Clustering Analysis of Drivers Based on Behavioral Characteristics Regarding Road Safety

Shirmohammadi, Hamid; Hadadi, Farhad; Saeedian, Moatasem

doi:10.1007/s40999-018-00390-2

Cited by 31 publications

(14 citation statements)

References 41 publications

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“…To have a clearer understanding of the differences in risk perception between younger drivers and elderly drivers, cluster analysis was used to classify the 28 drivers based on the risk sensitivity and judgment threshold. Cluster analysis is a multivariable statistical method for classifying samples [35]; the essence is to classify samples with similar attributes or characteristics into the same group according to the standard. The difference between the two types of drivers' risk perception ability can be discerned more objectively through classification.…”

Section: Driver Classification Discriminant Model Based On Risk Pementioning

confidence: 99%

Determination of Risk Perception of Drivers Using Fuzzy-Clustering Analysis for Road Safety

Guo

Zhou

et al. 2020

IEEE Access

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This study aims at investigating drivers' risk perception ability. To achieve this objective, the risk sensitivity and risk judgment thresholds of drivers of different ages were calculated. Five scenarios of intersections with risks were established for the driving simulator experiment. The driving behavior data of fourteen younger drivers and fourteen elderly drivers during the risky event and the subjective evaluation of risk by an expert driver were collected. The expert driver's conflict degree and subjective feeling were combined to classify the risk level of driving scene; then, fuzzy signal detection was used to calculate the driver's risk sensitivity (d') and judgment threshold (β). The β with the greatest difference was selected for cluster analysis and drivers were divided into four types according to the threshold. Finally, a driver classification discriminant model was constructed based on Fisher discriminant analysis. The results show that d' and β of younger drivers are both better than those of elderly drivers, younger drivers can detect and respond risks in time, while elderly drivers need be closer to risks and have intuitive feelings to make judgments. The results of the cluster analysis showed that younger drivers account for a large proportion of the sensitive type, indicating that younger drivers can find risks more sensitively in risk scenarios than elderly drivers, while elderly drivers easily ignore risks due to physical and psychological weakness. The correlation analysis showed that age, saccade amplitude and heart rate are the main factors that affect the risk judgment threshold.

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Section: Driver Classification Discriminant Model Based On Risk Pementioning

confidence: 99%

Determination of Risk Perception of Drivers Using Fuzzy-Clustering Analysis for Road Safety

Guo

Zhou

et al. 2020

IEEE Access

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“…where RA 1-5 and RB 1-5 represent the membership matrices of the first and second evaluation, respectively. Taking the membership matrix RA 1 as an example, based on Table 2 (N 11 = 0, 2, 43, 314, 173) and Equations (19) and (20), RA 1 = (0.0000, 0.0018, 0.0572, 0.5572, 0.3838) with p i , (i = 1, 2, . .…”

Section: Fuzzy Comprehensive Evaluationmentioning

confidence: 99%

“…BBS is deemed to be an effective approach to further prevent oil industry accidents. BBS has been successfully applied in a wide range of areas such as health care and hospitals [17,18], transportation [19][20][21], manufacturing [22], and construction [23][24][25]. These successful applications illustrated the effectiveness of the BBS approach for identifying potentially risky or unsafe behaviors [26].…”

Section: Introductionmentioning

confidence: 99%

A Safety Performance Assessment Framework for the Petroleum Industry’s Sustainable Development Based on FAHP-FCE and Human Factors

2019

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Sustainability is a key factor in petroleum industry development, determining whether an enterprise has the ability to maintain high-quality safety management in the long term. In order to reduce occupational injuries and accidents, and to improve safety in the petroleum industry, this study proposes a hybrid approach based on the fuzzy analytical hierarchy process (FAHP), human factors, and the fuzzy comprehensive evaluation (FCE) method in order to assess safety performance in a petroleum enterprise. This paper is comprised of four stages. In the first stage, a model is constructed for assessing the safety of the petroleum industry based on a literature review. In the second stage, we use the FAHP to determine the weights of five factors and 19 sub-factors. In the third stage, employees are the subjects of a questionnaire on the safety performance of the petroleum enterprise. According to the analysis of the assessment results, we focus on improving employees’ safety behaviors and mental health. A second round of questionnaires is distributed to the employees, and a second set of assessment results obtained. Finally, the results of the two evaluations are compared, and the effectiveness of the combination of FAHP, human factors, and FCE is verified.

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“…In this case, traffic accidents are more likely to happen. Shirmohammadi et al used questionnaires and statistical methods to cluster 681 drivers according to driving behavior and driving skill variables, and the results indicated that offensive cluster is the first unsafe subgroup among clusters and skillful cluster is classified as the safest subgroup among clusters [8]. Using the same questionnaire Asghar razmara et al researched the dangerous behaviors of taxi drivers in the city of Abbas, Iran.…”

Section: Introductionmentioning

confidence: 99%

Driving Behavior Clustering for Hazardous Material Transportation Based on Genetic Fuzzy C-Means Algorithm

Wang

2020

IEEE Access

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In recent years, many accidents of hazardous material transportation have been caused by dangerous driving behavior such as excessive speed or rapid speed change. To solve the problem, in this paper a series of indicators for driving behavior evaluation are selected for quantitative analysis based on the massive data of the operating vehicle networked control system. After that, three main indicators are introduced in detail, that is, the acceleration & deceleration behavior indicator, the over speed behavior indicator and the operation stability indicator. Then by using the GA-FCM clustering method, 40 drivers of hazardous material transportation are classified according to their behavior parameters. The results clearly show that the driving speed of the drivers with high-risk driving behavior, and most of the drivers with poor driving stability, are also at bad over speed level. Therefore, this paper creatively applies the method of combining factor analysis and GA-FCM clustering to the field of driving behavior of hazardous material transportation. It will facilitate enterprises and management departments to focus on controlling high-risk drivers, thereby reducing accidents. INDEX TERMS Driving behavior, factor analysis, genetic algorithm, fuzzy c-means clustering.

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Clustering Analysis of Drivers Based on Behavioral Characteristics Regarding Road Safety

Cited by 31 publications

References 41 publications

Determination of Risk Perception of Drivers Using Fuzzy-Clustering Analysis for Road Safety

Determination of Risk Perception of Drivers Using Fuzzy-Clustering Analysis for Road Safety

A Safety Performance Assessment Framework for the Petroleum Industry’s Sustainable Development Based on FAHP-FCE and Human Factors

Driving Behavior Clustering for Hazardous Material Transportation Based on Genetic Fuzzy C-Means Algorithm

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