Interactive risk analysis on crash injury severity at a mountainous freeway with tunnel groups in China

Huang, Helai; Peng, Yunying; Wang, Jie; Luo, Qizhang; Li, Xiang

doi:10.1016/j.aap.2017.11.024

Cited by 68 publications

(42 citation statements)

References 21 publications

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“…A CC in a freeway tunnel had a 19.4% higher propensity to be severe than that occurring on open freeways (coefficient = 2.47, elasticity = 19.4). In its nature, a freeway tunnel possesses traffic hazards given its constraint driving space and tedious driving environment [ 48 , 49 ]. Poor lighting and fatigued driving in a tunnel are also often considered as potential hazards causing crashes, especially severe ones [ 50 , 51 ].…”

Section: Discussionmentioning

confidence: 99%

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

Meng

Song

et al. 2020

IJERPH

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A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers.

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Section: Discussionmentioning

confidence: 99%

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

Meng

Song

et al. 2020

IJERPH

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show abstract

“…When elementary structures are sufficiently close, unique road sections with composite structures are formed by a similar or heterogeneous combination such as tunnel groups, bridge groups, and bridge-tunnel groups. Although existing research has provided definitions for these unique road sections [10,15,23], most of them lack clear quantitative standards and bases, and a uniform view has not yet been achieved. The root of the problem lies in how the distance between structures can be determined and how these structures can be turned into a composite road section in accordance with the actual conditions.…”

Section: Plos Onementioning

confidence: 99%

“…However, this research has limited applicability to mountainous freeways with high bridge and tunnel ratios. Huang et al [10] employed a classification and regression tree model to analyze the interactive risks related to serious car crash injuries on mountainous freeways with tunnels and found that although driving behavior, crash time, grade, curve radius, and vehicle type were significant factors, severe crashes mostly occurred owing to a combination of effects including weather and crash location. Duan et al [11] analyzed road traffic crashes on the Yuxiang freeway and found that bridge and tunnel ratios, annual average daily traffic (AADT), and road length were the main factors affecting the number of crashes.…”

Section: Introductionmentioning

confidence: 99%

Crash analysis of mountainous freeways with high bridge and tunnel ratios using road scenario-based discretization

Sun

Liu

et al. 2020

PLoS ONE

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Mountainous freeways with high bridge and tunnel ratios are a new type of road that rarely contain many special road sections formed by various structures. The crash characteristics of the road are still unclear, but it also provides conditions for studying how various road environments affect traffic. In view of the various structures and differences in the driving environments, a scenario-based discretization method for such a road was established. The traffic-influence areas of elementary and composite structures were proposed and defined. Actual data were analyzed to investigate the crash patterns in an entire freeway and in each special section through statistical and comparative research. The results demonstrate the applicability and validity of this method. The crash rates were found to be the highest in interchange and service areas, lower in ordinary sections, and the lowest in tunnels, being mostly attributed to collisions with fixtures. The crash severity on bridges and bridge groups was significantly higher than that on the other types of road sections, being mostly attributed to single-vehicle crashes. The annual average daily traffic and driving adaptability were found to be related to crashes. The findings shed some light on the road design and traffic management implications for strengthening the traffic safety of mountainous freeways.

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“…Besides all statistical approaches, data mining and machine learning techniques have also been employed for analyzing and explaining crash data. Huang et al examined the interactive effect of mountainous freeway alignment, driving behaviors, vehicle characteristics and environmental factors on crash severity using a classification and regression tree model [21]. Osman et al proposed a bi-level hierarchical classification methodology to identify different types of secondary tasks that drivers are engaged in using their driving behavior parameters [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches

Wang

2019

PLoS ONE

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Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers’ performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California’s Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles.

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Interactive risk analysis on crash injury severity at a mountainous freeway with tunnel groups in China

Cited by 68 publications

References 21 publications

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

Crash analysis of mountainous freeways with high bridge and tunnel ratios using road scenario-based discretization

Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches

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