Assessing the relationship between heavy vehicle driver sleep problems and confirmed driver behavior measurement tools in Iran

Naderi, Hossein; Nassiri, Habibollah

doi:10.1016/j.trf.2018.08.007

Cited by 36 publications

(19 citation statements)

References 32 publications

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“…is group of drivers appears to be traveling more and more at unauthorized speeds because they have less control over their speed. e result of this study is in accordance with the result of the study done by Naderi Nassiri et al in 2018 [20] but is against the results obtained by Precht Keinath et al in 2017 [18].…”

Section: Discussionsupporting

confidence: 92%

“…Other investigations have also addressed the issue of offenses among lorry drivers and the variables affecting offenses. Driving behavior and individual behavioral characteristics [17][18][19][20], driver demographic information [8,21], mileage [2,15,22], and fatigue and drowsiness [9,20] are emphasized as the parameters considered in this section of studies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In other studies, the effect of driving behavior on committing offenses and its role in the occurrence of lorry driver's accidents are considered. Among the most important parameters to be considered in this section are the four parameters of errors, lapses, common violations, and aggressive violations that in the Drivers Behavioral Questionnaire, the most critical parameter in predicting accidents is distinguished as common violations [20]. Some of the parameters viewed in other studies include the amount of cognitive error [11]; individual rules and perceived behavior control [27]; anger and drivers' differences in driving behavior [18]; mental and emotional conditions while driving; and the drivers' driving style.…”

Section: Literature Reviewmentioning

confidence: 99%

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Investigating the Effect of Urban New Technologies on the Iranian Lorry Drivers’ Behavior

Sheikholeslami

Ayazi

Moghadari

2021

Journal of Advanced Transportation

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Most accidents are directly related to driving offenses, and drivers who commit more offenses are more prone to accidents. Therefore, reducing driving offenses can reduce accidents. In other words, the recognition of common driving offenses among heavy vehicle (truck) drivers and the effective factors in directing them to reduce driving offenses can consequently reduce the frequency and severity of accidents. It seems that there is a necessity for in-depth studies to carry out research on this topic. The main objective of this study is to identify and evaluate important factors affecting lorry drivers committing traffic offenses. To achieve the goals, the required information was categorized into six categories: traffic tonnage, not fastening the seatbelt, speeding, technical defect, talking on cell phone, and lacking towing worksheet; these factors are known as dependent variables. Also, its influencing factors—in the group of driver characteristics, vehicle, and mileage—were obtained by using a demographic questionnaire, Driving Behavior Questionnaire (DBQ), and interviews with 420 drivers over 60 days at Tehran Terminal. After correcting incomplete questionnaires, 351 drivers’ information was used for statistical analysis. The statistical analysis of data using a multivariate logistic regression model showed that drivers loading and unloading five or six times per month are less likely to commit overloading than drivers loading and unloading more than 12 times per month. The results also show that the distracted drivers with less slip behavior are less likely to commit unauthorized speed offenses and 85.4% are less likely to commit this violation. Finally, the statistical analysis showed that drivers with aggressive driving behavior were more likely to commit a lack of towing worksheet offenses.

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Section: Discussionsupporting

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Investigating the Effect of Urban New Technologies on the Iranian Lorry Drivers’ Behavior

Sheikholeslami

Ayazi

Moghadari

2021

Journal of Advanced Transportation

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“…Other studies have also addressed the issue of offenses among commercial vehicle drivers and the variables affecting them. e parameters studied in this category included driving behavior and behavioral characteristics, driver demographic information [19,27], mileage [2,14,28], and fatigue/drowsiness [10,23]. e results of some studies show that there is a significant relationship between driving experience and driving offenses such as speeding and not fastening seat belts [2,25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…And the human factor is the most important factor in the analysis of traffic accidents [7]. Drivers' offenses are therefore among the most important human factors leading to accidents, which have been used in many studies to investigate driving behaviors that are related to behavioral characteristics and drivers characteristics [6][7][8][9][10][11][12]. According to the Ministry of Roads and Urban Development of Iran, there have been 63472 offenses in 2017 resulting in 121108 rural crashes leading to 16201 fatalities and 335995 injuries.…”

Section: Introductionmentioning

confidence: 99%

A Data Mining Approach on Lorry Drivers Overloading in Tehran Urban Roads

Ayazi

Sheikholeslami

2020

Journal of Advanced Transportation

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The aim of this study is to identify the important factors influencing overloading of commercial vehicles on Tehran’s urban roads. The weight information of commercial freight vehicles was collected using a pair of portable scales besides other information needed including driver information, vehicle features, load, and travel details by completing a questionnaire. The results showed that the highest probability of overloading is for construction loads. Further, the analysis of the results in the lorry type section shows that the least likely occurrence of overloading is among pickup truck drivers such that this likelihood within this group was one-third among Nissan and small truck drivers. Also, the results of modeling the type of route showed that the highest likelihood of overloading is for internal loads (origin and destination inside Tehran), and the least probability of overloading is for suburban trips (origin and destination outside of Tehran). Considering the type of load packing as a variable, the results of binary regression model analysis showed that the most probability of overloading occurs for packed (boxed) loads. Finally, it was concluded that drivers are 18 times more likely to commit overloading on weekends than on weekdays.

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