Abstract: States of traffic situations can be
classified into peak and nonpeak periods. The complexity of peak
traffic brings more difficulty to forecasting models. Travel time
index (TTI) is a fundamental measure in transportation. How to
master the characteristics and provide accurate real-time forecasts
is essential to intelligent transportation systems (ITS).
Cooperating with state space approach, least squares support vector
machines (LS-SVMs) are investigated to solve such a practical
problem in this paper. To the best of our knowledge, it is the first
time to apply the technique and analyze the forecast performance in
the domain. For comparison purpose, other two nonparametric
predictors are selected because of their effectiveness proved in
past research. Having good generalization ability and guaranteeing
global minima, LS-SVMs perform better than the others. Providing
sufficient improvement in stability and robustness reveals that the
approach is practically promising.
ZHANG Yang (张 扬), WANG Meng-ling (王梦灵)
. Peak Traffic Forecasting Using Nonparametric Approaches[J]. Journal of Shanghai Jiaotong University(Science), 2012
, 17(1)
: 76
-081
.
DOI: 10.1007/s12204-012-1232-x
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