A spectral power analysis of driving behavior changes during the transition from nondistraction to distraction

Wang, Yuan; Bao, Shan; Du, Wenjun; Ye, Zhirui; Sayer, James R.

doi:10.1080/15389588.2017.1320549

Cited by 19 publications

(11 citation statements)

References 17 publications

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“…The measures used in this study are time domain measures. Wang, Bao, Du, Ye, and Sayer (2017) used fast Fourier transform (FFT) to extract the frequency domain measures and showed that the results based on the frequency measures were more robust and consistent than that based on the time measures. However, Wang et al (2017) noted that FFT may not be a good method to interpret the dynamic changes (e.g., lane changes) because FFT generally assumes a steady-state process.…”

Section: Discussionmentioning

confidence: 99%

Effects of Different Alcohol Dosages on Steering Behavior in Curve Driving

Zhao

et al. 2018

Hum Factors

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Section: Discussionmentioning

confidence: 99%

Effects of Different Alcohol Dosages on Steering Behavior in Curve Driving

Zhao

et al. 2018

Hum Factors

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“…Few studies have analysed adaptations in driver behaviour capturing the impact of several explanatory factors and interdependencies between repeated observations over time for the same subject. For this purpose, recent studies have proposed linear mixed-effects models for repeated measures, which can accommodate both fixed and random effects capturing complex error structures (Peng, Boyle, and Lee 2014;Peng and Boyle 2015;Oviedo-Trespalacios et al 2017;Wang et al 2017;Geden, Staicu, and Feng 2018;Saad, Abdel-Aty, and Lee 2018;Albert 2019). Linear mixed-effects models allow to define explicitly a hierarchical structure (e.g.…”

Section: Statistical Analysis Methods For Adaptations In Driver Behavmentioning

confidence: 99%

Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

Varotto

Farah

Bogenberger

et al. 2020

Transportmetrica A: Transport Science

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Adaptive Cruise Control (ACC) can reduce traffic congestion and accidents. In dense traffic flow conditions and when changing lanes, drivers prefer to deactivate the ACC. These control transitions between automation and manual driving could impact driver behaviour characteristics. However, few studies have analysed the magnitude and duration of these adaptations. This research aims at quantifying the adaptations in speed, acceleration, distance headway and relative speed when drivers resume manual control. We collected driver behaviour data in an on-road experiment with fullrange ACC during peak hours in Munich. We analysed these data using linear mixed-effects models to identify statistically significant changes in driver behaviour characteristics after drivers resumed manual control (transition period). The results reveal that the speed decreased significantly after the system was deactivated and it increased significantly after the system was overruled by pressing the gas pedal. These adaptations might have a substantial impact on traffic efficiency and safety.

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“…Appropriate identification of risk factors affecting traffic accidents is necessary. From the existing research, driver attributes [53], weather conditions [54], traffic characteristics [55], driving behavior [56], temporal-spatial distribution [57] and vehicle types [58] can all influence the possibility of a traffic accident. We established the risk factor system based on the literature plus two newly added risk factors, i.e., weaving ratio and accident location.…”

Section: Methodsmentioning

confidence: 99%

Risk Factors Affecting Traffic Accidents at Urban Weaving Sections: Evidence from China

Mao

Cao

Gan

et al. 2019

IJERPH

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As a critical configuration of interchanges, the weaving section is inclined to be involved in more traffic accidents, which may bring about severe casualties. To identify the factors associated with traffic accidents at the weaving section, we employed the multinomial logistic regression approach to identify the correlation between six categories of risk factors (drivers’ attributes, weather conditions, traffic characteristics, driving behavior, vehicle types and temporal-spatial distribution) and four types of traffic accidents (rear-end, side wipe, collision with fixtures and rollover) based on 768 accident samples of an observed weaving section from 2016 to 2018. The modeling results show that drivers’ gender and age, weather condition, traffic density, weaving ratio, vehicle speed, lane change behavior, private cars, season, time period, day of week and accident location are important factors affecting traffic accidents at the weaving section, but they have different contributions to the four traffic accident types. The results also show that traffic density of ≥31 vehicle/100 m has the highest risk of causing rear-end accidents, weaving ration of ≥41% has the highest possibility to bring about a side wipe incident, collision with fixtures is the most likely to happen in snowy weather, and rollover is the most likely incident to occur in rainy weather.

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A spectral power analysis of driving behavior changes during the transition from nondistraction to distraction

Cited by 19 publications

References 17 publications

Effects of Different Alcohol Dosages on Steering Behavior in Curve Driving

Effects of Different Alcohol Dosages on Steering Behavior in Curve Driving

Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

Risk Factors Affecting Traffic Accidents at Urban Weaving Sections: Evidence from China

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