Structural equations modeling of real-time crash risk variation in car-following incorporating visual perceptual, vehicular, and roadway factors

Ding, Naikan; Jiao, Nisha; Zhu, Shunying; Liu, Bing

doi:10.1016/j.aap.2019.105298

Cited by 31 publications

(24 citation statements)

References 41 publications

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“…In these efforts, individual driving behaviors are without a doubt the unparalleled contributive factors to be investigated, because an inspection of the behavioral nature of moving vehicles and drivers is always worth being the first choice. In addition, speeding [13], insufficient headway [14], biased visual perception [14], and other relevant human factors [15][16][17] were found to be the predominant behavioral factors. A variety of nonbehavioral factors, i.e., external conditions, were also considered to approximate crash or crash risk, such as roadway feature, weather (visibility) condition, traffic volume, driver individual characteristics, and vehicle feature (see eofilatos and Yannis [5]; Mannering et al [3]; and Papadimitriou et al [4] for systematic reviews).…”

Section: Introductionmentioning

confidence: 99%

“…Further, previous studies did not particularly differentiate the traffic flow states in analyzing rear-end crashes or crash risk. However, rear-end crashes extensively proved to be closely related to car-following behavior [14,[21][22][23][24][25][26], and car-following is always supposed to be a driving state right before the occurrence of a rear-end crash. erefore, in this regard, the rear-end crashes or crash risk might be misunderstood in the absence of specific car-following behavior and car-following vehicle flow (car-following platoon).…”

Section: Introductionmentioning

confidence: 99%

“…Following the above rationale, in this study, we attempt to reason rear-end crash risk and define car-following behavior by incorporating some specific traffic flow variables (flow-level data) and to evaluate the performance with the specific flow-level information. Generally, this was achieved by extending a prior study, which tentatively specified crash risk variation with visual perceptual, vehicular, and roadway factors under the presence of a kind of perceptual markings [14]. In the previous study, the rearend crash risk variation was associated with the above factors, with drivers' distance risk perception (DRP) and speed risk perception (SRP) being a bridge, and was eventually specified by two extended surrogate safety indicators based on modified time-to-collision (mTTC) and deceleration rate to avoid collision (DRAC).…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the flow-level data used in this study included the flow rate (FR 10 ) and heavy vehicle proportion (r H ) defined in specific 10 min, platoon length (l p ) measured with the number of vehicles involved, and a flow-level risk indicator of platoon crash risk entropy (PCRE), as suggested in Ding et al [22]. Besides, the data collected on another freeway was utilized to examine the spatial transferability of the BN models trained with data sampled from the freeway presented in Ding et al [14]. e framework of this study is structured as in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

Ding

Jiao

2021

Discrete Dynamics in Nature and Society

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Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was proposed to capture the association between drivers’ visual perception, traffic flow uncertainty, and rear-end crash risk variation. Results show that (1) the BN model with flow-level data outperformed the BN model without flow-level data and could predict 85.3% of the cases of crash risk decrease, with a false alarm rate of 21.4%; (2) the hierarchical hybrid BN models showed plausible spatial transferability in predicting crash risk variation; and (3) the incorporation of specific flow-level variables and data greatly benefited the successful identification of rear-end crash risk variations. The findings of this study suggest that rear-end crash risk is inherently associated with both individual driving behaviors and traffic flow uncertainty, and appropriate visual perceptual information could compensate for crash risk and improve safety.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

Ding

Jiao

2021

Discrete Dynamics in Nature and Society

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“…Kurşunoğlu ve Önder[29], kömür ve gaz patlamalarının incelenmesinde, Jabeen vd [30]. yenilenebilir enerji alanında, Viloria ve Pineda Lezama[31], Latin Amerika'da üniversite öğrencilerinin okuldan ayrılmalarının sebeplerinin incelenmesinde ve Ding vd [32]. ise araç takibinde gerçek zamanlı çarpışma risklerinin incelenmesinde YEM'i kullanmışlardır.YEM kavramının iki önemli özelliği, çalışılan süreç bir seri yapısal eşitlik (regresyon eşitlikleri) içermesi ve bu yapısal eşitliklerin hipotezin daha kolay anlaşılabilmesi için görsel olarak çizilebilmesidir.…”

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Endüstri 4.0’ı etkileyen kriterlerin yapısal eşitlik modeli ile incelenmesi ve bir pilot uygulama

Kiraz

Canpolat

Özkurt

et al. 2020

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Investigation of Industry 4.0 level of businesses with Structural Equation Model  Determining the weights of the criteria affecting the Industry 4.0 level  A model to guide businesses in identifying digital transformation roadmaps Figure A. Flow chart of the studyPurpose: To determine the criteria that affect the concept of Industry 4.0 and to guide the measurement models of Industry 4.0 to be developed based on these criteria. The aim of the study is to help enterprises to catch up with the 4th industrial revolution by using calculated weights and to support them to prepare appropriate digital transformation road maps. Theory and Methods:The following 8 criteria were used in the model: technology, strategy, customers, leadership, government policy, culture, processes and employees. In the model prepared within the scope of the study, the Structural Equality Model (YEM) was used to examine the effects of the criteria on each other and on the model. YEM is a statistical model used to determine direct or indirect relationships between observed or unobserved variables. Confirmatory Factor Analysis (DFA) was used in the study to determine whether the criteria affecting the Industry 4.0 were compatible with the model. DFA helps to eliminate incompatible criteria by testing whether the generated models are validated on the dataset. Results:The criterion, which has the highest factor load, is the strategy (1.00). Then there are processes with 0.97 culture and 0.90 factor load. The 4th criterion is employees with a factor load of 0.84. When we examine the model, technology (0.77), state policy (0.66) and leadership (0.57) come from these criteria. Conclusion:The first thing that should be done by companies that want to make a successful transition to Industry 4.0 is to analyze their current situation well, to determine their strengths and weaknesses and then to create a suitable digital transformation roadmap. When the literature is examined, it is seen that the number of studies that determine the relations between criteria or the criteria affecting the Industry 4.0 concept is very small. In this study, a model has been established to determine the criteria of Industry 4.0 and the degree of impact of the criteria affecting the Industry 4.0 level is measured. As a result of the study, companies can identify the missing sides and focus on the aspects that need to be developed. It is intended that the study be used as a reference for creating a digital transformation roadmap before making large investment decisions.

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Safety Assessment and Risk Management of Urban Arterial Traffic Flow Based on Artificial Driving and Intelligent Network Connection: An Overview

Pei,

Hou

2024

Arch Computat Methods Eng

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Structural equations modeling of real-time crash risk variation in car-following incorporating visual perceptual, vehicular, and roadway factors

Cited by 31 publications

References 41 publications

Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

Endüstri 4.0’ı etkileyen kriterlerin yapısal eşitlik modeli ile incelenmesi ve bir pilot uygulama

Safety Assessment and Risk Management of Urban Arterial Traffic Flow Based on Artificial Driving and Intelligent Network Connection: An Overview

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