Analyzing driver behavior under naturalistic driving conditions: A review

Singh, Harpreet; Kathuria, Ankit

doi:10.1016/j.aap.2020.105908

Cited by 146 publications

(64 citation statements)

References 144 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, rural drivers are more likely to have unfavorable attitudes and beliefs about seat belts than urban drivers, and these unfavorable attitudes and beliefs indicate that seat belt usage will decrease [19]. In the town-center area, the elderly are more likely to have serious accidents, which was not consistent with the studies [21,51,52], but was consistent with findings in previous studies that senior drivers are found to have a higher probability to be more severely injured in rural roads [15]. Because the reaction ability of elderly drivers is relatively slow, their physical functions are not as good as those of young and middle-aged drivers, and their body's recovery capacity after an accident is not as good as that of other age groups, serious accidents are more likely to occur.…”

Section: Comparative Analysis Of Modelsmentioning

confidence: 63%

“…Kmet et al analyzed the accident mortality of children and adolescents in rural and urban areas and found that the mortality rate in rural areas was five times higher than that in urban areas [20]. Singh et al found that the main cause of most traffic accidents was the behavior of the driver, and whether the driver chose to reduce the speed when encountering adverse weather [21]. In terms of road characteristics research, Pokorny found that rural roads with a lane width of 1.50-2.50 m and a shoulder width of 0.50-0.75 m are safer in Norway [18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Study on the Impact of Traffic Accidents in Key Areas of Rural Roads

Xie

Shu

2021

Sustainability

View full text Add to dashboard Cite

In recent years, the proportion of rural road fatalities in the country is gradually increasing, among which the traffic safety problems are particularly prominent in the town-rural area and the town-center area. Based on the relevant accident data in Hunan Province in recent years, the chi-square test was conducted to obtain the correlation degree between each risk factor and accident severity based on gender stratification. Then, a binary logistic model was established to obtain the significant factors that affect the accident severity in the town-rural area and the town-center area, respectively. Based on the significant factors, relevant safety improvement measures were proposed for the key areas. The results show that severe accidents were significantly related to single-vehicle factors, motorcycle factors, and intersections factors in the town-rural area. In the town-center area, severe accidents were significantly related to elderly age, single-vehicle factors, and nighttime factors. The study obtained the risk factors in key areas, which can provide a reference for the improvement of traffic safety in key areas of rural roads, to ensure the safety and sustainability of rural traffic.

show abstract

Section: Comparative Analysis Of Modelsmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Study on the Impact of Traffic Accidents in Key Areas of Rural Roads

Xie

Shu

2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…To meet today's rising demand for mobility in the era of technological advancement boosted the use of electric vehicles [9,[35][36][37][38]. As the electric vehicles have a large number of added advantages, it is becoming popular in society.…”

Section: Discussionmentioning

confidence: 99%

“…and driving experience (F=1.325132<Fcrit.). For improved vehicle performance, some drivers behaviour under naturalistic driving circumstances were investigated [36]. Consequently, to realize the numerous practices followed by drivers, the perception of driver's behaviour related to getting the moving vehicle turned off while turning the vehicle on the road was examined in the context of age, gender, and driving experience.…”

Section: Discussionmentioning

confidence: 99%

Investigation of Perceived Risk Encountered by Electric Vehicle Drivers in Distinct Contexts

Patil¹,

Khairnar²

2021

Applied Engineering Letters

View full text Add to dashboard Cite

Not only pollution concerns, but also the rising prices of fuel used in conventional vehicles are enforcing people to make use of electric vehicles. In spite of numerous advantages such as pollution-free, noiseless smooth drive; there may be the new possibility of difficulties occurring due to the quiet nature of electric vehicles (EVs). To examine this, it was intended to conduct an extensive questionnaire survey with 398 driver participants to acquire technical data at Mumbai Metropolitan Region (MMR). The analysis used a well-proven driver behaviour questionnaire (DBQ) built on a six-point scale to statistically evaluate driver behaviour responses to perceived risk. This study aims to evaluate the perceived risk encountered by drivers that influence road safety by considering age group, driving experience, and gender. A systematic ANOVA approach was employed to evaluate the significant factors. The results show that the perceived risk is different based on the gender of the driver, especially when parking the vehicle (p=0.000, F=10.11716>Fcrit). The moderate difficulty level for identifying the presence of electric vehicles is present in almost all situations; however, no significant difference was recorded based on gender, age, and driving experience in the rest of the cases. The outcome of the proposed work would be useful while designing the safety policy for electric vehicles.

show abstract

“…Ellison et al [30] used GPS to collect acceleration and location data to analyze drivers' safety. Onboard diagnostic (OBD) loggers have also been used to collect driving behavior information such as speed, braking, and acceleration, and this information tends to be more reliable than GPS data [32,33]. Cao et al [34] used driving behavior data (including deceleration, braking, location, and acceleration) collected from OBD loggers, combined with cluster techniques, to forecast crash events.…”

Section: Related Workmentioning

confidence: 99%

Individual Driver Crash Risk Classification Based on IoV Data and Offline Consumer Behavior Data

Zhao

Dai

2021

Mobile Information Systems

View full text Add to dashboard Cite

With the development of big data technologies, usage-based insurance (UBI) has received considerable attention from insurance companies. UBI products focus on identifying the relationship between the individual driver’s risk and online channel behavior variables from Internet of Vehicles (IoV) data. Although omnichannel information integration has promoted the development of many industries, it has not been used to improve the accuracy of driver risk classification models in insurance industries. This paper investigates the role of combining different channel variables in improving the classification of driver’s risk. Specifically, several models, including logistic regression and three different data mining techniques (neural networks, random forests, and support vector machines), augmented with driving behavior data based on the IoV and offline consumer behavior data collected from 4S (Sale, Spare part, Service, Survey) dealers, are applied to the classification model of risk. The empirical results show that the inclusion of online and offline channel data improves the different risk assessments; results also demonstrate the importance of offline consumer behavior variables in different models. These insights have important implications for insurance companies on UBI pricing strategy and cost management.

show abstract

Analyzing driver behavior under naturalistic driving conditions: A review

Cited by 146 publications

References 144 publications

Study on the Impact of Traffic Accidents in Key Areas of Rural Roads

Study on the Impact of Traffic Accidents in Key Areas of Rural Roads

Investigation of Perceived Risk Encountered by Electric Vehicle Drivers in Distinct Contexts

Individual Driver Crash Risk Classification Based on IoV Data and Offline Consumer Behavior Data

Contact Info

Product

Resources

About