Conflict Situations and Driving Behavior in Road Traffic – An Analysis Using Eyetracking and Stress Measurement on Car Drivers

2020 ◽  
pp. 86-103
Author(s):  
Swenja Sawilla ◽  
Christine Keller ◽  
Thomas Schlegel
Keyword(s):  
Road Traffic ◽  
Stress Measurement ◽  
Driving Behavior ◽  
Conflict Situations

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.

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 Research on the classification for road traffic visibility based on the characteristics of driving behaviour – a driving simulator experiment

2020 ◽  
Vol 3 (1) ◽  
pp. 30-36
Author(s):  
Kun Wang ◽  
Weihua Zhang ◽  
Zhongxiang Feng ◽  
Cheng Wang
Keyword(s):  
Traffic Safety ◽  
Driving Simulator ◽  
Road Traffic ◽  
Driving Behavior ◽  
Control Measures ◽  
Content Type ◽  
The Road ◽  
Road Traffic Safety ◽  
Fine Classification

Purpose The purpose of this paper is to perform fine classification of road traffic visibility based on the characteristics of driving behavior under different visibility conditions. Design/methodology/approach A driving simulator experiment was conducted to collect data of speed and lane position. ANOVA was used to explore the difference in driving behavior under different visibility conditions. Findings The results show that only average speed is significantly different under different visibility conditions. With the visibility reducing, the average vehicle speed decreases. The road visibility conditions in a straight segment can be divided into five levels: less than 20, 20-30, 35-60, 60-140 and more than 140 m. The road visibility conditions in a curve segment can be also divided into four levels: less than 20, 20-30, 35-60 and more than 60 m. Originality/value A fine classification of road traffic visibility has been performed, and these classifications help to establish more accurate control measures to ensure road traffic safety under low-visibility conditions.

scholarly journals Road Traffic Dynamic Pollutant Emissions Estimation: From Macroscopic Road Information to Microscopic Environmental Impact

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 53
Author(s):  
Giovanni De Nunzio ◽  
Mohamed Laraki ◽  
Laurent Thibault
Keyword(s):  
Air Quality ◽  
Road Traffic ◽  
Estimation Error ◽  
Driving Behavior ◽  
Pollutant Emissions ◽  
Road Segment ◽  
Traffic Information ◽  
Volume Data ◽  
Modeling Framework ◽  
Boundary Parameter

Air pollution poses a major threat to health and climate, yet cities lack simple tools to quantify the costs and effects of their measures and assess those that are most effective in improving air quality. In this work, a complete modeling framework to estimate road traffic microscopic pollutant emissions from common macroscopic road and traffic information is proposed. A machine learning model to estimate driving behavior as a function of traffic conditions and road infrastructure is coupled with a physics-based microscopic emissions model. The up-scaling of the individual vehicle emissions to the traffic-level contribution is simply performed via a meta-model using both statistical vehicles fleet composition and traffic volume data. Validation results with real-world driving data show that: the driving behavior model is able to maintain an estimation error below 10% for relevant boundary parameter of the speed profiles (i.e., mean, initial, and final speed) on any road segment; the traffic microscopic emissions model is able to reduce the estimation error by more than 50% with respect to reference macroscopic models for major pollutants such as NOx and CO2. Such a high-resolution road traffic emissions model at the scale of every road segment in the network proves to be highly beneficial as a source for air quality models and as a monitoring tool for cities.

scholarly journals Female Drivers’ Attitudes and Behavior Regarding Traffic Regulations in Riyadh, Saudi Arabia.

2021 ◽  
Author(s):  
Abdulrahman Bin Wahaq ◽  
Amen Bawazir
Keyword(s):  
Saudi Arabia ◽  
Road Traffic ◽  
Driving Behavior ◽  
Aggressive Driving ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Riyadh City ◽  
Dangerous Driving ◽  
And Behavior ◽  
Study Participants

Abstract Background: Road traffic injuries (RTIs) are of great concern, as they have the second-highest fatality rate in the world. This is also true in Saudi Arabia. Objectives: This study aims to identify whether females in Riyadh commonly have aggressive, dangerous driving behavior.Methods: A cross-sectional survey was used to collect data from female car drivers in Riyadh City. A validated Dulla index was used as the instrument to identify the level of aggressive, dangerous driving behavior among the study participants.Results: The participants comprised 407 females. The majority were in the age group of younger than 30 years (44.5%), married (54.8%), at university (44.7%), house owners with personal property (64.1%), employees (63.6%), and with a middling monthly income (32.2%). The sum of the scores from the Dula Dangerous Driving Index (DDDI) was categorized into “inadequate” and “adequate”. The overall prevalence for the inadequate index was 48.4% for the negative cognitive/emotional driving (NCED) subscale, 42.3% for the aggressive driving (AD) subscale, and 48.2% for the risky driving (RD) subscale.Conclusion: Generally, all of the females who participated in this study had reasonably good knowledge of traffic rules and regulations based on the DDDI. This research is essential for decision-makers to formulate and set priorities for enhancing adherence to traffic regulations for the safety of the community.

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.

scholarly journals Effects of Marking Automated Vehicles on Human Drivers on Highways

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 286 ◽  
Author(s):  
Tanja Fuest ◽  
Alexander Feierle ◽  
Elisabeth Schmidt ◽  
Klaus Bengler
Keyword(s):  
Road Traffic ◽  
Driving Behavior ◽  
Sensor Technology ◽  
Lane Change ◽  
Objective Data ◽  
Automated Vehicles ◽  
Traffic Jam ◽  
Automated Vehicle ◽  
Time Headway ◽  
First Time

Due to the short range of the sensor technology used in automated vehicles, we assume that the implemented driving strategies may initially differ from those of human drivers. Nevertheless, automated vehicles must be able to move safely through manual road traffic. Initially, they will behave as carefully as human learners do. In the same way that driving-school vehicles tend to be marked in Germany, markings for automated vehicles could also prove advantageous. To this end, a simulation study with 40 participants was conducted. All participants experienced three different highway scenarios, each with and without a marked automated vehicle. One scenario was based around some roadworks, the next scenario was a traffic jam, and the last scenario involved a lane change. Common to all scenarios was that the automated vehicles strictly adhered to German highway regulations, and therefore moved in road traffic somewhat differently to human drivers. After each trial, we asked participants to rate how appropriate and disturbing the automated vehicle’s driving behavior was. We also measured objective data, such as the time of a lane change and the time headway. The results show no differences for the subjective and objective data regarding the marking of an automated vehicle. Reasons for this might be that the driving behavior itself is sufficiently informative for humans to recognize an automated vehicle. In addition, participants experienced the automated vehicle’s driving behavior for the first time, and it is reasonable to assume that an adjustment of the humans’ driving behavior would take place in the event of repeated encounters.

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