Operational data quality evaluation based on BP neural network and rough set theory

2017 ◽  
Author(s):  
Lijun Liu
Keyword(s):  
Neural Network ◽  
Data Quality ◽  
Set Theory ◽  
Rough Set ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Rough Set Theory ◽  
Operational Data

A Traffic Accident Predictive Model Based on Neural Networks Algorithm and Rough Set Theory

2011 ◽  
Vol 97-98 ◽  
pp. 947-951 ◽  
Author(s):  
Qiao Ru Li ◽  
Liang Chen ◽  
Chang Guang Cheng ◽  
Yue Xiang Pan
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Bp Neural Network ◽  
Traffic Accident ◽  
Rough Set Theory ◽  
Road Traffic ◽  
Black Spot ◽  
Black Spots ◽  
Neural Network Algorithm

The most important and critical step to improve road traffic safety is prediction and identification of traffic accident black spot. A new prediction model of traffic accident black spots is proposed based on GA-BP neural network algorithm and rough set theory. First of all, the traffic accident statistics of Jinwei Road in Tianjin are analyzed. With consideration of static road conditions, the samples of road accident black spots are obtained by the GA-BP neural network algorithm. Furthermore, an effective road traffic accident black spot prediction model is established by utilizing rough set theory with consideration of the impact of real time dynamic conditions. Finally, a numerical example is illustrated. Experimental results show that the proposed model with the combination of these two theories can reduce the hybrid and burdensome amount of data, lower the false alarm rate and improve the forecasting accuracy of accident black spots.

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