Characterization of Driver Perception Reaction Time at the Onset of a Yellow Indication

El-Shawarby, Ihab; Rakha, Hesham A.; Amer, Ahmed; McGhee, Catherine C

doi:10.1007/978-3-319-41682-3_32

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…Previous publications demonstrated that driver perception-reaction time, deceleration levels, and stop-or-run behavior are consistent with other empirical observations in North America (12,(22)(23)(24)(25)(26).…”

Section: Proposed Model Predictorssupporting

confidence: 86%

Enhanced Modeling of Driver Stop-or-Run Actions at a Yellow Indication

Elhenawy

Rakha

El-Shawarby

2014

Transportation Research Record: Journal of the Transportation R

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The ability to model driver stop-or-run behavior at signalized intersections is critical in the design of advanced driver assistance systems. Such systems can reduce intersection crashes and fatalities by predicting driver stop-or-run behavior. The research presented in this paper used data collected from a controlled field experiment on the smart road at the Virginia Tech Transportation Institute to model driver stop-or-run behavior at the onset of a yellow indication. The paper offers three contributions. First, it evaluates the importance of various model predictors in the modeling of driver stop-or-run behavior in the vicinity of signalized intersections. Second, this paper introduces a new variable related to driver aggressiveness and demonstrates that this measure enhances the modeling of driver stop-or-run behavior. Third, the paper applies well-known machine learning techniques, including k nearest neighbor (k nn), random forests, and adaptive boosting (AdaBoost) techniques on the data and compares their performance to standard logistic models in an attempt to identify the optimum modeling framework. The experimental work shows that adding the driver aggressiveness predictor to the model increases the model accuracy by approximately 10% for the logistic, random forest, and k nn models and by 7% for the AdaBoost model. The paper also demonstrates that all modeling frameworks produce similar prediction accuracies.

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Section: Proposed Model Predictorssupporting

confidence: 86%

Enhanced Modeling of Driver Stop-or-Run Actions at a Yellow Indication

Elhenawy

Rakha

El-Shawarby

2014

Transportation Research Record: Journal of the Transportation R

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“…These regions should be pure with respect to the response variable. Given trained RF classifier, the label assigned to a test example is obtained based on the majority votes from all trees, [30,31].…”

Section: Random Forestmentioning

confidence: 99%

A framework for testing independence between lane change and cooperative intelligent transportation system

Elhenawy¹,

Glaser²,

Bond³

et al. 2020

PLoS ONE

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Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. We are preparing for the largest Field Operational Test (FOT) in Australia to evaluate C-ITS safety benefits. Two of the safety benefit hypotheses we formulated assume a dependency between lane changes and C-ITS warnings displayed on the Human Machine Interface (HMI) during safety events. Lane change detection is done by processing many predictors from several sensors at the time of the safety event. However, in our planned FOT, the participating vehicles are only equipped with the vehicle C-ITS and the IMU. Therefore, in this paper, we propose a framework to test lane change and C-ITS dependency. In this framework, we train a random forest classifier using data collected from the IMU to detect lane changes. Consequently, the random forest output probabilities of the testing data in case of C-ITS and control are used to construct a 2x2 contingency table. Then we develop a permutation test to calculate the null hypothesis needed to test the independence of the lane change during safety events and the C-ITS.

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“…A particular PRT value of 2.5s is introduced by AASHTO [24] to calculate the sight distance requirements for a given design situation, and it emphasizes that the PRT value should not be viewed as a constant, because it is influenced by many factors. In El-Shawarby et.al [25], the PRT abstracted from 2016 data records show that the PRT at 72.4km/h ranges from 1.93s to 4.69s with a mean equal to 3.59s and a standard deviation of 0.45s; additionally, the PRT at 88.5km/h ranges between 2.31s and 5.33s with a mean value equal to 3.98s and a standard deviation of 0.53s.…”

Section: Safety-reliability-based Calculation Methods Of Igtmentioning

confidence: 96%

Intergreen Time Calculation Method of Signalized Intersections Based on Safety Reliability Theory: A Monte-Carlo Simulation Approach

Jiang

Wang

2019

Journal of Advanced Transportation

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In China, around ninety percent of the traffic accidents at signalized intersections occur within the signal change intervals, especially during signal change from green to red. Hence, intergreen time (IGT), that is, yellow change interval plus red clearance interval, is of great significance to the safety at signalized intersections. The conventional calculation method of IGT ignores the randomness of drivers’ behaviors, which we believe is an important factor in calculation of IGT. Therefore, the purpose of this research is to investigate a new approach to calculate the IGT based on safety reliability theory. Firstly, a comprehensive literature review concerning the conventional calculation methods of IGT is conducted. Secondly, a theoretical calculation method of IGT based on safety reliability theory is put forward; different from the conventional methods, this model accounts for the uncertainty of driving behavior parameters. Thirdly, a Monte-Carlo simulation is employed to simulate the interactive process of perception-reaction time (PRT) and vehicular deceleration and solve the proposed model. Finally, according to the Monte-Carlo simulation results, the curve clusters describing the relationship between IGT, safety reliability (50%-90%), and intersection width (15-35m) are drawn. Results show that the IGT of a signalized intersection, obeying the normal distribution, is influenced by multiple factors and most sensitive to the PRT and vehicular deceleration. Our method thus successfully incorporates the probabilistic nature of driving behavior. Taking the safety reliability into consideration can provide a more reasonable method to calculate the IGT of signalized intersections.

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Characterization of Driver Perception Reaction Time at the Onset of a Yellow Indication

Cited by 9 publications

References 4 publications

Enhanced Modeling of Driver Stop-or-Run Actions at a Yellow Indication

Enhanced Modeling of Driver Stop-or-Run Actions at a Yellow Indication

A framework for testing independence between lane change and cooperative intelligent transportation system

Intergreen Time Calculation Method of Signalized Intersections Based on Safety Reliability Theory: A Monte-Carlo Simulation Approach

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