Modelling brake transition time of young alcohol-impaired drivers using hazard-based duration models

Yadav, Ankit; Velaga, Nagendra R.

doi:10.1016/j.aap.2021.106169

Cited by 24 publications

(3 citation statements)

References 44 publications

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“…Various multivariate count models have been developed for jointly analyzing crash frequencies with different outcomes of injury severity. Based on Poisson regression, traditional count-data models (negative binomial models), Tobit models, multivariate models, and other derived models (Table 1) have been widely adopted to study the probability of crashes, and random parameters have been used to account for unobserved heterogeneity [17,[19][20][21][22][23][24][25][26][27][28][29][30][31][32]. As shown in Table 1, among all of the contributing factors, a wide range of roadway, traffic, and environmental characteristics influence both the likelihood and injury severity of crashes [15].…”

Section: Literature Review Of Modeling Methodology For Crash Frequencymentioning

confidence: 99%

A Random-Parameter Negative Binomial Model for Assessing Freeway Crash Frequency by Injury Severity: Daytime versus Nighttime

et al. 2022

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This study explored the effects of contributing factors on crash frequency, by injury severity of all, daytime, and nighttime crashes that occurred on freeways. With three injury severity outcomes classified as light injury, minor injury, and severe injury, the effects of the explanatory variables affecting the crash frequency were examined in terms of the crash, traffic, speed, geometric, and sight characteristics. Regarding the model estimations, the lowest AIC and BIC values (2263.87 and 2379.22, respectively) showed the superiority of the random-parameter multivariate negative binomial (RPMNB) model in terms of the goodness-of-fit measure. Additionally, the RPMNB model indicated the highest R2 (0.25) and predictive accuracy, along with a significantly positive α parameter. Moreover, transferability tests were conducted to confirm the rationality of separating the daytime and nighttime crashes. Based on the RPMNB models, several explanatory variables were observed to exhibit relatively stable effects whereas other variables presented obvious variations. This study can be of certain value in guiding highway design and policies and developing effective safety countermeasures.

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Section: Literature Review Of Modeling Methodology For Crash Frequencymentioning

confidence: 99%

A Random-Parameter Negative Binomial Model for Assessing Freeway Crash Frequency by Injury Severity: Daytime versus Nighttime

et al. 2022

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“…However, the LMM only tests for differences in means between the conditions. Recent research has shown that for reaction time data, more insight into the data can be gained by complementing the LMM with a survival analysis (Haque & Washington, 2015;Yadav & Velaga, 2021). In studies on lane changes it has been used to assess the probability of lane changes .…”

Section: Discussionmentioning

confidence: 99%

“…In studies on lane changes it has been used to assess the probability of lane changes . To assess how the duration to an event is influenced covariates an accelerated failure time (AFT) survival analysis is particularly appropriate Haque et al, 2016;Mahajan & Velaga, 2020;Yadav & Velaga, 2021). The assumption in this analysis is that the covariates can influence or scale (accelerate/decelerate) the response time towards an event (for a formal description see Haque & Washington, 2015;Washington et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Keeping the Driver in the Loop While Driving With Conditional Automation

Dillmann¹

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The automation of vehicles can be divided into several steps (SAE International, 2018). Level 0 contains no automation at all. Level 1 contains driving assistance that is either longitudinal or lateral. For instance, cruise control or adaptive cruise control would assist longitudinal vehicle control by holding or adapting velocity. At partial automation (Level 2) both lateral and longitudinal driving assistance systems are combined to support the driver. An example is the combination of adaptive cruise control and lane centering functions. In combination these two assistance functions support the driver in staying in the lane and maintaining a certain speed. However, at this level of automation, the driver remains the labels entail (Flach, 1995;Woods, 1993) and shifting scientific endeavors away from observable human performance (Dekker & Hollnagel, 2004). The Loop Defined From Perception-Action Theory PerspectiveGenerally speaking, perception-action theory originates from James Gibson's seminal work on understanding drivers' and pilots' capacity to control movement adequately (Gibson et al., 1955;Gibson & Crooks, 1938). Building on Gibson's later work (Gibson, 1986) the perception-action theory approach investigates how drivers locomote by interacting with the environment through the process of perception and action loops (Bootsma, 1998;Harrison et al., 2016;Mathieu et al., 2017a). For example, the optical expansion of a stop sign in the retina of drivers guides their braking actions towards it (DeLucia et al., 2016;Lee, 1976;Morando et al., 2016). In turn, the action of braking changes the optical expansion of the stop sign relative to the driver, thereby closing the perception-action loop. Iterations of these perception-action loops allow driving behavior to emerge (Harrison et al., 2016;Mathieu et al., 2017a).In order to understand how drivers can be kept in the loop, it is necessary to understand two key processes underlying the perception-action loop: perceptual attunement and perceptual-motor calibration. Perceptual attunement entails that a driver is exposed to -and able to detect and track -the specifying (i.e. task-relevant) visual information (Bootsma, 1998). In the vehicle braking example above, drivers are perceptually attuned to visual variables such as looming, specifying the relative distance to other vehicles (Fajen & Devaney, 2006). In lane keeping tasks, drivers have been shown to perceptually attune to visual variables such as optical flow, bearing-and slay angles (Li & Chen, 2010), or the inner point of approaching curves (Land & Lee, 1994).To close the perception-action loop, drivers must be proficient in scaling their motor responses adequately to this information, a process termed perceptualmotor calibration (Brand & de Oliveira, 2017;Van Andel et al., 2017). Perceptualmotor calibration is not static but dynamically dependent on the situation. Drivers have been shown to need a period of perceptual-motor calibration when tailwind is introduced in a vehicle braking task (Fajen, 2005b) or wh...

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Validity of Using a Driving Game Simulator to Study the Visual Attention Differences in Young and Older Adults

Vera-Uribe

Rodríguez

Armenta

et al. 2022

Proceedings of the International Conference on Ubiquitous Computing &Amp; Ambient Intelligence (UCAmI 2022)

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Modelling brake transition time of young alcohol-impaired drivers using hazard-based duration models

Cited by 24 publications

References 44 publications

A Random-Parameter Negative Binomial Model for Assessing Freeway Crash Frequency by Injury Severity: Daytime versus Nighttime

A Random-Parameter Negative Binomial Model for Assessing Freeway Crash Frequency by Injury Severity: Daytime versus Nighttime

Keeping the Driver in the Loop While Driving With Conditional Automation

Validity of Using a Driving Game Simulator to Study the Visual Attention Differences in Young and Older Adults

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