Analysis of fatal crashes due to signal and stop sign violations

Advances in technology have dramatically influenced and changed the world of transportation in countless way. While many improvements have been realized with the help of technology in this area, negative consequences, like distracted driving, continue to be formidable challenges to the public’s safety and health. Studies have demonstrated that mobile phones, built-in navigation systems, and other in-vehicle–related entertainment/information technology (infotainment) have contributed to distracted driving, leading to increased morbidity and mortality, especially among the youth. Today, the ubiquitous use of mobile phones while driving has led to major increases of distracted driving, with some attribution to injury and fatal crashes, particularly among young drivers.

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Understanding Gap Acceptance Behavior at Unsignalized Intersections using Naturalistic Driving Study Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211007140 ◽

2021 ◽

pp. 036119812110071

Author(s):

Yingfeng (Eric) Li ◽

Haiyan Hao ◽

Ronald B. Gibbons ◽

Alejandra Medina

Keyword(s):

Road Traffic ◽

Wait Time ◽

Study Data ◽

Gap Acceptance ◽

Factors Affecting ◽

Unsignalized Intersections ◽

Research Activities ◽

Stop Sign ◽

Naturalistic Driving Study ◽

Naturalistic Driving

Even though drivers disregarding a stop sign is widely considered a major contributing factor for crashes at unsignalized intersections, an equally important problem that leads to severe crashes at such locations is misjudgment of gaps. This paper presents the results of an effort to fully understand gap acceptance behavior at unsignalized intersections using SHPR2 Naturalistic Driving Study data. The paper focuses on the findings of two research activities: the identification of critical gaps for common traffic/roadway scenarios at unsignalized intersections, and the investigation of significant factors affecting driver gap acceptance behaviors at such intersections. The study used multiple statistical and machine learning methods, allowing a comprehensive understanding of gap acceptance behavior while demonstrating the advantages of each method. Overall, the study showed an average critical gap of 5.25 s for right-turn and 6.19 s for left-turn movements. Although a variety of factors affected gap acceptance behaviors, gap size, wait time, major-road traffic volume, and how frequently the driver drives annually were examples of the most significant.

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Detection of Stop Sign Violations From Dashcam Data

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2021.3053648 ◽

2021 ◽

pp. 1-10

Author(s):

Luca Bravi ◽

Luca Kubin ◽

Stefano Caprasecca ◽

Douglas Coimbra de Andrade ◽

Matteo Simoncini ◽

...

Keyword(s):

Stop Sign

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Why do three-wheelers carrying schoolchildren suffer very low fatal crashes?

IATSS Research ◽

10.1016/j.iatssr.2014.12.001 ◽

2015 ◽

Vol 38 (2) ◽

pp. 130-134 ◽

Cited By ~ 2

Author(s):

Gaurav Pandey ◽

Dinesh Mohan ◽

K. Ramachandra Rao

Keyword(s):

Fatal Crashes

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Evaluating the Effects of Traffic on Driver Stopping and Turn Signal Use at a Stop Sign

Journal of Organizational Behavior Management ◽

10.1300/j075v27n02_03 ◽

2007 ◽

Vol 27 (2) ◽

pp. 27-35 ◽

Cited By ~ 5

Author(s):

Angela R. Lebbon ◽

John Austin ◽

Ron Van Houten ◽

Louis E. Malenfant

Keyword(s):

Turn Signal ◽

Stop Sign ◽

Signal Use

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Drug and Alcohol Involvement in Four Types of Fatal Crashes*

Journal of Studies on Alcohol and Drugs ◽

10.15288/jsad.2011.72.567 ◽

2011 ◽

Vol 72 (4) ◽

pp. 567-576 ◽

Cited By ~ 37

Author(s):

Eduardo Romano ◽

Robert B. Voas

Keyword(s):

Alcohol Involvement ◽

Fatal Crashes ◽

Drug And Alcohol

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A Comparison of Presentation Mediums for the Study of Trust in Autonomous Vehicles

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181321651320 ◽

2021 ◽

Vol 65 (1) ◽

pp. 878-882

Author(s):

Katherine Garcia ◽

Ian Robertson ◽

Philip Kortum

Keyword(s):

Autonomous Vehicles ◽

Automated System ◽

Spatial Presence ◽

Viewing Condition ◽

Stop Sign ◽

Flat Screen ◽

The Mean ◽

Presentation Methods ◽

Self Driving Cars ◽

User Trust

The purpose of this study is to compare presentation methods for use in the validation of the Trust in Selfdriving Vehicle Scale (TSDV), a questionnaire designed to assess user trust in self-driving cars. Previous studies have validated trust instruments using traditional videos wherein participants watch a scenario involving an automated system but there are strong concerns about external validity with this approach. We examined four presentation conditions: a flat screen monitor with a traditional video, a flat screen with a 2D 180 video, an Oculus Go VR headset with a 2D 180 video, and an Oculus Go with a 3D VR video. Participants watched eight video scenarios of a self-driving vehicle attempting a right-hand tum at a stop sign and rated their trust in the vehicle shown in the video after each scenario using the TSDV and rated telepresence for the viewing condition. We found a significant interaction between the mean TSDV scores for pedestrian collision and presentation condition. The TSDV mean in the Headset 2D 180 condition was significantly higher than the other three conditions. Additionally, when used to view the scenarios as 3D VR videos, the headset received significantly higher ratings of spatial presence compared to the condition using a flatscreen a 2D video; none of the remaining comparisons were statistically significant. Based on the results it is not recommended that the headset be used for short scenarios because the benefits do not outweigh the costs.

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