Variable Weighted Combination Forecasting Model Based on Genetic Algorithm and Artificial Neural Network

2007 ◽  
Author(s):  
Junfeng Li ◽  
Wenzhan Dai ◽  
Haipeng Pan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Forecasting Model ◽  
Model Based ◽  
Combination Forecasting ◽  
Combination Forecasting Model ◽  
Artificial Neural ◽  
Weighted Combination

Application of a Traffic Flow Prediction Model Based on Neural Network in Intelligent Vehicle Management

2019 ◽  
Vol 33 (03) ◽  
pp. 1959009 ◽  
Author(s):  
Yang Guo ◽  
Lu Lu
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Forecasting Model ◽  
Intelligent Vehicle ◽  
Traffic Flow Prediction ◽  
Model Based ◽  
Flow Prediction ◽  
Combination Forecasting ◽  
Combination Forecasting Model ◽  
Vehicle Management

The ultimate direction of intelligent vehicle management is to achieve artificial intelligence (AI), and data mining is an important supporting technology for AI. The adoption of new AI technology can effectively improve operational efficiency and safety, especially in terms of performance. This paper takes the researches on traffic jam as an example and proposes one algorithm for combination forecasting model based on a segmentation algorithm for traffic flow sequence and BP neural network prediction. In this paper, it also introduces the traffic flow clustering analysis and mining algorithms for congestion events at the intersections. The blocking point algorithm is improved, and experimental analysis is performed through samples. Experimental results show that the algorithm use for combination forecasting model can greatly improve the real-time performance of short-term traffic flow prediction and significantly reduce the prediction error rate. Therefore, this algorithm has practical and innovative significance in the field of intelligent vehicle management.

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