Effects of foggy conditions on drivers’ speed control behaviors at different risk levels

Yan, Xuedong; Li, Yongming; Liu, Yang; Zhao, Jia

doi:10.1016/j.ssci.2014.04.013

Cited by 94 publications

(53 citation statements)

References 45 publications

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“…The studies related to road characteristics include the curve radius/ curvature (Coutton-Jean et al, 2009;Bella et al, 2014;Boer, 1996), edge lines (Coutton-Jean et al, 2009), lane width (Robertshaw and Wilkie, 2008;Coutton-Jean et al, 2009), curve length (Zuriaga et al, 2010;Hu and Donnell, 2010) and pavement condition (Buddhavarapu et al, 2013;Zador et al, 1987). The research related to environmental conditions in curve segments includes weather conditions (Jung et al, 2014;Yan et al, 2014), nighttime (Bella et al, 2014;Hu and Donnell, 2010), roadside clearance (Aram, 2010;Bella, 2013), sight distance (Kondo and Ajimine, 1968), traffic volume (Aram, 2010;AASHTO, 2010) and markings and speed signs (Rutley, 1972;Comte and Jamson, 2000). The vehicle-related factors can significantly influence the relevant driving behaviors and running out of curve crashes, such as vehicle type (Liu and Subramanian, 2009;Fitzsimmons et al, 2013) and vehicle occupancy (Liu and Ye, 2011).…”

Section: Factors Associated With Curve Safetymentioning

confidence: 99%

“…In fact, professional drivers' extensive driving time and mileage trains them to develop better skills and experience of vehicle control. Professional drivers have been found to have better performance on complex road segments than NP drivers (Yan et al, 2014). The demands of their work also facilitate professional drivers to drive more cautiously.…”

Section: Influence Of Driver Experience and Gender On Driving Behaviormentioning

confidence: 99%

“…The experimental road for the driving simulation was composed of straight segments and an S-curve segment, both of which were two-way with two lanes 3.5 m wide. The S-curve segment was 200 m long, connected with a 400 m entry straight segment, and this test track is part of the road network in the study of Yan et al (2014). Detailed dimensions of the experimental road were shown in Fig.…”

Section: Scenario Design and Data Collectionmentioning

confidence: 99%

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Effects of fog, driver experience and gender on driving behavior on S-curved road segments

Yan

Wong

2015

Accident Analysis & Prevention

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Driving on curved roads has been recognized as a significant safety issue for many years. However, driver behavior and the interactions among variables that affect driver performance on curves is complicated and not well understood. Previous studies have investigated various factors that influence driver performance on right- or left-turn curves, but have paid little attention to the effects of foggy weather, driver experience and gender on driver performance on complex curves. A driving simulator experiment was conducted in this study to evaluate the relationships between driving behavior on a continuous S-curve and foggy weather, driver experience and gender. The process of negotiating a curve was divided into three stages consisting of a straight segment, the transition from the straight segment to the S-curve and the S-curve. The experimental results indicated that drivers tended to drive more cautiously in heavy fog, but the driving risk was still increased, especially in the transition stage from the straight segment to the S-curve. The non-professional (NP) drivers were less sensitive to the impending change in the road geometry, and less skilled in both longitudinal and lateral vehicle control than the professional drivers. The NP female drivers in particular were found to be the most vulnerable group in S-curve driving.

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Section: Factors Associated With Curve Safetymentioning

confidence: 99%

Section: Influence Of Driver Experience and Gender On Driving Behaviormentioning

confidence: 99%

Section: Scenario Design and Data Collectionmentioning

confidence: 99%

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Effects of fog, driver experience and gender on driving behavior on S-curved road segments

Yan

Wong

2015

Accident Analysis & Prevention

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“…Driving simulation experience indicated that drivers for reduce the risk of driving in foggy conditions at all three levels of risks they are willing to reduce speed [11]. In the other study on 75km of suburban freeway sections, reducing the average speed was 8 km/h and 19.2 km/h for foggy and snowy conditions respectively [12].…”

Section: Introductionmentioning

confidence: 89%

Predicting Average Vehicle Speed in Two Lane Highways Considering Weather Condition and Traffic Characteristics

Mirbaha

Saffarzadeh

Beheshty

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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“…However, these behaviors might be adjusted according to climate and weather conditions [36]. In this regard, Yan et al [37], Jung et al [38] and Hamdar et al [39] stated that dynamic nature of driving is affected by weather conditions. The abnormal weather and other environmental conditions factors substantially impact traffic flow dynamics [40] as well as traffic congestion and crashes [39,41], which may affect driver behavior (for example, resulting in speed reductions).…”

Section: Introductionmentioning

confidence: 99%

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

et al. 2018

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Road transportation is the main energy consumer and major contributor of ever-increasing hazardous emissions. Transportation professionals have raised the idea of applying the green concept in various areas of transportation, including green highways, green vehicles and transit-oriented designs, to tackle the negative impact of road transportation. This research generated a new dimension called the green driver to remediate urgently the existing driving assessment models that have intensified emissions and energy consumption. In this regard, this study aimed to establish the green driver's behaviors related to fuel saving and emission reduction. The study has two phases. Phase one involves investigating the driving behaviors influencing fuel saving and emission reduction through a systematic literature review and content analysis, which identified twenty-one variables classified into four clusters. These clusters included the following: (i) FE f1 , which is driving style; (ii) FE f2 , which is driving behavior associated with vehicle transmission; (iii) FE f3 , which is driving behavior associated with road design and traffic rules; and (iv) FE f4 , which is driving behavior associated with vehicle operational characteristics. The second phase involves validating phase one findings by applying the Grounded Group Decision Making (GGDM) method. The results of GGDM have established seventeen green driving behaviors. The study conducted the Green Value (GV) analysis for each green behavior on fuel saving and emission reduction. The study found that aggressive driving (GV = 0.16) interferes with the association between fuel consumption, emission and driver's personalities. The research concludes that driver's personalities (including physical, psychological and psychosocial characteristics) have to be integrated for advanced in-vehicle driver assistance system and particularly, for green driving accreditation.

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Effects of foggy conditions on drivers’ speed control behaviors at different risk levels

Cited by 94 publications

References 45 publications

Effects of fog, driver experience and gender on driving behavior on S-curved road segments

Effects of fog, driver experience and gender on driving behavior on S-curved road segments

Predicting Average Vehicle Speed in Two Lane Highways Considering Weather Condition and Traffic Characteristics

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

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