scholarly journals Road Traffic Casualties and Risky Driving Behavior in Hualien County, 2001–2005

2007 ◽  
Vol 19 (3) ◽  
pp. 152-158 ◽  
Author(s):  
Yin-Ming Li
Keyword(s):  
Road Traffic ◽  
Driving Behavior ◽  
Risky Driving

scholarly journals Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Jinshuan Peng ◽  
Yiming Shao
Keyword(s):  
Neural Network ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Time Window ◽  
Driving Behavior ◽  
Practical Significance ◽  
Accurate Identification ◽  
Risky Driving ◽  
Bayesian Filter ◽  
Identification Rate

Risky driving behavior is a major cause of traffic conflicts, which can develop into road traffic accidents, making the timely and accurate identification of such behavior essential to road safety. A platform was therefore established for analyzing the driving behavior of 20 professional drivers in field tests, in which overclose car following and lane departure were used as typical risky driving behaviors. Characterization parameters for identification were screened and used to determine threshold values and an appropriate time window for identification. A neural network-Bayesian filter identification model was established and data samples were selected to identify risky driving behavior and evaluate the identification efficiency of the model. The results obtained indicated a successful identification rate of 83.6% when the neural network model was solely used to identify risky driving behavior, but this could be increased to 92.46% once corrected by the Bayesian filter. This has important theoretical and practical significance in relation to evaluating the efficiency of existing driver assist systems, as well as the development of future intelligent driving systems.

A Computational Model for Driver Risk Evaluation and Crash Prediction using Contextual Data from On-Board Telematics

2020 ◽  
Vol 13 ◽  
Author(s):  
Neetika Jain ◽  
Sangeeta Mittal
Keyword(s):  
Computational Model ◽  
Road Traffic ◽  
Risk Index ◽  
Driving Behavior ◽  
Risky Driving ◽  
Contextual Data ◽  
Vehicle Crash ◽  
Proposed Model ◽  
Driving Risk

Introduction: Vehicle crashes can be hazardous to public safety and may cause infrastructure damage. Risky driving significantly raises the possibility of the occurrence of a vehicle crash. As per statistics by World Health Organization (WHO), approximately 1.35 million people are involved in road traffic crashes resulting in loss of life or physical disability. WHO attributes events like over-speeding, drunken driving, distracted driving, unsafe road infrastructure and unsafe practices such as non-use of helmets and seatbelts to road traffic accidents. As these driving events negatively affect driving quality and enhance risk of vehicle crash, they are termed as negative driving attributes. Methods: A multi-level hierarchical fuzzy rules-based computational model has been designed to capture risky driving by a driver as driving risk index. Data from the onboard telematics device and vehicle controller area network is used for capturing the required information in a naturalistic way during actual driving conditions. Fuzzy rules-based aggregation and inference mechanisms have been designed to alert about the possibility of a crash due to onset of risky driving. Results: On-board telematics data of 3213 sub-trips of 19 drivers has been utilized to learn long term risky driving attributes. Further, current trip assessment of these drivers demonstrates the efficacy of the proposed model in correctly modeling driving risk index of all of them including 7 drivers who were involved in a crash after the monitored trip. Conclusions: In this work, risky driving behavior has been associated not just with rash driving but also other contextual data like driver’s long-term risk aptitude and environmental contexts such as type of roads, traffic volume and weather conditions. Proposed model has been able to correctly identify driver’s dangerous behavior as six drivers who were labeled as dangerous drivers had actually met with crash. Similarly, other 12 drivers indicated safe behavior for 90% of time and didn’t meet crash. Proposed model can be used as alert mechanism to indicate potential crash scenarios to the driver. The current study didn’t study lane changing behavior in detail due to difficulty in capturing road lanes in Indian context. Discussion: In this work, it is aimed to utilize on-board telematics data to develop a computational model that can predict vehicle crash. This work has been done to meet following objectives 1) Design a computational model for long term risk aptitude of a driver. 2) Define negative driving behavior attributes and develop a model to capture them in real time. 3) Develop a method to assess risky environmental circumstances and their effect on negative driving behavior 4) Define a Driving Risk Index that is computed from driver risk aptitude, current driving behavior attributes along with environmental context. 5) Analyze the existing literature for existing state-of-art in this problem domain.

Risky Driving Behavior and Road Traffic Crashes Among Young Asian Australian Drivers: Findings From the DRIVE Study

2010 ◽  
Vol 11 (3) ◽  
pp. 222-227 ◽  
Author(s):  
Soufiane Boufous ◽  
Rebecca Ivers ◽  
Teresa Senserrick ◽  
Robyn Norton ◽  
Mark Stevenson ◽  
...  
Keyword(s):  
Road Traffic ◽  
Driving Behavior ◽  
Traffic Crashes ◽  
Risky Driving ◽  
Road Traffic Crashes

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 Road Casualties and Changes in Risky Driving Behavior in France Between 2001 and 2004 Among Participants in the GAZEL Cohort

2009 ◽  
Vol 99 (7) ◽  
pp. 1247-1253 ◽  
Author(s):  
Aymery Constant ◽  
Louis Rachid Salmi ◽  
Sylviane Lafont ◽  
Mireille Chiron ◽  
Emmanuel Lagarde
Keyword(s):  
Driving Behavior ◽  
Risky Driving

scholarly journals Research on Risky Driving Behavior of Novice Drivers

2019 ◽  
Vol 11 (20) ◽  
pp. 5556
Author(s):  
Longhai Yang ◽  
Xiqiao Zhang ◽  
Xiaoyan Zhu ◽  
Yule Luo ◽  
Yi Luo
Keyword(s):  
Theory Of Planned Behavior ◽  
Planned Behavior ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Risky Behaviors ◽  
Driving Behavior ◽  
Risky Driving ◽  
Novice Drivers ◽  
Driving Skills ◽  
Planned Behavior Theory

Novice drivers have become the main group responsible for traffic accidents because of their lack of experience and relatively weak driving skills. Therefore, it is of great value and significance to study the related problems of the risky driving behavior of novice drivers. In this paper, we analyzed and quantified key factors leading to risky driving behavior of novice drivers on the basis of the planned behavior theory and the protection motivation theory. We integrated the theory of planned behavior (TPB) and the theory of planned behavior (PMT) to extensively discuss the formation mechanism of the dangerous driving behavior of novice drivers. The theoretical analysis showed that novice drivers engage in three main risky behaviors: easily changing their attitudes, overestimating their driving skills, and underestimating illegal driving. On the basis of the aforementioned results, we then proposed some specific suggestions such as traffic safety education and training, social supervision, and law construction for novice drivers to reduce their risky behavior.

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