Name
Abstract | ||
|---|---|---|
In this paper, a new short-term traffic flow prediction model and method based on incremental support vector regression (ISVR) is proposed, according to the data collected sequentially by the probe vehicle or loop detectors, which can update the prediction function in real time via incremental learning way. As a result, it is fitter for the real engineering application. The ISVR model was tested by using the I-880 database, and the result shows that this model is superior to the back-propagation neural network (BPNN) model. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICNC.2007.661 | ICNC |
Keywords | Field | DocType |
short-term traffic flow prediction,real time,back-propagation neural network,i-880 database,incremental learning,prediction function,loop detector,incremental support,prediction model,isvr model,real engineering application,vector regression,incremental support vector regression,support vector machines,learning artificial intelligence,data collection,support vector regression,traffic flow | Data mining,Traffic flow,Least squares support vector machine,Computer science,Incremental learning,Support vector machine,Road traffic,Artificial intelligence,Artificial neural network,Detector,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-2875-9 | 6 | 0.75 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haowei Su | 1 | 15 | 1.73 |
| Ling Zhang | 2 | 143 | 14.77 |
| Shu Yu | 3 | 24 | 2.56 |