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.

Identification of Dangerous Driving Behaviors Based on Neural Network and Bayesian Filter

2013 ◽  
Vol 846-847 ◽  
pp. 1343-1346
Author(s):  
Yuan Xin Xu
Keyword(s):  
Neural Network ◽  
Traffic Accidents ◽  
Model Performance ◽  
Real Data ◽  
Driving Behavior ◽  
Bayesian Filter ◽  
Dangerous Driving ◽  
Network Output ◽  
Lane Line ◽  
Distance Data

Identification and amendment dangerous driving behavior timely and accurately is a necessary means to reduce traffic accidents. This paper proposed a dangerous driving behavior identification method based on neural network and Bayesian filter. By using vehicle-mounted radars and cameras obtain movement state information of the vehicles around the host vehicle and lane line distance data, on the basis of which, the identification model is established. Then evaluate model performance by the real data. The test results show that after the correction of neural network output by Bayesian filter, the model accuracy has a sharp rise.

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.

scholarly journals Empirical Modelling for exploring the factors contributing to disability severity from road traffic accidents in Thailand

1970 ◽  
Vol 6 (2) ◽  
pp. 176-185
Author(s):  
Jaratsri Rungrattanaubol ◽  
Anamai Na-udom ◽  
Antony Harfield
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
Empirical Modelling ◽  
Standard Data ◽  
Accident Data ◽  
Computer Based

This paper introduces a computer-based model for predicting the severity of injuries in road traffic accidents. Using accident data from surveys at hospitals in Thailand, standard data mining techniques were applied to train and test a multilayer perceptron neural network. The resulting neural network specification was loaded into an interactive environment called EDEN that enables further exploration of the computer-based model. Although the model can be used for the classification of accident data in terms of injury severity (in a similar way to other data mining tools), the EDEN tool enables deeper exploration of the underlying factors that might affect injury severity in road traffic accidents. The aim of this paper is to describe the development of the computer-based model and to demonstrate the potential of EDEN as an interactive tool for knowledge discovery.

scholarly journals Analisis Perilaku Pengemudi Truk Serta Kontribusinya Pada Kecelakaan

2021 ◽  
Vol 23 (1) ◽  
pp. 79-87
Author(s):  
Budi Dwi Hartanto
Keyword(s):  
Path Analysis ◽  
Direct Effect ◽  
Human Error ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Driving Behavior ◽  
Truck Drivers ◽  
Accident Risk ◽  
Aggressive Driving ◽  
The Relationship

ABSTRAKIn Indonesia, the death rate due to road traffic accidents is still quite high, with some of these accidents involving trucks. Several studies stated that the main cause of traffic accidents is human error. Therefore, research related to the behavior of truck drivers and their contribution to accidents is necessary.There are four variables used in this study, namely green driver (X1), multitasking driving (X2), aggressive driving (Y), and accidents (Z). Path analysis is used to describe the relationship and influence between variables.The results of the analysis show that the green driver variable and the multitasking driving variable simultaneously have a direct effect on aggressive driving behavior, but the two variables have no direct effect on the level of accident risk. Green drivers and multitasking driving have an indirect effect on the level of accident risk through the level of aggressive driving behavior which functions as an intervening variable.ABSTRAKDi Indonesia tingkat kematian yang diakibatkan  kecelakaan lalu lintas jalan masih cukup tinggi, dimana sebagian dari kecelakaan tersebut melibatkan kendaraan angkutan barang (truk). Beberapa penelitian menyebutkan bahwa penyebab utama terjadinya kecelakaan lalu lintas adalah human error. Oleh sebab maka penelitian terkait dengan perilaku pengemudi truk serta kontribusinya pada kecelakaaan perlu untuk dilakukan.Terdapat empat variabel yang digunakan dalam penelitian ini yaitu variabel usia muda serta minim pengalaman (X1), mengemudi dalam kondisi multitasking (X2), mengemudi secara agresif (Y), dan potensi terjadinya kecelakaan (Z). Untuk menggambarkan hubungan dan pengaruh antar variabel digunakan analisis jalur (path analysis).Dari hasil analisis diketahui bahwa variabel usia muda serta minim pengalaman dan variabel mengemudi dalam kondisi multitasking secara simultan berpengaruh langsung terhadap perilaku mengemudi agresif, namun kedua variabel tidak berpengaruh langsung terhadap tingkat resiko kecelakaan. Usia muda serta minim pengalaman dan mengemudi dalam kondisi multitasking berpengaruh tidak langsung terhadap tingkat resiko kecelakaan melalui tingkat perilaku mengemudi agresif yang berfungsi sebagai variabel intervening

scholarly journals HUBUNGAN AGRESSIVE DRIVING BEHAVIOR PENGEMUDI SEPEDA MOTOR DENGAN KECELAKAAN LALU LINTAS (STUDI PADA SISWA SMA DI KABUPATEN SIDOARJO)

2019 ◽  
Vol 13 (2) ◽  
pp. 222
Author(s):  
Mazroh Ilma Soffania
Keyword(s):  
Public Health ◽  
High School ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Driving Behavior ◽  
Senior High School ◽  
Observational Research ◽  
Control Group ◽  
Case Group ◽  
Road Traffic Accidents

Road traffic accident was the public health problem that can decrease public health status. Most of the road traffic acccident involving motorcyclist and mostly among people around 15-19 years old. Agressive driving behavior was one of the factors causing road traffic accidents. The aim of this study to analize the relationship between motorcyclist’s agressive driving behavior with road traffic accidents. This research was analytic observational research with case-control design. The population was senior high school student who riding motorcycle aged ≥ 17 years old in Kabupaten Sidoarjo. Population were divided into two groups, namely case group and control group. Case group were respondents who had road traffic accidents while control group were respondents who never had a road traffic accidents in the last year. The number of respondens were involved 24 respondents in case group and 48 respondents in control group. Sampling were purposive sample in case group and matching sampling in control group by age and sex. The result of analysis using chi-square test  (α = 5 %) showed that agressive driving behavior in motorcyclist has significant relationship of road traffic accidents (p= 0,0006; OR= 5,320). Senior high school students were encouraged to managed time and more prioritised safety while driving to avoid traffic accidents.

scholarly journals Association of Midfacial Fractures with Ophthalmic Injury

2017 ◽  
Vol 10 (2) ◽  
pp. 099-105 ◽  
Author(s):  
Constantinus Politis ◽  
Alexandra Kluyskens ◽  
Titiaan Dormaar
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Time Window ◽  
Maxillofacial Surgery ◽  
Oral And Maxillofacial Surgery ◽  
University Hospitals ◽  
Initial Management ◽  
Midfacial Fractures ◽  
The Mean ◽  
The University

The aim of this study is to evaluate the incidence of ophthalmic complications following midfacial fractures and investigate its relation to surgical or nonsurgical treatment. This article is a retrospective study, describing the spectrum and incidence of ophthalmic injury in 106 patients presenting with midfacial fractures at the Department of Oral and Maxillofacial Surgery of the University Hospitals Leuven over a period of 16 months (January 2013 to April 2014). The mean age of the patients was 45.6 years with a gender distribution of 68 men and 38 women. The main cause of trauma was road traffic accidents. Forty-one patients suffered an ophthalmic injury following the fracture. Twelve of them had a persistent ophthalmic problem. Ophthalmic examination is necessary during the initial management. The time window for preservation of sight is small and treatment should be started immediately. Development of an emergency trauma scale that includes fractures, symptoms of visual impairment, and patient history is recommended and should stimulate a multidisciplinary approach of complex cases.

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