Estimating risk effects of driving distraction: A dynamic errorable car-following model

Przybyla, Jay; Taylor, Jeffrey; Jupe, Jason; Zhou, Xuesong

doi:10.1016/j.trc.2014.07.013

Cited by 41 publications

(14 citation statements)

References 16 publications

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“…Brodsky et al [10] explored the effects of driver music engagement to the driving safety. Przybyla et al [11] estimated the risk effects of distracted driving, by incorporating a dynamic, data-driven car-following model in an…”

Section: Driver Distractionmentioning

confidence: 99%

Detecting phone‐related pedestrian distracted behaviours via a two‐branch convolutional neural network

Saenz

Sun

Wu³

et al. 2020

IET Intelligent Trans Sys

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The distracted phone‐use behaviours among pedestrians, like Texting, Game Playing and Phone Calls, have caused increasing fatalities and injuries. However, the research of phone‐related distracted behaviour by pedestrians has not been systemically studied. It is desired to improve both the driving and pedestrian safety by automatically discovering the phone‐related pedestrian distracted behaviours. Herein, a new computer vision‐based method is proposed to detect the phone‐related pedestrian distracted behaviours from a view of intelligent and autonomous driving. Specifically, the first end‐to‐end deep learning based Two‐Branch Convolutional Neural Network (CNN) is designed for this task. Taking one synchronised image pair by two front on‐car GoPro cameras as the inputs, the proposed two‐branch CNN will extract features for each camera, fuse the extracted features and perform a robust classification. This method can also be easily extended to video‐based classification by confidence accumulation and voting. A new benchmark dataset of 448 synchronised video pairs of 53,760 images collected on a vehicle is proposed for this research. The experimental results show that using two synchronised cameras obtained better performance than using one single camera. Finally, the proposed method achieved an overall best classification accuracy of 84.3% on the new benchmark when compared to other methods.

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Section: Driver Distractionmentioning

confidence: 99%

Detecting phone‐related pedestrian distracted behaviours via a two‐branch convolutional neural network

Saenz

Sun

Wu³

et al. 2020

IET Intelligent Trans Sys

Self Cite

View full text Add to dashboard Cite

show abstract

“…e systems can reduce traffic emissions at intersections by reducing unnecessary brakes and accelerations. Przybyla et al [26] studied the changes of vehicle trajectory and follow-up laws under the distracted driving behaviour. e following model of the front and subsequent vehicles was developed under the distracted behaviour.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Pedestrian and Nonmotorized Vehicle Violations on Vehicle Emissions at Signalized Intersections in the Real World: A Case Study in Beijing

Huang

Song

Zhang

et al. 2021

Journal of Advanced Transportation

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Emission around intersections has become an issue in the urban traffic network. This paper aims to investigate the impact of pedestrian and nonmotorized vehicle violations on emissions at mixed-traffic flow intersection based on the volumes of vehicles, nonmotor vehicles, and pedestrians. Also, it focuses on the arterial and collector intersections with high vehicle volume and limited space. Running red light and crossing intersection diagonally are two critical violations, accounting for 91.75% of effective violations (interference with vehicles’ operation). In this context, a violation blocking model is developed to estimate the blocking probability for each vehicle based on the volumes of pedestrians and nonmotor vehicles. The model includes two scenarios. (1) Through phase: the violation blocking model of running red light is developed based on the survival curve (the relationship between waiting time and running red light probability). (2) Left-turn phase: the violation blocking model at this phase includes two parts: (i) crossing the intersection diagonally model is developed for the first vehicle and (ii) running red light model is developed for subsequent vehicles. The existing emission model can estimate the emissions based on the blocking positions. In the case study, emissions increase with the vehicle volume approaching the saturated flow rate and the volumes of nonmotor vehicles and pedestrians increasing. Results show that the maximum emission increase of CO (carbon monoxide) for through phase and left-turn phase can reach 16.7% and 36.4%.

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“…(2) Impact of interference has an impact on vehicles, both at the trajectory level and the traffic flow level. At the trajectory level, Przybyla established a dynamic car-following model for driving distraction [24]. At the traffic flow level, interference has an impact on vehicle speed [25] and further influences intersection capacity [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Violations of Bicycles and Pedestrians on Vehicle Emissions at Signalized Intersections

Huang

Song

Zhang

et al. 2020

Journal of Advanced Transportation

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An intersection is a typical emission hot spot in the urban traffic network. And frequent violations such as running the red light have been a critical social problem at signalized intersections in developing countries. This article aimed to quantify the impact of violations (behaviors which will block the fleet) on emissions at signalized intersections. Increased emissions of vehicles affected by violations are of two levels: (1) trajectory level for the first four affected vehicles and (2) traffic flow level for the subsequent vehicles. At the trajectory level, the study focuses on the second-by-second activities of the first four affected vehicles. First, the trajectory model of the first affected vehicle is developed. Then, the trajectory of the other three vehicles is constructed using the Gipps car-following model. At the traffic flow level, a linear emission model is developed to describe the relationship between emission factors and idling time in the one-stop (vehicle stop once) and two-stop (vehicle stop twice) scenarios based on the global position system (GPS)-collected data at 44 intersections in Beijing. Based on the linear emission model, increased emissions at the traffic flow level are calculated as knowing the number of stops and idling time before and after violations. The analysis of the subsequent vehicles is divided into unsaturated and saturated conditions. Under the unsaturated condition, the emissions have barely increased due to the increase of idling time for one-stop vehicles caused by the violations. Under the saturated conditions, the emission increment increases sharply as the one-stop vehicle gradually transforms to a two-stop vehicle because of violations, and the maximum emission increment reaches 45% in half an hour in the case. The increment of emissions decreases steadily as the proportion of two-stop vehicles reaches 100% after violations, while the proportion before violations keeps increasing.

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Estimating risk effects of driving distraction: A dynamic errorable car-following model

Cited by 41 publications

References 16 publications

Detecting phone‐related pedestrian distracted behaviours via a two‐branch convolutional neural network

Detecting phone‐related pedestrian distracted behaviours via a two‐branch convolutional neural network

The Impact of Pedestrian and Nonmotorized Vehicle Violations on Vehicle Emissions at Signalized Intersections in the Real World: A Case Study in Beijing

The Impact of Violations of Bicycles and Pedestrians on Vehicle Emissions at Signalized Intersections

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