A study of freeway crash risk prediction and interpretation based on risky driving behavior and traffic flow data

2021 ◽  
Vol 160 ◽  
pp. 106328
Author(s):  
Miao Guo ◽  
Xiaohua Zhao ◽  
Ying Yao ◽  
Pengwei Yan ◽  
Yuelong Su ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Risk Prediction ◽  
Driving Behavior ◽  
Crash Risk ◽  
Flow Data ◽  
Risky Driving

scholarly journals Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data

2020 ◽  
Vol 12 (23) ◽  
pp. 9888
Author(s):  
Jiajun Shen ◽  
Guangchuan Yang
Keyword(s):  
Logistic Regression ◽  
Traffic Flow ◽  
Binary Logistic Regression ◽  
Regression Modeling ◽  
Mitigation Strategies ◽  
Heterogeneous Traffic ◽  
Crash Risk ◽  
Flow Data ◽  
Speed Variation ◽  
Logistic Regression Modeling

This paper investigates the impacts of heavy vehicles (HV) on speed variation and assesses the rear-end crash risk for four vehicle-following patterns in a heterogeneous traffic flow condition using three surrogate safety measures: speed variation, time-to-collision (TTC), and deceleration rate to avoid a crash (DRAC). A video-based data collection approach was employed to collect the speed of each individual vehicle and vehicle-following headway; a total of 3859 vehicle-following pairs were identified. Binary logistic regression modeling was employed to assess the impacts of HV percentage on crash risk. TTCs and DRACs were calculated based on the collected traffic flow data. Analytical models were developed to estimate the minimum safe vehicle-following headways for the four vehicle-following patterns. Field data revealed that the variation of speed first increased with HV percentage and reached the maximum when HV percentage was at around 0.35; then, it displayed a decreasing trend with HV percentage. Binary logistic regression modeling results suggest that a high risk of rear-end collision is expected when HV percentage is between 0.19 and 0.5; while, when HV percentage is either below 0.19 or exceed 0.5, a low risk of rear-end collision is anticipated. Analytical modeling results show that the passenger car (PC)-HV vehicle-following pattern requires the largest minimum safe space headway, followed by HV-HV, PC-PC, and HV-PC vehicle-following patterns. Findings from this research present insights to transportation engineers regarding the development of crash mitigation strategies and have the potential to advance the design of real-time in-vehicle forward collision warnings to minimize the risk of rear-end crash.

scholarly journals A Crash Prediction Method Based on Artificial Intelligence Techniques and Driving Behavior Event Data

2021 ◽  
Vol 13 (11) ◽  
pp. 6102
Author(s):  
Yunjong Kim ◽  
Juneyoung Park ◽  
Cheol Oh
Keyword(s):  
Traffic Flow ◽  
Traffic Accidents ◽  
Prediction Method ◽  
Driving Behavior ◽  
Crash Risk ◽  
Gradient Boosting ◽  
Event Data ◽  
Trajectory Data ◽  
Data Set ◽  
Ensemble Technique

Various studies on how to prevent and deal with traffic accidents are ongoing. In the past, the key research emphasis was on passive accident response measures that analyzed roadway-based historical data to identify road sections with high crash risk. Through assessing crash risks by analyzing simulation data and actual vehicle driving trajectory data, this study suggests a method of effectively preventing accidents before they happen. In this analysis, using digital tachograph (DTG) data, which is the vehicle trajectory data for commercial vehicles running on Korean highways, hazardous and normal traffic flows were identified and extracted. Driving behavior event data for both types of traffic flow was processed by measuring safety indicators through the extracted data. Safety indicators with a high impact on traffic flow classification were then extracted using gradient boosting, a representative ensemble technique. A neural network analysis was performed using the extracted safety indicators as independent variables to create a traffic flow classifier, which had a high accuracy of 94.59%. The DTG data set was also classified based on the severity of each accident that occurred in the studied roadway, the time of the accident, and the weather; the results were compiled to enable comprehensive accident prediction. It is expected that proactive crash prevention will be possible in the future by evaluating real-time accident risks using the findings and ensemble-based methodologies of this paper.

scholarly journals Novice Drivers' Risky Driving Behavior, Risk Perception, and Crash Risk: Findings From the DRIVE Study

2009 ◽  
Vol 99 (9) ◽  
pp. 1638-1644 ◽  
Author(s):  
Rebecca Ivers ◽  
Teresa Senserrick ◽  
Soufiane Boufous ◽  
Mark Stevenson ◽  
Huei-Yang Chen ◽  
...  
Keyword(s):  
Risk Perception ◽  
Driving Behavior ◽  
Crash Risk ◽  
Risky Driving ◽  
Novice Drivers

Crash Risk and Risky Driving Behavior Among Adolescents During Learner and Independent Driving Periods

2018 ◽  
Vol 63 (5) ◽  
pp. 568-574 ◽  
Author(s):  
Pnina Gershon ◽  
Johnathon P. Ehsani ◽  
Chunming Zhu ◽  
Kellienne R. Sita ◽  
Sheila Klauer ◽  
...  
Keyword(s):  
Driving Behavior ◽  
Crash Risk ◽  
Risky Driving

scholarly journals Examining Risky Driving Behavior Among Older Drivers Using Self-Reported and Objective Data

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 465-465
Author(s):  
Jennifer Zakrajsek ◽  
Lisa Molnar ◽  
David Eby ◽  
David LeBlanc ◽  
Lidia Kostyniuk ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Seat Belt ◽  
Driving Behavior ◽  
Video Data ◽  
Older Drivers ◽  
Driving Frequency ◽  
Middle Aged ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Study Results

Abstract Motor vehicle crashes represent a significant public health problem. Efforts to improve driving safety are multifaceted, focusing on vehicles, roadways, and drivers with risky driving behaviors playing integral roles in each area. As part of a study to create guidelines for developing risky driving countermeasures, 480 drivers (118 young/18-25, 183 middle-aged/35-55, 179 older/65 and older) completed online surveys measuring driving history, risky driving (frequency of engaging in distracted [using cell phone, texting, eating/drinking, grooming, reaching/interacting] and reckless/aggressive [speeding, tailgating, failing to yield right-of-way, maneuvering unsafely, rolling stops] driving behaviors), and psychosocial characteristics. A cluster analysis using frequency of the risky behaviors and seat belt use identified five risky behavior-clusters: 1) rarely/never distracted-rarely/never reckless/aggressive (n=392); 2) sometimes distracted-rarely/never reckless/aggressive (n=33); 3) sometimes distracted-sometimes reckless/aggressive (n=40); 4) often/always distracted-often/always reckless/aggressive (n=11); 5) no pattern (n=4). Older drivers were more likely in the first/lowest cluster (93.8% of older versus 84.2% of middle-aged and 59.3% of young drivers; p<.0001). Fifteen older drivers participated in a follow-up study in which their vehicles were equipped with a data acquisition system that collected objective driving and video data of all trips for three weeks. Analysis of video data from 145 older driver trips indicated that older drivers engaged in at least one distracted behavior in 115 (79.3%) trips. While preliminary, this suggests considerably more frequent engagement in distracted driving than self-reported and that older drivers should not be excluded from consideration when developing risky driving behavior countermeasures. Full study results and implications will be presented.

scholarly journals Identifying Interactive Factors That May Increase Crash Risk Between Young Drivers and Trucks: A Narrative Review

2021 ◽  
Vol 18 (12) ◽  
pp. 6506
Author(s):  
Melissa R. Freire ◽  
Cassandra Gauld ◽  
Angus McKerral ◽  
Kristen Pammer
Keyword(s):  
Review Literature ◽  
Narrative Review ◽  
Crash Risk ◽  
Risky Driving ◽  
Vehicle Performance ◽  
Young Driver ◽  
Young Drivers ◽  
Safety Research ◽  
Increased Risk ◽  
Performance Limitations

Sharing the road with trucks is associated with increased risk of serious injury and death for passenger vehicle drivers. However, the onus for minimising risk lies not just with truck drivers; other drivers must understand the unique performance limitations of trucks associated with stopping distances, blind spots, and turning manoeuverability, so they can suitably act and react around trucks. Given the paucity of research aimed at understanding the specific crash risk vulnerability of young drivers around trucks, the authors employ a narrative review methodology that brings together evidence from both truck and young driver road safety research domains, as well as data regarding known crash risks for each driving cohort, to gain a comprehensive understanding of what young drivers are likely to know about heavy vehicle performance limitations, where there may be gaps in their understanding, and how this could potentially increase crash risk. We then review literature regarding the human factors affecting young drivers to understand how perceptual immaturity and engagement in risky driving behaviours are likely to compound risk regarding both the frequency and severity of collision between trucks and young drivers. Finally, we review current targeted educational initiatives and suggest that simply raising awareness of truck limitations is insufficient. We propose that further research is needed to ensure initiatives aimed at increasing young driver awareness of trucks and truck safety are evidence-based, undergo rigorous evaluation, and are delivered in a way that aims to (i) increase young driver risk perception skills, and (ii) reduce risky driving behaviour around trucks.

Sign in / Sign up

Export Citation Format

Share Document