scholarly journals A Simulation Framework for Emergency Response of Highway Traffic Accident

2012 ◽  
Vol 29 ◽  
pp. 1075-1080 ◽  
Author(s):  
Huang Deqi ◽  
Chu Xiumin ◽  
Mao Zhe
Keyword(s):  
Emergency Response ◽  
Traffic Accident ◽  
Simulation Framework ◽  
Highway Traffic

DrillSim: A Simulation Framework for Emergency Response Drills

2006 ◽  
pp. 237-248 ◽  
Author(s):  
Vidhya Balasubramanian ◽  
Daniel Massaguer ◽  
Sharad Mehrotra ◽  
Nalini Venkatasubramanian
Keyword(s):  
Emergency Response ◽  
Simulation Framework

Highway Traffic Accident Prediction Based on SVR Trained by Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 5886-5889 ◽  
Author(s):  
Zhen Qi Yang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Regression Function ◽  
Support Vector ◽  
Highway Traffic ◽  
Prediction Ability ◽  
Accident Prediction ◽  
Better Than

The multi researches and experiments show that the future highway traffic accident situation is shown by the highway traffic accident prediction. In the paper, support vector regression trained by genetic algorithm is presented in highway traffic accident prediction. In the method, genetic algorithm is used to train the parameters of support vector regression. Firstly, the regression function of support vector regression algorithm is introduced, and the parameters of support vector regression are optimized by genetic algorithm. The computation results between G-SVR and SVR can indicate that the prediction ability for highway traffic accidents of G-SVR is better than that of SVR absolutely.

scholarly journals Traffic accident analysis based on C4.5 algorithm in WEKA

2019 ◽  
Vol 272 ◽  
pp. 01035 ◽  
Author(s):  
Jiajia Li ◽  
Jie He ◽  
Ziyang Liu ◽  
Hao Zhang ◽  
Chen Zhang
Keyword(s):  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Safety Management ◽  
Depth Information ◽  
Highway Traffic ◽  
Safety Issues ◽  
C4.5 Algorithm ◽  
Accident Data ◽  
The Impact

At present, China is in a period of steady development of highways. At the same time, traffic safety issues are becoming increasingly serious. Data mining technology is an effective method for analysing traffic accidents. In-depth information mining of traffic accident data is conducive to accident prevention and traffic safety management. Based on the data of Wenli highway traffic accidents from 2006 to 2013, this study selected factors including time factor, linear factor and driver characteristics as research indicators, and established the decision tree using C4.5 algorithm in WEKA to explore the impact of various factors on the accident. According to the degree of contribution of each variable to the classification effect of the model, various modes affecting the type of the accident are obtained and the overall prediction accuracy is about 80%.

Simulation and Analysis of Highway Traffic Accident Based on VISSIM

2012 ◽  
Vol 253-255 ◽  
pp. 1682-1685
Author(s):  
Jun Qing Shi ◽  
Lin Cheng
Keyword(s):  
Queue Length ◽  
Traffic Accident ◽  
Traffic Volume ◽  
Simulation Software ◽  
Highway Traffic ◽  
Traffic Jam ◽  
Remaining Capacity

When the highway traffic accident happened, the capacity will decrease, and if the upstream traffic volume was more than the remaining capacity, the traffic jam will happen. The simulation software VISSIM was used to simulate and analyze the highway traffic accident under different number of blocked lane, upstream traffic volume and traffic accident duration. Simulation result shows that, the capacity has something to do with the number of blocked lane, but has nothing to do with the location of traffic accident. The time of biggest delay has something to do with the number of blocked lane and traffic accident duration, but has nothing to do with the upstream traffic volume. The capacity has nothing to do with traffic accident duration. The longer traffic accident duration, the more delay. And the queue length increases when the number of blocked lane, upstream traffic volume and traffic accident duration increase.

Highway traffic accident prediction using VDS big data analysis

2016 ◽  
Vol 72 (7) ◽  
pp. 2815-2831 ◽  
Author(s):  
Seong-hun Park ◽  
Sung-min Kim ◽  
Young-guk Ha
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Traffic Accident ◽  
Big Data Analysis ◽  
Highway Traffic ◽  
Accident Prediction

scholarly journals Erratum to: Highway traffic accident prediction using VDS big data analysis

2016 ◽  
Vol 72 (7) ◽  
pp. 2832-2832 ◽  
Author(s):  
Seong-hun Park ◽  
Sung-min Kim ◽  
Young-guk Ha
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Traffic Accident ◽  
Big Data Analysis ◽  
Highway Traffic ◽  
Accident Prediction
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