Unsupervised learning based performance analysis of n-support vector regression for speed prediction of a large road network

Many intelligent transportation systems (ITS) applications require accurate prediction of traffic parameters. Previous studies have shown that data driven machine learning methods like support vector regression (SVR) can effectively and accurately perform this task. However, these studies focus on highways, or a few road segments. We propose a robust and scalable method using v-SVR to tackle the problem of speed prediction of a large heterogeneous road network. The traditional performance measures such as mean absolute percentage error (MAPE) and root mean square error (RMSE) provide little insight into spatial and temporal characteristics of prediction methods for a large network. This inadequacy can be a serious hurdle in effective implementation of prediction models for route guidance, congestion avoidance, dynamic traffic assignment and other ITS applications. We propose unsupervised learning techniques by employing k-means clustering, principal component analysis (PCA), and self organizing maps (SOM) to overcome this insufficiency. We establish the effectiveness of the developed methods by evaluation of spatial and temporal characteristics of prediction performance of the proposed variable window v-SVR method.

[1]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[2]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[3]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[4]  Stephen Graham Ritchie,et al.  TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES , 1993 .

[5]  Mark Dougherty,et al.  SHORT TERM INTER-URBAN TRAFFIC FORECASTS USING NEURAL NETWORKS , 1997 .

[6]  Brian L. Smith,et al.  Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[7]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[8]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[9]  F. Girosi,et al.  Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[10]  Gunnar Rätsch,et al.  Predicting Time Series with Support Vector Machines , 1997, ICANN.

[11]  Billy M. Williams,et al.  Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models , 1998 .

[12]  Bernhard Schölkopf,et al.  Shrinking the Tube: A New Support Vector Regression Algorithm , 1998, NIPS.

[13]  Martin L. Kersten,et al.  Database Architecture Optimized for the New Bottleneck: Memory Access , 1999, VLDB.

[14]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[15]  Dongjoo Park,et al.  Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network , 1999 .

[16]  Hugh Garraway Parallel Computer Architecture: A Hardware/Software Approach , 1999, IEEE Concurrency.

[17]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2001, Springer Series in Statistics.

[18]  John McFadden,et al.  Application of Artificial Neural Networks to Predict Speeds on Two-Lane Rural Highways , 2001 .

[19]  Samy Bengio,et al.  SVMTorch: Support Vector Machines for Large-Scale Regression Problems , 2001, J. Mach. Learn. Res..

[20]  Billy M. Williams,et al.  Comparison of parametric and nonparametric models for traffic flow forecasting , 2002 .

[21]  XiangMo Zhao,et al.  Traffic flow time series prediction based on statistics learning theory , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[22]  Chih-Jen Lin,et al.  Training v-Support Vector Regression: Theory and Algorithms , 2002, Neural Computation.

[23]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[24]  Jan-Ming Ho,et al.  Travel time prediction with support vector regression , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[25]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[26]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[27]  Georg Pölzlbauer Survey and Comparison of Quality Measures for Self-Organizing Maps , 2004 .

[28]  L. Vanajakshi,et al.  A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[29]  Michalis Vazirgiannis,et al.  c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. On Clustering Validation Techniques , 2022 .

[30]  Eleni I. Vlahogianni,et al.  Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach , 2005 .

[31]  Michel Pasquier,et al.  POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction , 2006, IEEE Transactions on Intelligent Transportation Systems.

[32]  Yi Zhang,et al.  Simultaneously Prediction of Network Traffic Flow Based on PCA-SVR , 2007, ISNN.

[33]  L. Vanajakshi,et al.  Support Vector Machine Technique for the Short Term Prediction of Travel Time , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[34]  Yinglong Wang,et al.  Short-term traffic flow forecasting based on clustering and feature selection , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[35]  Gaetano Valenti,et al.  Traffic Estimation And Prediction Based On Real Time Floating Car Data , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[36]  Dario Rossi,et al.  Support vector regression for link load prediction , 2008 .

[37]  Lee D. Han,et al.  Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions , 2009, Expert Syst. Appl..

[38]  Yang Zhang,et al.  Traffic forecasting using least squares support vector machines , 2009 .

[39]  H. J. Van Zuylen,et al.  Bayesian committee of neural networks to predict travel times with confidence intervals , 2009 .

[40]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[41]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[42]  Wanli Min,et al.  Real-time road traffic prediction with spatio-temporal correlations , 2011 .

[43]  Yi Lu Murphey,et al.  Real time vehicle speed prediction using a Neural Network Traffic Model , 2011, The 2011 International Joint Conference on Neural Networks.

[44]  Zhu Yue Short-term Traffic Flow Prediction Based on SVM , 2012 .