Analysis of traffic accident size for Korean highway using structural equation models

Lee, Jun-Young; Chung, Jin-Hyuk; Son, Bongsoo

doi:10.1016/j.aap.2008.08.006

Cited by 133 publications

(90 citation statements)

References 10 publications

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“…Figure 1 shows a simple schematic diagram of SEM along with the various kinds of variables. The equations of SEM are specified by direct links between these variables, which can be called "the simultaneous equations" [30]. SEM is defined using three sets of equations: structural equations, and measurement equations for both the endogenous and exogenous variables [31].…”

Section: Structural Equation Modelingmentioning

confidence: 99%

Influencing Mechanism of Potential Factors on Passengers’ Long-Distance Travel Mode Choices Based on Structural Equation Modeling

et al. 2017

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Understanding the public transportation users' preferences to long-distance travel modes would contribute to reasonable developing policies and resource allocation. This paper aims to explore the influencing mechanism of potential factors on the long-distance travel mode choice. A survey was conducted to collect the data. The analysis of variance (ANOVA) approach was applied to analyze the correlation relationship between potential factors and travel mode choice behavior. The results showed that, except gender, service demand for safety and departure time, all of the other factors significantly influenced the travel mode choice behavior. Specifically, passengers with higher education level and income level were more likely to choose high-speed railway (HSR) and plane; passengers caring about travel expense were more likely to choose ordinary train, whereas plane and HSR may be chosen more by passengers caring more about comfort, punctuality and efficiency; the more passengers were satisfied with travel modes' service performance, the more they would be likely to choose them; the most competitive distance ranges for coach, ordinary train, HSR and plane were below 500 km, 500-1000 km, 500-1500 km and over 1500 km, respectively. Besides, the structural equation modeling (SEM) technique was applied to investigate the influencing mechanism of factors on the long-distance travel mode choice. The results revealed that travel distance was the most significant variable directly influencing passengers' mode choices, followed by the service demand, performance evaluation, and personal attributes. Furthermore, personal attributes were verified to have an indirect effect on travel mode choice behavior by significantly affecting the service demand and performance evaluation.

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Section: Structural Equation Modelingmentioning

confidence: 99%

Influencing Mechanism of Potential Factors on Passengers’ Long-Distance Travel Mode Choices Based on Structural Equation Modeling

et al. 2017

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“…Traffic accident prediction models have been developed to understand the factors affecting traffic accidents and eventually to reduce traffic accidents by controlling and/or improving factors [5]. As far as the authors are aware the first accident-prediction models for multi-lane roads were devised by Persaud and Dzbik [6].…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

Çodur¹,

Tortum²

2015

PROMET

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“…Permanent Disability due to Road Crashes The concept of a factor loading value of 0.5 is regarded as acceptable; the manifest variables with loading values of less than 0.5 should be dropped [25,26]. Some researchers [27] argue that 0.4 should be the acceptable loading, however, some [21] suggested that "manifest variable with loading values between 0.4 and 0.7 should be reviewed before elimination.…”

Section: Tm Presence Of Emergency Room Injury Surveillance Systemmentioning

confidence: 99%

“…Findings end up with the analysis that road factors, driver factors and environmental factors were strongly related to the accident size" [26]. A similar type of analysis was conducted to evaluate the relationship between accident severity and accident type [36], between gender, anxiety, depression, reward sensitivity, sensation seeking propensity and risky driving [37], between psychological symptoms, sensation seeking, aggression, dysfunctional drinking habits, aberrant driving behavior, speed and accidents [38].…”

Section: Sem and Road Safety Risk Analysismentioning

confidence: 99%

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Road Safety Risk Assessment: An Analysis of Transport Policy and Management for Low-, Middle-, and High-Income Asian Countries

et al. 2018

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Abstract:Road safety assessment has played a crucial role in the theory and practice of transport management systems. This paper focuses on risk evaluation in the Asian region by exploring the interaction between road safety risk and influencing factors. In the first stage, a data envelopment analysis (DEA) method is applied to calculate and rank the road safety risk levels of Asian countries. In the second stage, a structural equation model (SEM) with latent variables is applied to analyze the interaction between the road safety risk level and the latent variables, measured by six observed performance indicators, i.e., financial impact, institutional framework, infrastructure and mobility, legislation and policy, vehicular road users, and trauma management. Finally, this paper illustrates the applicability of this DEA-SEM approach for road safety performance analysis.

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Analysis of traffic accident size for Korean highway using structural equation models

Cited by 133 publications

References 10 publications

Influencing Mechanism of Potential Factors on Passengers’ Long-Distance Travel Mode Choices Based on Structural Equation Modeling

Influencing Mechanism of Potential Factors on Passengers’ Long-Distance Travel Mode Choices Based on Structural Equation Modeling

An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

Road Safety Risk Assessment: An Analysis of Transport Policy and Management for Low-, Middle-, and High-Income Asian Countries

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