Causes Of Road Traffic Accidents Research Articles

Perception and Practices Towards Road Safety Rules and Regulations Amongst Pakistani Population

Background: Road traffic accidents (RTAs) remain a critical public health issue in densely populated urban areas like Latifabad, Hyderabad. Understanding the community's perception and practices towards road safety can inform targeted interventions to reduce RTAs. Objective: This study aims to explore the perception and practices regarding road traffic rules and regulations among residents of various units in Latifabad, Hyderabad, Pakistan. Methods: A cross-sectional descriptive study was conducted from March to August 2023 in Latifabad, Hyderabad. It involved 400 commuters aged 18 years and above, from either gender, residing in the area. Participants were included irrespective of their possession of a driving license, provided they consented to participate. Data on their perceptions and practices concerning road traffic were collected using a structured written questionnaire. Results: The participant demographic included 64.7% males and 35.3% females with an average age of 22.6 ± 1.2 years; the majority were students. Nearly half (48.0%) attributed poor road conditions as a primary cause of RTAs. Over half (57.0%) recognized driving without a valid license as an offense, with females significantly more aware (p<0.05) of the risks of improper overtaking and the links between illiteracy, irresponsible behavior, and increased RTAs. There was no significant difference in the scores for violations and lapses related to injury exposure, though a significant association was found between errors and injury exposure. Conclusion: Despite possessing adequate knowledge, participants frequently displayed a dismissive attitude towards compliance with traffic laws, normalizing risky behaviors and potentially increasing the risk of road traffic accidents and injuries.

The influences of human factors on road accidents in Romania in the period 2013 – 2022.

The main objective of the study is to establish a correlation between the main causes of road traffic accidents in Romania and “human factors”. The aim of the study is to show that accidents are attributed exclusively to human mistakes in a higher percentage than. The data used in the research are taken from the database of the Traffic Police-for Road traffic accidents during 2013-2022 and from the database of the Romanian Road Authority - ARR - for road safety inspections during 2018-2021. From the perspective of human factors, the research will give answers on the nature of the causes that lead to the production / favouring of road accidents in Romania and will propose solutions to address road risks to improve road infrastructure safety. As a case study, the research will focus on the human factors that contribute to road accidents on National Road 2. Compared to other national roads, on DN 2 road accidents occur with the highest number of fatalities, approximately 8.28% of all road deaths nationwide. The analysis of findings from road safety inspection reports carried out on DN2 shows that there is a correlation between road infrastructure non-conformities and causes of road traffic accidents.

Tackling the Menace of Speed Violation among Motorcyclists in Nigeria: A Policy Brief

Motorcycle accidents in Nigeria are of particular concern because they rank among the top two vehicle causes of road traffic accidents in the country. Also, the motorcycle plies every road type in the country, and over-speeding tops its causes of accidents, a cause that also ranks top in the human causes of all accidents in all vehicle types. The partial or complete ban on the use of motorcycles has proven to be ineffective in various regions of the country due to its role in sustaining the livelihood of many Nigerians. However, on a policy level, motorcycle accidents have been neglected in Nigeria despite changing policies and intervention programmes since the pre-1939 era of Road Traffic Management. Currently, it is paramount to investigate Motorcycle accidents, emphasizing over-speeding as an emergency. Therefore, the aim of this review is to investigate any existing policy that tackles motorcycle accidents, whether at the national or regional level and develop it to make recommendations for an independent policy that should urgently regulate the motorcycle means of transportation and subsequently tackle motorcycle accidents in Nigeria.

A randomized controlled trial of nurse-led Brief Focused Intervention for patients with alcohol use-related mild traumatic brain injury in the emergency and casualty services of a tertiary hospital.

Driving under the influence of alcohol is one of the leading causes of road traffic accidents in India. Individuals with acute injuries often present to emergency hospital services. Carrying out brief interventions in the emergency can prevent further injury and even progression to severe patterns of drinking. However, there are no known studies from India examining the effectiveness of such interventions in emergency settings. Against this background, the objective of this randomized controlled trial was to evaluate the effectiveness of a nurse-led Brief Focused Intervention (BFI) in comparison with the minimal intervention for patients with mild Traumatic Brain Injury (TBI) reporting to the emergency and casualty services of a tertiary hospital in Bengaluru, South India, who screened positive for alcohol use. The BFI comprised a video portraying the effects of alcohol on the brain and muscles and brief advice on how to reduce or avoid alcohol use. Subjects (N = 90) were randomly allocated to two groups: BFI (n = 45) or Minimal Intervention Group (MIG) (n = 45). Standardized tools were used to assess both groups on specific outcomes for up to six months following discharge from the emergency and casualty services. At the end of six months, complete data wasavailable for 73 patients. Participants' (N = 73) mean age was 35 years (standard deviation [SD]-11). Over the six-month follow-up, the BFI group reported significantly lesser quantity of alcohol consumption (Alcohol Use Disorder Identification Test [AUDIT] Score - 5.03, SD 4.09, 95% confidence interval [CI] = 3.70, 6.35) compared to MIG (AUDIT Score - 9.76, SD 2.96, 95% CI = 8.73, 10.80), and fewer alcohol use-related problems in BFI group (Mean - 4.18, SD 3.21, 95% CI = 3.14, 5.22) compared to MIG (Mean - 5.88, SD 2.59, 95% CI = 4.98, 6.79). Results of logistic regression showed that being in MIG as well as baseline hazardous use of alcohol were associated with unfavorable outcomes at the end of six months follow-up. Findings provide the first known evidence from India for the effectiveness of nurse-led BFI in the emergency and casualty services in improving post-discharge outcomes for patients with alcohol use-related mild TBI. While the findings of the study are statistically significant, these findings also have significant clinical relevance, as they have shown that the BFI improved clinical outcomes. Thus, brief interventions should be implemented for these patients whenever possible in the Emergency setting.

A MODERN SOLUTION THAT HAS A POSITIVE IMPACT ON LOGISTICS SERVICES AND SIGNIFICANTLY REDUCES INTERSECTION FATALITIES

Year after year, the problems on the world's roads are getting worse. According to the UN Economic Commission for Europe (ECE) 2006 Report on the 1968 Geneva Convention "Road Signs and Signals", the rules are periodically updated to take into account the growing demands for human safety, environmental protection, and the rapid development of technological progress around the world (Report IEC UN, New York and Geneva, 2007). Unfortunately, for about a hundred years now, no drastic measures have been taken against the causes of road traffic accidents around the world. Our research is focused on reducing intersection congestion, traffic accidents, and related deaths. A new road sign and traffic light were invented. According to the conclusions of domestic and foreign scientists, the invented sign and traffic light should be a great success in practice.

Causes and Strategies for Reducing Road Traffic Accidents in the Bongo District of Ghana

Aim: This study intends to assess drivers’ state of knowledge on the main causes and the best strategies capable of reducing road traffic accidents in the Bongo District of Ghana.
 Study Design: The study was a descriptive design.
 Place and Duration of Study: The study was conducted in the Bongo District of the Upper East Region of Ghana between July 2023 and August 2023.
 Methodology: The researcher conducted a survey involving 100 drivers who were randomly selected from a driver population of 120 in the Bongo District. The data were analyzed using Microsoft Excel [Version 19]. The results of the data analysis were presented as tables and charts.
 Results: The study found that the main causes of road traffic accidents emanate from human error such as low driving skills, excessive speeding, and poor vision of driver. Also, road conditions like bad road networks, no lane markings, and no speed limit signs were found as causal factors of road traffic accidents. Faulty vehicles with causative factors such as defective tyres, brakes, and lights were noted to be also responsible for road accidents. Nevertheless, the least factor was environmental conditions such as bad weather conditions and stray animals being enumerable as environmental conditions which cause road accidents. The study further found that the best strategies for curbing road traffic accidents were enforcement of traffic laws, education and training of drivers, obeying traffic rules by drivers, and maintaining vehicles well.
 Conclusion: The study, therefore, concluded that human factors play a central role in contributing to road accidents and curbing road traffic accidents. The research findings can be used to guide stakeholders in formulating and prioritizing interventions that are more human-centred for the curbing of road traffic accidents in the Bongo District of Ghana.

Accident Severity Detection Using Machine Learning A Review

One of the greatest challenges in today's world is traffic accidents. It results in fatalities, accidents, and property damage. Making a model that can accurately predict traffic accidents is difficult. The objective of this project is to create a classification system for injuries based on a set of influential factors, including the environment, vehicle speed, driver behaviour, etc. Using data related to traffic accidents, several algorithms are utilized, including AdaBoost, Logistic Regression (LR), Naive Bayes (NB), and Random Forests (RF). Some of the best algorithms are the most effective, including Random Forest, Naive Bayes, and Ada Boost. Compared to LR, NB, and AdaBoost, the RF algorithm performed better, with 75.5% accuracy. To employ various machine learning classification algorithms for traffic accident prediction, the goal of this study is to uncover the underlying causes of road traffic accidents. then decide which prediction model is most likely to help decrease these highway accidents. This paper's goal is to review different authentication procedures offered by numerous scholars around the world.

Driver distraction detection via multi‐scale domain adaptation network

AbstractDistracted driving is the leading cause of road traffic accidents. It is essential to monitor the driver's status to avoid traffic accidents caused by distracted driving. Current research on detecting distracting behaviours focuses on analysing image features using convolutional neural networks (CNNs). However, the generalisation ability of the current distracted driving models is limited. This paper aims to improve the generalisation ability of distracted driving models that are affected by factors such as the driver himself, the background, the monitoring angle, and so on. A new driver distraction detection method, which is referred to as multi‐scale domain adaptation network (MSDAN), was proposed to improve model adaptability. The method consists of three stages: first, multi‐scale convolution was introduced to build a new backbone to accommodate better the valuable feature of the target on different scales. Secondly, the authors designed the domain adaptation network to improve the model's adaptability to the difference in data sources through adversarial training. Finally, dropout is added to the fully connected layer to increase the model's generalisation ability. The comparison results on the large‐scale driver distraction detection dataset show that the authors’ method can accurately detect driver distraction and has good generalisation performance, with an accuracy improvement in the cross‐driver and cross‐dataset experiments.

EVALUATION OF THE STABILITY OF A TWO-AXLE WHEELED MACHINE WHEN SLIDDING DURING THE BRAKING PROCESS

One of the common causes of road traffic accidents is the skidding of the car during braking with the rear wheels locking in advance. It was determined that the cause of the car skidding is the anticipatory blocking of the rear wheels of the car. However when building dynamic and mathematical models the degree of excessive braking of the rear wheels wasn't taken into account. The conducted research made it possible to obtain analytical expressions for estimating the steady speed of the car when braking with locked rear and unlocked front wheels at different degrees of underutilization of the coupling weight on the front axle. A new indicator has been introduced – the degree of underutilization of the car's hitch weight when the rear axle skids. The goal of the study is to improve the accuracy of the evaluation of the car's indicators when braking with the front non-locked and rear locked wheels by improving the method of determining the total reaction of the road on the rear wheels. To achieve the goal, the task of determining the total reaction of the road on the rear wheels was solved. As a result of the conducted research, analytical expressions were obtained for estimating the steady speed of the car when braking with locked rear and unlocked front wheels at different degrees of underutilization of the coupling weight on the front axle. In the obtained analytical expressions, a new indicator is used – the degree of underutilization of the towing weight of the car

Modeling of cable-stayed bridge and modal analysis

Problem. The considered Characteristics of the own forms of a cable-stayed bridge according to several of its schemes. The analysis of the influence of the type of resistance on the characteristics of the own forms of resistance for different sets was carried out. Among the types of resistance, full rigidity, hinge and no resistance were taken. Goal. The purpose of the study is to determine the causes of registered traffic accidents and emergencies associated with them. To do this, it is proposed to analyze the own forms of the cable-stayed bridge for various types of its contact (resistance) with the columns. The object of the study is a cable-stayed bridge with a service life of about 50 years. The bridge is a system of three types of elements: pylon, span and columns. The subject of the study is the forms of natural oscillations of the bridge according to its various models. Methodology. The study was carried out by using oscillation and finite element methods. It should also be noted that the original methods for constructing models of complex machine-building systems were used, which were developed directly by the author. Results. Based on the results of the research, three-dimensional finite element models of parts and the entire bridge as a single system for various types of resistance were built, and their own forms were obtained for each model of the system. The study allows us to draw conclusions on specifying the causes of road traffic accidents and ways to prevent them. Originality. Regarding the methods of constructing models of a cable-stayed bridge, we note their uniqueness. Taking into account the features of modeling, it became possible to create several states of interaction between the parts of the system. To conduct reliable research for a real model of a structure, it is necessary to have reliable data about the real state of the connections of its elements. This state can range from absolutely rigid to free. Accounting for these features is possible with the help of a unique author's methodology for building models using the universal capabilities of the finite element method. Previous studies of the bridge by other methods did not allow us to solve the tasks set for the occurrence of traffic accidents. Practical value. The results of the work have direct practical application. Based on the results of the work, conclusions were drawn about ways to improve the reliability of the system and the measures to prevent traffic accidents.

Comparison of mortality prediction models for road traffic accidents: an ensemble technique for imbalanced data

BackgroundInjuries caused by RTA are classified under the International Classification of Diseases-10 as ‘S00-T99’ and represent imbalanced samples with a mortality rate of only 1.2% among all RTA victims. To predict the characteristics of external causes of road traffic accident (RTA) injuries and mortality, we compared performances based on differences in the correction and classification techniques for imbalanced samples.MethodsThe present study extracted and utilized data spanning over a 5-year period (2013–2017) from the Korean National Hospital Discharge In-depth Injury Survey (KNHDS), a national level survey conducted by the Korea Disease Control and Prevention Agency, A total of eight variables were used in the prediction, including patient, accident, and injury/disease characteristics. As the data was imbalanced, a sample consisting of only severe injuries was constructed and compared against the total sample. Considering the characteristics of the samples, preprocessing was performed in the study. The samples were standardized first, considering that they contained many variables with different units. Among the ensemble techniques for classification, the present study utilized Random Forest, Extra-Trees, and XGBoost. Four different over- and under-sampling techniques were used to compare the performance of algorithms using “accuracy”, “precision”, “recall”, “F1”, and “MCC”.ResultsThe results showed that among the prediction techniques, XGBoost had the best performance. While the synthetic minority oversampling technique (SMOTE), a type of over-sampling, also demonstrated a certain level of performance, under-sampling was the most superior. Overall, prediction by the XGBoost model with samples using SMOTE produced the best results.ConclusionThis study presented the results of an empirical comparison of the validity of sampling techniques and classification algorithms that affect the accuracy of imbalanced samples by combining two techniques. The findings could be used as reference data in classification analyses of imbalanced data in the medical field.

Validation of an EEG-based Neurometric for online monitoring and detection of mental drowsiness while driving.

The driving drowsiness has been identified as one of the major causes of road traffic accidents, causing fatalities and permanent injuring. Drowsy drivers are prone to suddenly lose control of the car, mostly without any prior behavioral cue. The present study involved 19 participants in a simulated driving protocol, designed to induce mental drowsiness into the drivers. The objective of the study consisted in testing an innovative Electroencephalographic (EEG)-based index, the MDrow index, in detecting the driving drowsiness. Such an index, derived from parietal EEG channels, was already validated in our previous work achieving outstanding performance with respect to more conventional techniques. In this study, the possibility of obtaining a similar index from the frontal sites in order to foster its exploitation in real environments has been investigated. The results demonstrated the capability of the "frontal" MDrow index in evaluating the driving drowsiness experienced by the participants with performance comparable to that one previously validated over parietal sites. Also, the impact of the reduction of the electrodes number on index reliability has been investigated, in order to evaluate its compatibility with current wearable EEG devices.

RESEARCH OF DEFORMATION AND DESTRUCTION OF CAR ELEMENTS UNDER EXTREME CONDITIONS

The article is devoted to the improvement of the methodology for the examination of the operation the vehicles in extreme conditions that occur during road traffic accidents (RTA). Of great interest in the field of road safety are the tasks of determining the causes of RTA and the conditions under which they occur. There are computer systems that allow modeling the processes of interaction automobile vehicles with obstacles and among themselves during an accident. These developments based on various mathematical models of the car collision process. The most reliable and promising are systems that based on methods for analyzing plastic deformations and destruction of car structural elements during an accident. The basis of these methods is the law of conservation energy or the model of the transition of the kinetic energy of a moving vehicle into the potential energy of a structure deformed after an accident. This process described with the help of the parameter – the energy equivalent of the speed. The analysis of the object is study – automobile body structures carried out. They classified as thin-walled shell structures that are easily plastically deformed. The available methods for solving the problems of analysis the elastic-plastic deformations in such structures do not allow providing the high accuracy of the solution necessary for the examination of the RTA.
 Purpose – improvement of the methodology for conducting an autotechnical examination of an accident.
 Methodology: solution of inverse problems of continuum mechanics.
 Results: a variant of the finite element representation of the design and a method of the variational approach to determining the energy criteria of the process under study were developed. It provides the greatest accuracy in solving the tasks.
 Practical implications: it is expedient to apply the obtained results when carrying out an autotechnical examination of an accident.

Pattern of Maxillofacial Injuries in Road traffic Accident During Covid-19

Objectives: This study aims to assess the prevalence and pattern of maxillofacial injuries presented at department of oral & maxillofacial surgery (OMFS), Mayo Hospital, Lahore, Pakistan during COVID- 19 pandemic. Determining patterns of occurrence of maxillofacial trauma and etiology can improve early diagnosis and treatment, thereby can decrease percentage of morbidity and mortality associated with these fractures. Place and duration; Sample data was collected from Department of OMFS, Mayo hospital, during January 2021 till December 2021. Methodology: (n=263) patients with confirmed diagnosis of maxillofacial trauma participated in this research. Record of all patients including sex, age, cause of road traffic accidents, radiograph, fracture site, vehicle type, use of seat belts/helmets, time to reach the hospital, and other safety devices were reviewed and evaluated. Results: There were (n=299, 57.27%) mandibular, (n=102, 19.50 %) zygomatic, (n=100, 19.10 %) maxillary, (n=12, 2.29 %) nasal, and (n=9, 1.70 %) frontal injuries. Maxillofacial trauma was more prevalent in males (87%) than in females (13%) and was commonly seen in young age groups (21-30 years). Two-wheelers (motorcycle) was the vehicle used in most of the cases reported (68.4 %) and major cause of RTA was inattentiveness to traffic rules (31.9 %). In road traffic accidents, mandible (57.27%) is the most common fractured bone, followed by Zygomatic Bone fracture. Conclusion: It is concluded that maxillofacial injuries in males (20 to 30 years) of age range were more prevalent and are observed mostly in mandibular bones. Although cases reported was less in number when compared to previous data, but pattern of fractured facial bones were more or less same.

The Causes of Road Traffic Accidents in Hargeisa City, Somaliland

Road traffic accidents are the eighth leading cause of death globally, and the leading cause of death for young people aged 15-29 (productive age group). The objective of the thesis is to examine the causes of road traffic accidents in Hargeisa City. Both primary and secondary data sources were used. Primary data was collected from a sample of pedestrians and drivers by using a questionnaire and field observation. Also, archival documents and interviews with key informants from the Hargeisa traffic police. About 144 sample pedestrians and 80 sample drivers were drawn from a sampling frame of pedestrians and drivers using a purposive sampling technique. The data was analyzed through both descriptive and inferential statistics and narration for quantitative and qualitative information, respectively . According to the findings of the chi-square test, the following factors are associated with road traffic accidents in Hargeisa city: drivers age (0.001), driver′s speed (0.003), failure to give pedestrians priority to pass first as required by law (0.027), use of mobile phones (0.008), and non-use of seat belts (0.009), drivers violating traffic rules and regulations (0.015). As the main contributing factors to road traffic accidents in the city. The study recommended that unless immediate actions to revert the problem are taken, it will worsen in the future as motorization and the population of the city grow altogether. Keywords: Causes, Road Traffic Accidents, Pedestrians, Drivers, Hargeisa, Somaliland DOI: 10.7176/RHSS/12-3-03 Publication date: February 28 th 2022

FPT: Fine-Grained Detection of Driver Distraction Based on the Feature Pyramid Vision Transformer

According to the surveys of the World Health Organization, distracted driving is one of main causes of road traffic accidents. To improve road traffic safety, real-time detection of drivers’ driving behavior is very important for the development of highly reliable Advanced Driver Assistance System (ADAS). At present, the deep learning architecture based on a Convolutional Neural Network (CNN) has disadvantages such as large number of parameters and weak global feature extraction ability. Therefore, this paper proposes an innovative driver distraction detection model based on the fusion of a transformer and a CNN, referred to as FPT, which is the first exploration in the field of driver distraction detection. First, we introduce the latest Twins transformer as a benchmark. Then, we design residual embedding to replace block embedding, which can further integrate the convolutional neural network with Transformer and improve the feature extraction ability. In addition, the Multilayer Perceptron (MLP) module with a large parameter occupancy rate in the original transformer structure is replaced with a lightweight group convolution module to reduce computational complexity. Finally, a cross-entropy loss function for label smoothing is designed to guide network learning with significantly differentiated features. Comparison results on two large-scale driver distraction detection datasets show that the proposed FPT offers a better compromise between computational cost and performance compared to the state-of-the-art CNN and Transformer architectures.

Effects of mobile phone texting on postural control and gait parameters in college students

Background: Mobile phone has become a part of everyday life. Mobile phone texting is one of the major causes of road traffic accidents, falls and injuries in young adults. Dual-task activities while using mobile phone increases the risk of falls. Objectives: To find out the changes in postural control and gait parameters while using mobile phone in young college students. Materials and Methods: A total of 100 college students of age 18–25 years participated in the study. NeuroCom Balance Master was used and the subjects were tested with and without phone. The participants had to maintain balance on firm surface and foam surfaces with and without phone. For walking, participants had to walk without phone followed by walking with phone. Outcome measures used were Modified Clinical test for Sensory Interaction on balance and Walk Across for balance and gait respectively. Results: The study showed statistically significant changes in postural control on firm and foam surfaces. Furthermore, the gait parameters such as step length, speed and step length symmetry were altered significantly, except step width while using the mobile phone. Conclusion: Subjects showed increased postural sway, step length and step length symmetry while the step width and speed decreased on using the mobile phone.

Analysis of Factors Affecting Road Traffic Accidents in North Cyprus

Road traffic accidents are a global issue. North Cyprus exhibits a similar trend to the rest of the world. Urbanization and the increasing number of vehicles come along with the increase of road traffic accidents. The objectives of the current study are to draw attention to the issue of road traffic accidents and to identify which factors are the main causes of road traffic accidents in North Cyprus. This article follows a two-stage study to examine the causes of this critical issue. In the first stage, a survey was conducted to determine local public opinions. The participants were asked about how environmental factors, vehicle factors, human factors, and road factors impact accidents. According to the survey outcomes, human factors and road factors have the most significant impact. In the second stage of the study, official data from official statistics were considered. The number of accidents, fatalities, and injured people are indicated initially followed by the causes of accidents. Negligent driving and speeding were identified to be the most frequent causes within the studied period. At the end of the study, the common causes of accidents in official statistics and in survey results were specified. Negligent driving and over-speeding were the major causes of road traffic accidents according to the results.

Trends and Contributing Factors to Road Traffic Accidents in Douala

Introduction: An estimated 1.2 million people are killed in road traffic accidents each year, and as many as 50 million are injured, occupying 30 percent to 70 percent of orthopedic beds in developing countries hospitals. But there is limited information on the burden of RTA in Douala. Aim: The study aims at reporting the epidemiology and pattern of road traffic related injuries in Douala in the Littoral Region of Cameroon using available police and health data. Methods: This was a chart review of hospital based and police department records. The records were reviewed over a period of two years in a District hospital in Douala in the Littoral region of Cameroon and over four years in the Police Department in Douala. The data review was done for two months. The records of all patients received in the emergency department of Bonassama District hospital after a road traffic related injury were reviewed for epidemiological variables, type of vehicle involved, severity of injuries, and outcome of the management. For the police records, they were reviewed for epidemiological variables, type of vehicle involved, and the causes of accident. Results: A total of 2,062 persons were involved in 818 cases of road traffic accidents reported by the police department. The major causes of road traffic accident (RTA) included poor mastery of driving, over speeding and left driving. From the Police records, RTA involving a vehicle and motorcycle increased dramatically in the four-year period from 0.39 % (8) in 2015 to 5.82% (120) in 2018. A total of 1,883 persons with injuries from RTA were received and managed by the Bonassama District Hospital. Majority, 1,590(84.4%) [95% CI: 82.7%-86.1%] of the injuries that were managed in the health units were sustained from RTA involving the motorcycle. Most, 893(47.4%) [95% CI: 45.2%-49.7%] of the accidents occurred during the night hours. Conclusion: The available data in both health and police sectors revealed that there was an increase in the number of RTA in the years under study.

Modeling drivers’ scrambling behavior in China: An application of theory of planned behavior

Scrambling behavior is one of the main causes of road traffic accidents in China. This study aimed to investigate the characteristics of drivers’ scrambling behavior and its influencing factors based on the theory of planned behavior. A total of 388 drivers answered the questionnaire and 359 provided valid data. The structure equation model of scrambling behavior showed that positive attitudes towards scrambling behavior, subjective norms and perceived behavior control increased the intention of scrambling behavior. Furthermore, the path coefficient of the structural equation model for the scrambling behavior revealed that attitude was the most important factor influencing scrambling behavior. Thus, to prevent drivers from scrambling, traffic regulators should focus on improving drivers’ attitude towards this behavior, while auxiliary measures should be enacted to regulate drivers' subjective norms and perceptual behavior control. Implications on intervention strategy and policy to reduce scrambling behavior are discussed.