Measuring road safety achievement based on EWM-GRA-SVD: A decision-making support system for APEC countries

Chen, Faan; Shi, Lin; Li, Yaxin; Wang, Qilin; Sun, Haosen; Tang, Xinyu; Zu, Jiacheng; Sun, Zhigang

doi:10.1016/j.knosys.2022.109373

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…When the drivers change lanes, their driving habits have a direct impact on making lane-changing decisions, and the interference from their movements. This also suggests that driving habits have an impact on road safety [38], drivers with good driving habits will make safer trips [53]. As a result, improving drivers' driving habits and conducting exams on drivers' mental health will have a positive effect on improving road traffic safety [54].…”

Section: Results Of Semmentioning

confidence: 99%

“…This also suggests that driving habits have an impact on road safety [38], drivers with good driving habits will make safer trips [53]. As a result, improving drivers’ driving habits and conducting exams on drivers’ mental health will have a positive effect on improving road traffic safety [54].…”

Section: Discussionmentioning

confidence: 99%

“…

\;{k_j} = {\mathrm{\;max}}( {{z_{ij}}} )

;

\theta \in ( {0,1} )

denotes the differentiation factor, which is usually taken as 0.5 in previous studies [38, 39].…”

Section: Methodsmentioning

confidence: 99%

“…where k j = max(z i j ); 𝜃 ∈ (0, 1) denotes the differentiation factor, which is usually taken as 0.5 in previous studies [38,39].…”

Section: Processmentioning

confidence: 99%

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Lane changing and keeping as mediating variables to investigate the impact of driving habits on efficiency: An EWM‐GRA and CB‐SEM approach with trajectory data

Wang,

Lu,

Sun

et al. 2023

IET Intelligent Trans Sys

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This paper uses the Entropy Weight Method‐Grey Relational Analysis (EWM‐GRA) and Covariance Base Structural Equations Model (CB‐SEM) to study the relationships between driving habits and efficiency. EWM‐GRA ranks 12 indicators in terms of their relevance of lane‐changing and driving efficiency. Based on this, a CB‐SEM‐based framework to describe the relevance between driving habits and lane‐changing is established, focusing on the effects of lane‐changing and car‐following behaviour. To validate the established framework, NGSIM trajectory data is used as measurement variables to describe latent variables. Several hypotheses about the relationships between the latent variables in this framework are proposed, and they are verified using trajectory data. The results show that driving habits have a direct impact on efficiency, and this impact becomes more significant when associated with lane‐change behaviour.

show abstract

Section: Results Of Semmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…

\;{k_j} = {\mathrm{\;max}}( {{z_{ij}}} )

;

\theta \in ( {0,1} )

denotes the differentiation factor, which is usually taken as 0.5 in previous studies [38, 39].…”

Section: Methodsmentioning

confidence: 99%

“…where k j = max(z i j ); 𝜃 ∈ (0, 1) denotes the differentiation factor, which is usually taken as 0.5 in previous studies [38,39].…”

Section: Processmentioning

confidence: 99%

See 2 more Smart Citations

Lane changing and keeping as mediating variables to investigate the impact of driving habits on efficiency: An EWM‐GRA and CB‐SEM approach with trajectory data

Wang,

Lu,

Sun

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…In this study, the selection of Safety Performance Indicators (SPIs) is primarily divided into three categories: Safer Product, Safer People, and Safer System, based on Chen's improvement [4,5] . Each category consists of a different number of indicators: 2 indicators for Safer Product, 3 indicators for Safer People, and 14 indicators for Safer System.…”

Section: Safety Performance Indicators (Spis)mentioning

confidence: 99%

Assessment of road safety performance based on CRITIC-TOPSIS-Kmeans model

Ding,

He,

Sun

et al. 2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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The study assesses road safety performance in the Southeast Asian region by using the CRITIC-TOPSIS-Kmeans model. First, the weight of each indicator is obtained by CRITIC. Then, the obtained weights are embedded into the TOPSIS model. Furthermore, the TOPSIS sores are put into the K-means unsupervised machine learning model. The proposed CRITIC-TOPSIS-Kmeans model is utilized to rank and group the road safety performance of Southeast Asian countries. Finally, radar and bar plots are utilized to deconstruct indicators and TOPSIS scores, providing valuable references for policymakers. Overall, the development of the CRITIC-TOPSIS-Kmeans model not only establishes a fresh foundation for evaluating road safety achievements but also offers potential solutions for other MCDM challenges.

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Can we trust our eyes? Interpreting the misperception of road safety from street view images and deep learning

Yu,

Ma,

Tang

et al. 2024

Accident Analysis & Prevention

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Measuring road safety achievement based on EWM-GRA-SVD: A decision-making support system for APEC countries

Cited by 7 publications

References 21 publications

Lane changing and keeping as mediating variables to investigate the impact of driving habits on efficiency: An EWM‐GRA and CB‐SEM approach with trajectory data

Lane changing and keeping as mediating variables to investigate the impact of driving habits on efficiency: An EWM‐GRA and CB‐SEM approach with trajectory data

Assessment of road safety performance based on CRITIC-TOPSIS-Kmeans model

Can we trust our eyes? Interpreting the misperception of road safety from street view images and deep learning

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