Short-term traffic speed forecasting hybrid model based on Chaos–Wavelet Analysis-Support Vector Machine theory

2013 ◽  
Vol 27 ◽  
pp. 219-232 ◽  
Author(s):  
Jin Wang ◽  
Qixin Shi
Keyword(s):  
Support Vector Machine ◽  
Wavelet Analysis ◽  
Hybrid Model ◽  
Support Vector ◽  
Short Term ◽  
Model Based ◽  
Machine Theory ◽  
Traffic Speed

scholarly journals A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 27789-27801 ◽  
Author(s):  
Hongxin Xue ◽  
Yanping Bai ◽  
Hongping Hu ◽  
Ting Xu ◽  
Haijian Liang
Keyword(s):  
Support Vector Machine ◽  
Hybrid Model ◽  
Support Vector ◽  
Classification Problems ◽  
Model Based

Traffic Speed Time Series Short Term Forecasting Using Aggregated Model

2014 ◽  
Vol 587-589 ◽  
pp. 2057-2062
Author(s):  
Jian Gu ◽  
Shu Yan Chen
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Short Term ◽  
Support Vector Machine Regression ◽  
Prediction Ability ◽  
Aggregated Model ◽  
Traffic Speed ◽  
Short Term Forecasting

This paper integrated superiority from time series model and least square support vector machine regression model with data aggregation for traffic speed short term forecasting. Based on the results of traffic data variations analysis, the practicability that speed data can be aggregated to several periods was confirmed, and aggregated model can be developed to forecast the speed with auto regression (AR) model and support vector machine regression (SVR). Then the speed data in case study were integrated to 4 periods at the location of Remote Traffic Microwave Sensors (RTMS) 2047 on 2ndRing Road Expressway in Beijing. Arguments with coefficients from AR models then act as the independent variables of LSSVR in aggregated model. Short term traffic speed was predicted by aggregated model, and the results indicated that taking advantages of time periods variation rule inside the aggregated model would help save the model running time cost under the premise of accuracy with better prediction ability than LSSVR in certain conditions.

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