scholarly journals Analysis of influencing factors for rear-end collision on the freeway

2019 ◽  
Vol 11 (7) ◽  
pp. 168781401986507
Author(s):  
Jianfeng Xi ◽  
Hongyu Guo ◽  
Jian Tian ◽  
Lisa Liu ◽  
Weifu Sun
Keyword(s):  
Influencing Factors ◽  
Significant Influence ◽  
Traffic Accidents ◽  
Influence Factors ◽  
Warning System ◽  
Analysis Model ◽  
Freeway Traffic ◽  
Independent Variables ◽  
Driving Speed ◽  
Fatigue Driving

Rear-end collision accounts for the main type of traffic accidents occurring on the freeway. In order to extract the significant influence factors of rear-end collision on the freeway, this study utilized the data of freeway traffic accidents between 2010 and 2015 in China. First, based on quasi-induced exposure theory, the information of driver, vehicle, and road environment was analyzed. Gender, age, driving age, vehicle safety, load, weather, fatigue, driving speed, road alignment, accident time, and visibility were selected as the important factors that might affect rear-end collision. Second, based on logistic regression model, the influencing factors analysis model of freeway rear-end collision was established. In the regression analysis, the possible important factors selected were taken as the independent variables, and the accident responsibility was taken as the dependent variable. Then, the factors that had significant influence on rear-end collision were selected from candidate independent variables by stepwise regression method. Finally, the specific influence of driving age, load, weather, accident time, visibility, fatigue, and driving speed on rear-end collision occurring on the freeway was discussed. The analysis results were explained according to the odds ratio. The research results of this article can provide guidance for the prevention of rear-end collision on the freeway and theoretical support for the development of freeway early warning system.

scholarly journals A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Liu ◽  
Yunfeng Ji ◽  
Yun Gao ◽  
Zhenyu Ping ◽  
Liang Kuang ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Traffic Accidents ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Warning System ◽  
Support Vector ◽  
Eyelid Closure ◽  
Recognition Result ◽  
Fatigue Driving

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

scholarly journals Driving Comfort Evaluation for Manhole Covers and Pavement around Manholes

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Quanman Zhao ◽  
Zhigang Li ◽  
Wenjun Hu ◽  
Xianguang Meng ◽  
Hui Zhang
Keyword(s):  
Root Mean Square ◽  
Influencing Factors ◽  
Influence Factors ◽  
Mean Square ◽  
Height Difference ◽  
Maximum Acceleration ◽  
Driving Speed ◽  
Longitudinal Slope ◽  
Variation Characteristics ◽  
Pavement Damage

In order to study the driving comfort and influencing factors when vehicles pass over manholes and pavement around manholes on an urban road, the deformation and vibration of the manhole cover were considered, a multidegree of freedom vibration model of the human-vehicle-manhole cover was established, and the variation characteristics of driving acceleration was analyzed. The root mean square of weighted acceleration was taken as the basic index, and driving comfort was evaluated based on ISO 2631-1-1997 standard. After that, 9 influencing factors were analyzed, such as driving speed, subsidence of manhole, manhole cover stiffness, and longitudinal slope. Then, grey correlation entropy analysis was used to evaluate the influencing factors, and the main factors were determined. The results showed that the maximum acceleration was 3.6 m/s2 when a vehicle was passing over a manhole cover under the basic parameters. At the same time, the root mean square of weighted acceleration was 0.975 m/s2 and driving comfort degree was “uncomfortable.” Driving direction and vibration of the manhole cover had little influence on driving comfort, while the remaining influencing factors had significant influence on that. The ranking of key influence factors on driving comfort was longitudinal slope, driving speed, height difference caused by pavement damage, height difference caused by manhole cover subsidence, tire stiffness, manhole stiffness, and tire damping. Therefore, in order to ensure driving comfort and safety, damage to pavement around manholes and manhole cover subsidence should be repaired in a timely manner.

scholarly journals ANALISIS PENGARUH BOPO, NPL, NIM, DAN LDR TERHADAP PROFITABILITAS PADA BANK UMUM DI INDONESIA PERIODE 2012-2017

KEUNIS ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 34
Author(s):  
Evi Rohmiati ◽  
Winarni Winarni ◽  
Nina Woelan Soebroto
Keyword(s):  
Regression Analysis ◽  
Significant Influence ◽  
Commercial Banks ◽  
Analysis Model ◽  
Return On Assets ◽  
Independent Variables ◽  
Net Interest Margin ◽  
Analysis Technique ◽  
Independent Variable ◽  
Research Show

<p><em>This research is performed in order to test the influence of the Operation Expenses to Operations Income (BOPO), Non Performing Loan (NPL), Net Interest Margin (NIM), and Loan to Deposit Ratio (LDR) toward Profitability of Commercial Banks in Indonesia Period 2012-2017. </em></p><p><em>            The sample used is 7 Commercial Banks that entered into the list of Commercial Banks Business Activities 2012-2017. The independent variables in this research are BOPO, NPL, NIM, and LDR. While the dependent variable is Profitability which is represented by Return On Assets (ROA). The analysis model used in this research is Multiple Linear Regression, while the analysis technique in this research using F Statistic Test, t Statistic Test, and Determination Coefficient Test. </em></p><em>            The results of this research show that BOPO and NIM have significant influence to Profitability, while NPL and LDR have not significant influence to Profitability. Based on result of regression analysis, it is obtained that Adjusted R<sup>2</sup> is 0,906, meaning the contribution of independent variable in explaining the dependent variable is 90,6% and the rest that is 9,4% is influenced by other variable not examined in this research.</em>

scholarly journals PENGARUH PELATIHAN DAN DISPLIN KERJA TERHADAP KEPUASAN DAN KINERJA PEGAWAI DINAS PERHUBUNGAN KABUPATEN MERAUKE

2018 ◽  
Vol 1 (1) ◽  
pp. 1-13
Author(s):  
Aldisa Arifudin
Keyword(s):  
Human Resources ◽  
Path Analysis ◽  
Significant Influence ◽  
Quantitative Method ◽  
Employee Performance ◽  
Analysis Model ◽  
Independent Variables ◽  
Descriptive Research ◽  
Performance Satisfaction ◽  
Path Analysis Model

Human resources is the most important asset owned by an organization because The success of an organization is determined by humans. This study aims to determine to determine the effect of Training and  Discipline against Job Satisfaction and Employee Performance of the Office of Transportation of Merauke Regency. This research uses quantitative method. This type of research is descriptive research is causal (causal), to determine the effect of a variable on other variables. In this study using path analysis model (path analysis) because among independent variables with dependent variable there is mediation that influence. This analysis is assisted with the help of SPSS 20.0 software. The result of test that have been done show that training has a positive and significant effect on satisfaction, work discipline has a positive and significant influence on satisfaction, training has a positive and significant influence on satisfaction employee performance, work discipline has a positive and significant influence on satisfaction employee performance, satisfaction has no effect on employee performance. Keywords: Training, Work Discipline, Satisfaction, Employee Performance

Analysis of in Accident Influence Factors Based on Degree of Grey Correlational Model

2013 ◽  
Vol 380-384 ◽  
pp. 1278-1281
Author(s):  
Li Min Song ◽  
Yu Zhuo Men ◽  
Yuan Yuan Sun ◽  
Ji Xin Yin ◽  
Xiao Lei Liu
Keyword(s):  
Prediction Model ◽  
Influencing Factors ◽  
Traffic Accidents ◽  
Influence Factors ◽  
Accident Prediction ◽  
Correlation Degree ◽  
Gray Correlation ◽  
Main Factors ◽  
Grey Correlation Degree ◽  
Correlational Model

It is the problem how to search the main factors in various factors on accident. The gray correlation can not only improve the efficiency of the data which have existed, but also remedy the limitation of that carrying out systems analysis by mathematical statistics. From the overall perspective of human-machine-environment, accident prediction model is established and the influencing factors are analyzed of accidents in this paper. The grey correlation degree of the influencing factors is calculated. At last, prediction model of examples is examined. The result shows that the model is applicable and reliable in forecasting the main factors and the relations between them, thus providing reference for traffic administrative department to avoid traffic accidents.

Influencing Factors Analysis of House Prices Based on Multiple Linear Regression

2013 ◽  
Vol 415 ◽  
pp. 722-725 ◽  
Author(s):  
Cheng Wang
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Influencing Factors ◽  
House Prices ◽  
Supply And Demand ◽  
Housing Prices ◽  
Influence Factors ◽  
Analysis Model ◽  
Factors Analysis ◽  
The Stability

Housing problems are related to the vital interests of people and the stability of community. The stability of housing prices is directly related to the level of people's life and the development of the national economy. This paper analyzed the influence factors for market supply and demand of commodity housing, and established price influence factors analysis model based on multiple linear regression. By the empirical analysis, the main influencing factors of housing prices are got, and some policy recommendations of price control are given based on these main influencing factors.

PENGARUH PERSONAL BACKGROUND, POLITICAL BACKGROUND DAN PENGEATAHUAN DEWAN TENTANG ANGGARAN TERHADAP PERAN DPRD DALAM PENGAWASAN KEUANGAN DAERAH

2008 ◽  
Author(s):  
Yesi Mutia Basri ◽  
Rosliana Rosliana
Keyword(s):  
Multivariate Analysis ◽  
Significant Influence ◽  
Political Activity ◽  
Primary Data ◽  
Financial Control ◽  
Independent Variables ◽  
Dependent Variables ◽  
Political Background ◽  
Activity Dependent

This research aim to examine the influence of personal background, political background, and council budget knowledge towards the role of DPRD on region financial control. This research is motivated by the fact that individual background will effect to individual behavior on political activity. Dependent variables in this research are personal background, political background, and council budges knowledge towards the role of DPRD on region financial control Independent variables are the role of DPRD on region financial control in planning, implementing, and responsibility steps. The data in this research consist of primary data that taken from questionnaires distributed directly to respondents. The collected are from 34 Respondents that members of DPRD at Pekanbaru. Hypothesis of this research are examine by using Multivariate Analysis of Variances (MANOVA). The result of this research HI personal background political background and budget knowledge have significant influence toward the role of DPRD on region financial control in planning steps.H2 personal background, politico I background and budget knowledge have no significant influence toward the role of DPRD on region financial control in Implementing steps. H3 personal background political background and budget knowledge have no significant influence toward the role of DPRD on region financial control in Controlling steps.

scholarly journals Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity

2021 ◽  
Vol 11 (11) ◽  
pp. 5072
Author(s):  
Byung-Kook Koo ◽  
Ji-Won Baek ◽  
Kyung-Yong Chung
Keyword(s):  
Traffic Accidents ◽  
Average Rate ◽  
Modern Society ◽  
Rate Of Change ◽  
Weighted Voting ◽  
Independent Variables ◽  
Dependent Variables ◽  
Accident Severity ◽  
Harmonic Average ◽  
Bagging Method

Traffic accidents are emerging as a serious social problem in modern society but if the severity of an accident is quickly grasped, countermeasures can be organized efficiently. To solve this problem, the method proposed in this paper derives the MDG (Mean Decrease Gini) coefficient between variables to assess the severity of traffic accidents. Single models are designed to use coefficient, independent variables to determine and predict accident severity. The generated single models are fused using a weighted-voting-based bagging method ensemble to consider various characteristics and avoid overfitting. The variables used for predicting accidents are classified as dependent or independent and the variables that affect the severity of traffic accidents are predicted using the characteristics of causal relationships. Independent variables are classified as categorical and numerical variables. For this reason, a problem arises when the variation among dependent variables is imbalanced. Therefore, a harmonic average is applied to the weights to maintain the variables’ balance and determine the average rate of change. Through this, it is possible to establish objective criteria for determining the severity of traffic accidents, thereby improving reliability.

scholarly journals Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles

2021 ◽  
Vol 11 (15) ◽  
pp. 7132
Author(s):  
Jianfeng Xi ◽  
Shiqing Wang ◽  
Tongqiang Ding ◽  
Jian Tian ◽  
Hui Shao ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Operating Data ◽  
Detection Model ◽  
Driving Behaviors ◽  
K Nearest Neighbor Algorithm ◽  
Fatigue Driving ◽  
Freight Vehicles

Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue driving studies mostly use vehicle operating data from experiments or simulation data, exposing certain drawbacks in the validity and reliability of the models used. This study collected a large quantity of real driving data to extract sample data under different fatigue degrees. The parameters of vehicle operating data were selected based on significant driver fatigue degrees. The k-nearest neighbor algorithm was used to establish the detection model of fatigue driving behaviors, taking into account influence of the number of training samples and other parameters in the accuracy of fatigue driving behavior detection. With the collected operating data of 50 freight vehicles in the past month, the fatigue driving behavior detection models based on the k-nearest neighbor algorithm and the commonly used BP neural network proposed in this paper were tested, respectively. The analysis results showed that the accuracy of both models are 75.9%, but the fatigue driving detection model based on the k-nearest neighbor algorithm is more reliable.

scholarly journals Development of an early warning system for loose automobile tires

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042094088
Author(s):  
Huibo Wu ◽  
Fei Song ◽  
Kainan Wu ◽  
Cheng Chen ◽  
Xiaohua Wang
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Traffic Accidents ◽  
Warning System ◽  
Working Environment ◽  
Vehicle Body ◽  
Economic Losses ◽  
Control Module ◽  
Alarm Signal ◽  
Monitoring Module

The looseness of tires or even falling off from cars will lead to serious traffic accidents. Once it occurs, it will bring casualties and huge economic losses to society, seriously affecting the traffic safety. To mitigate such possible safety concerns, an early loosening warning system is developed in this article. The system consists of the tire monitoring module and the working control module. The tire monitoring module is installed on the tire and is designed with no power supply. The control module is installed in the vehicle body. Signal transmission between the two modules is achieved through wireless radio frequency. In the driving, once the tire is loosened, the monitoring device will send out the alarm signal automatically and wirelessly. After the driver gets the alarm signal, he can immediately perform the emergency processing, parking, and inspection, which can avoid traffic accidents caused by it. This article introduces the detailed structure, working principle, and operation process of the system. This early warning system has simple structure, high reliability, and is easy to use. It can be used in the common working environment of automobiles. Meanwhile, it is also the foundation of intelligent connected vehicle.

Sign in / Sign up

Export Citation Format

Share Document