on urban expressways with poor pavement conditions and wet surface conditions is greater than that on urban expressways with good pavement conditions and dry surface conditions. In addition, as visibility increases, the probability of more severe collisions on urban expressways gradually decreases. These results provide more effective strategies to reduce casualties as a result of urban expressway collisions.

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Section: Discussionsupporting

confidence: 91%

Exploring Influential Factors Affecting the Severity of Urban Expressway Collisions: A Study Based on Collision Data

Wang

Feng

2022

IJERPH

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“…Some limitations may exist in predicting the result by the linear regression model due to the binary response variables frequently involved in traffic behavior. e logistics regression has been widely applied and proven to be successful to model traffic safety research, such as evaluating the contributing factors for vehicle accident [31], especially in examination of risk factors involved in red-light running and yellow-light running behavior [7,8,11,16]. In our model, Y � 1 denoted yellow-light running behavior of e-bike riders and Y � 0 denoted that riders stopped their riding behavior in the yellow-light interval.…”

Section: Modeling Ridermentioning

confidence: 99%

Exploring Factors Affecting the Yellow-Light Running Behavior of Electric Bike Riders at Urban Intersections in China

Cai

Zhao

Liu

et al. 2020

Journal of Advanced Transportation

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Electric bikes play an important role in the urban transportation system in China. Yellow-light running behavior of riders is one of the most critical factors for e-bike riders involved in traffic crashes at intersection. The main purpose of this study is to explore how a variety of factors affect e-bike riders’ yellow-light running behaviors at intersection by a field observation conducted in Xi’an, China. Based on 396 e-bike riders who faced yellow-light samples, two analytical methods, the principle component analysis logistics model and a base logistics model, were employed to evaluate the impacts of contributing factors on e-bike riders’ yellow-light running behavior. The modeling results showed that seven variables significantly affect the e-bike riders’ yellow-light running behavior, which were the approaching speed of e-bike, the distance to stop line, riders’ age and gender attributes, type of e-bike, and the characteristics of intersection including the width of intersection and the existence of physical barriers. This study can provide valuable insights into understanding e-bike riders’ yellow-light running behavior and may also help decision makers propose countermeasures to reduce e-bike rider-related crashes at intersection.

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“…Logistic regression is an appropriate method to model a binary dependent variable as a function of predictors. It has been applied to model a wide variety of transportation and pavement related data, including traffic accidents, pavement fatigue cracking, pavement patching effect, and selection of preservation projects [22][23][24][25][26]. In this research, the goal of logistic regression is to describe how the predictors influence the probability that M&R will be required.…”

Section: Logistic Regressionmentioning

confidence: 99%

Factors Affecting Maintenance Probability and Resurfacing Thickness Based on the Pavement Management System

Jiang

2020

Advances in Civil Engineering

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In order to investigate the key factors and analyze their effects on maintenance and rehabilitation (M&R) strategies, data for 2495 pavement sections were collected from the pavement management system (PMS), including pavement performance data, traffic data, material property data, and M&R record data. Logistic regression was first employed to explore the influential factors on maintenance probability. Afterward, the classification tree model was established to find out the key factors on resurfacing thickness. Results showed that road sections with higher IRI, rutting depth (RD), deterioration rate of surface friction coefficient (DRSFC), pavement patching ratio (PPR), and transverse cracking severity index (TCSI) before treatment had significantly higher maintenance probability, which could be quantified by the developed logistic model. Moreover, treatments implemented on bridge decks tended to have greater resurfacing thickness. For pavement M&R projects, with the tensile strength ratio (TSR) of top layer materials higher than 88.7% and pretreatment SFC higher than 49, the resurfacing thickness would be thinner. For bridge M&R projects, middle layer TSR higher than 88.3% led to thinner overlays, and much thinner resurfacing thickness can be observed if pretreatment RD was less than 8.72 mm. When middle layer TSR was lower than 88.3% and pretreatment IRI was higher than 2.383 m/km with larger AESAL, the resurfacing thickness would probably be the thickest. The two models built in this paper provided probabilistic estimation of maintenance probability and explored key factors together with their critical split points for resurfacing thickness, which could be regarded as an alternative decision-making tool for pavement engineers.

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Exploring factors affecting the severity of night-time vehicle accidents under low illumination conditions

Cited by 31 publications

References 24 publications

Exploring Influential Factors Affecting the Severity of Urban Expressway Collisions: A Study Based on Collision Data

Exploring Influential Factors Affecting the Severity of Urban Expressway Collisions: A Study Based on Collision Data

Exploring Factors Affecting the Yellow-Light Running Behavior of Electric Bike Riders at Urban Intersections in China

Factors Affecting Maintenance Probability and Resurfacing Thickness Based on the Pavement Management System

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