Study on Macroscopic Prediction Model of Traffic Accident Influence Factors

2012 ◽  
Vol 590 ◽  
pp. 531-535
Author(s):  
Yu Zhuo Men ◽  
Hai Bo Yu ◽  
Xin Pan
Keyword(s):  
Prediction Model ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Influence Factors ◽  
Prediction Method ◽  
Theory Model ◽  
Urban Traffic ◽  
Grey Theory ◽  
Correlation System ◽  
Correlation Grade

In order to study the main influence factors on urban traffic accident, the grey correlation system macroscopic prediction method was presented. Concerning the overall environmental perspective of people, automobile, and road as well as the distinctive characteristic of urban traffic accident, the prediction model of factors contributing to traffic accident was proposed. EXCEL software was applied to analyze the relations between the influence factors and traffic accidents with grey theory model adopted to calculate the correlation grade among different factors. The prediction Model was also validated through examples on the basis of the investigation of traffic accident and the relevant statistics. The results show that the model is applicable and efficient in forecasting the main factors and the relations between them, thus to avoid traffic accidents.

Evaluation and Optimization on Colors in Urban Traffic Based on PSO and GT-BPANN

2011 ◽  
Vol 97-98 ◽  
pp. 1162-1167
Author(s):  
Hong Wei Yuan ◽  
Wen Bo Zhang
Keyword(s):  
Quantitative Research ◽  
Traffic Accidents ◽  
Evaluation Model ◽  
Reliability And Validity ◽  
Urban Traffic ◽  
Evaluation Index System ◽  
Grey Theory ◽  
Urban Road ◽  
Modern Knowledge ◽  
Color Psychology

In order to reduce traffic accidents, achieving safety and harmony of traffic color, a quantitative research on traffic color of urban road were carried. Grounded on modern knowledge of color theory, color psychology, Grey Theory and Back-error Propagation Artificial Neural Network (GT-BPNN), Particle Swarm Optimization algorithm (PSO) and traffic questionnaires, the evaluation index system of traffic color in urban road, the evaluation model of transportation color and the model of color harmony and optimization in urban road were constructed. Assisted by MATLAB and other software, the reliability and validity of models were determined, taking a road in Xuzhou, Jiangsu as a test section. According to the results, some reasonable improvements on traffic safe color were recommended.

scholarly journals Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Li Wang ◽  
Shimin Lin ◽  
Jingfeng Yang ◽  
Nanfeng Zhang ◽  
Ji Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Traffic Congestion ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Rbf Neural Network ◽  
Calibration Method ◽  
Theory Model ◽  
Point Of View ◽  
Urban Traffic

Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole.

Prediction Method of Motor Vehicle Traffic Accident Based on Support Vector Machine

2014 ◽  
Vol 631-632 ◽  
pp. 284-287
Author(s):  
Bo Yang ◽  
Li Na Zhang
Keyword(s):  
Social Influence ◽  
Index System ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Motor Vehicle ◽  
Rapid Development ◽  
Prediction Method ◽  
Living Standard ◽  
Support Vector ◽  
Vehicle Traffic

With the rapid development of economy and the improvement of people's living standard, there are more and more vehicles in China, with the increase of traffic accidents. In this paper, by analyzing the factors of social influence on motor vehicle traffic accident, we establish the index system, that is corresponding relationship of motor vehicle traffic accident and factors of social influence, According to this index system, design of motor vehicle traffic accident prediction method based on SVM. Based on the statistical data of social factors and motor vehicle traffic accident in 1985-2012 in china, to train the SVM model, at the same time, the kernel function and parameters of SVM used were setting and compared. The experimental results show that, the accuracy of the use of the RBF function is 97.2%, predicted by using time 95ms, with higher accuracy and faster computing speed.

Grey Predicting Model: Rice Output of Baise City

2014 ◽  
Vol 556-562 ◽  
pp. 3442-3445
Author(s):  
Da Ming Xu ◽  
De Wang Li ◽  
Wu Sheng Wang
Keyword(s):  
Prediction Model ◽  
Theory Model ◽  
Rice Production ◽  
Grey Theory ◽  
Output Data ◽  
Predicting Model ◽  
Forecast Models ◽  
Statistical Yearbook

By using rice output data of Baise statistical yearbook from 1989 to 2010, based on the Grey theory and Grey forecast models, which are GM(1,1), have been adopted to predict the rice output of Baise city in this paper. So we get the GM(1,1) prediction model and predict the rice production of Baise city from 2011 to 2020. The results show that the Grey theory model in predicting rice output is feasibility and reasonable.

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.

Identification of Urban Traffic Accident Hotspot Zones Using GIS: A Case Study of Dhaka Metropolitan Area

2020 ◽  
Vol 3 (1) ◽  
pp. 36-42
Author(s):  
Arif Ahmad ◽  
Khandaker Hossain ◽  
Mallik Hossain
Keyword(s):  
Spatial Patterns ◽  
Metropolitan Area ◽  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Safety Management ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Information ◽  
Gis Technology

The issue of traffic safety becomes increasingly prominent and has attracted widespread attention from researchers and policy makers. Dhaka, the capital of Bangladesh, is the most vulnerable city both in terms of total number of accidents and accident rates. GIS technology has been widely applied to urban traffic information and safety management. This paper presents a geospatial analysis to identify the road traffic accident (RTA) hotspot zones in Dhaka Metropolitan Area (DMA). ‘Spatial analysis’ and ‘spatial statistics tools’ are used to examine spatial patterns of accident data. A systematic comparison of identified hotspot zones using Local Moran’s-I Statistic, Getis-Ord Gi* statistic and Kernel Density Estimation (KDE) carried out to examine spatial patterns of high cluster of traffic accidents. These analyses revealed a total 22 hotspot zones in DMA during the years 2010-2012. This kind of research would help generating new parameters for reducing road traffic accidents in Dhaka Metropolitan Area.

scholarly journals Traffic Accident Data Generation Based on Improved Generative Adversarial Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5767
Author(s):  
Zhijun Chen ◽  
Jingming Zhang ◽  
Yishi Zhang ◽  
Zihao Huang
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Original Data ◽  
Urban Traffic ◽  
Generative Adversarial Networks ◽  
Generation Model ◽  
Data Generation ◽  
Road Traffic Safety ◽  
Adversarial Networks ◽  
Accident Data

For urban traffic, traffic accidents are the most direct and serious risk to people’s lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful effects. However, research scholars are often confronted with the problem of scarce and difficult-to-collect accident data resources for traffic accident scenarios. Therefore, in this paper, a traffic data generation model based on Generative Adversarial Networks (GAN) is developed. To make GAN applicable to non-graphical data, we improve the generator network structure of the model and used the generated model to resample the original data to obtain new traffic accident data. By constructing an adversarial neural network model, we generate a large number of data samples that are similar to the original traffic accident data. Results of the statistical test indicate that the generated samples are not significantly different from the original data. Furthermore, the experiments of traffic accident recognition with several representative classifiers demonstrate that the augmented data can effectively enhance the performance of accident recognition, with a maximum increase in accuracy of 3.05% and a maximum decrease in the false positive rate of 2.95%. Experimental results verify that the proposed method can provide reliable mass data support for the recognition of traffic accidents and road traffic safety.

ADHD is a Risk Factor of Road Crashes: A Purposive Study of Finding Relation between ADHD and Road Traffic Accident in Bangladesh

2018 ◽  
Vol 9 (08) ◽  
pp. 20531-20536
Author(s):  
Nusrat Shamima Nur ◽  
M. S. l. Mullick ◽  
Ahmed Hossain
Keyword(s):  
Risk Factor ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Fatality Rate ◽  
Road Traffic Accidents ◽  
Road Crashes ◽  
Road Crash ◽  
Day By Day

Background: In Bangladesh fatality rate due to road traffic accidents is rising sharply day by day. At least 2297 people were killed and 5480 were injured in road traffic accidents within 1st six months of 2017.Whereas in the previous year at 2016 at least 1941 people were killed and 4794 were injured within the 1st six months. No survey has been reported in Bangladesh yet correlating ADHD as a reason of impulsive driving which ends up in a road crash.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

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