Traffic forecasting using least squares support vector machines

Accurate and timely forecasting of traffic parameters is crucial for effective management of intelligent transportation systems. Travel time index (TTI) is a fundamental measure in transportation. In this article, a non-parametric technique called least squares support vector machines (LS-SVMs) is proposed to forecast TTI. To the best of our knowledge, it is the first time to cooperate the rising computational intelligence technique with state space approach in traffic forecasting. Five other baseline predictors are selected for comparison purposes because of their proved effectiveness. Having good generalisation ability and guaranteeing global minima, LS-SVMs perform better than the others. Experimental results demonstrate that our predictor can significantly reduce mean absolute percentage errors and variance of absolute percentage errors, especially for predicting traffic data with weak regularity. Persuasive comparisons clearly show that it provides a large improvement in stability and robustness, which reveals that it is a promising approach in traffic forecasting and time series analysis.

[1]  D. T. Lee,et al.  Travel-time prediction with support vector regression , 2004, IEEE Transactions on Intelligent Transportation Systems.

[2]  Shu Yu,et al.  Hybrid GA Based Online Support Vector Machine Model for Short-Term Traffic Flow Forecasting , 2007, APPT.

[3]  Ling Zhang,et al.  Short-term Traffic Flow Prediction Based on Incremental Support Vector Regression , 2007, Third International Conference on Natural Computation (ICNC 2007).

[4]  Xiaoyan Zhang,et al.  Short-term travel time prediction , 2003 .

[5]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .

[6]  Eleni I. Vlahogianni,et al.  Short‐term traffic forecasting: Overview of objectives and methods , 2004 .

[7]  Karl Petty,et al.  Travel Time Prediction Algorithm Scalable to Freeway Networks with Many Nodes with Arbitrary Travel Routes , 2005 .

[8]  Erik van Zwet,et al.  A simple and effective method for predicting travel times on freeways , 2004, IEEE Transactions on Intelligent Transportation Systems.

[9]  D. Boyce,et al.  FORECASTING TRAVEL FOR VERY LARGE CITIES: CHALLENGES AND OPPORTUNITIES FOR CHINA , 2007 .

[10]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

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

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

[13]  William H. K. Lam,et al.  ESTIMATION OF AADT FROM SHORT PERIOD COUNTS IN HONG KONG - A COMPARISON BETWEEN NEURAL NETWORK METHOD AND REGRESSION ANALYSIS , 2000 .

[14]  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.

[15]  Xiaoyan Zhang,et al.  Short-Term Travel Time Prediction Using A Time-Varying Coecient Linear Model ? , 2001 .

[16]  Lee D. Han,et al.  AADT prediction using support vector regression with data-dependent parameters , 2009, Expert Syst. Appl..

[17]  W. Lam,et al.  Comparison of Four Modeling Techniques for Short-Term AADT Forecasting in Hong Kong , 2003 .

[18]  Peter J. Bickel,et al.  Day-to-Day Travel-Time Trends and Travel-Time Prediction from Loop-Detector Data , 2000 .

[19]  Stephen D. Clark,et al.  Traffic Prediction Using Multivariate Nonparametric Regression , 2003 .

[20]  Ping-Feng Pai,et al.  Highway traffic forecasting by support vector regression model with tabu search algorithms , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

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

[22]  Rod E. Turochy.  Enhancing Short-Term Traffic Forecasting with Traffic Condition Information , 2006 .

[23]  Timothy C. Coburn,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2004, Technometrics.

[24]  Hojjat Adeli,et al.  Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting , 2005 .

[25]  Deyun Zhang,et al.  Multi-scale Combination Prediction Model with Least Square Support Vector Machine for Network Traffic , 2005, ISNN.

[26]  Bart van Arem,et al.  Recent advances and applications in the field of short-term traffic forecasting. , 1997 .

[27]  Roland Chrobok,et al.  Different methods of traffic forecast based on real data , 2004, Eur. J. Oper. Res..

[28]  William H. K. Lam,et al.  Comparison of two non‐parametric models for daily traffic forecasting in Hong Kong , 2006 .

[29]  Johan A. K. Suykens,et al.  Optimal control by least squares support vector machines , 2001, Neural Networks.

[30]  Soi Hoi Michael Lam,et al.  Short-Term Travel Time Prediction Using Support Vector Regression , 2008 .

[31]  Yunlong Zhang,et al.  Forecasting of Short-Term Freeway Volume with v-Support Vector Machines , 2007 .

[32]  Fei Luo,et al.  Elevator traffic flow prediction with least squares support vector machines , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[33]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[34]  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.

[35]  Xiugang Li,et al.  Predicting motor vehicle crashes using Support Vector Machine models. , 2008, Accident; analysis and prevention.

[36]  Young-Ihn Lee,et al.  Short-term travel speed prediction models in car navigation systems , 2006 .

[37]  William H. K. Lam,et al.  USING AUTOMATIC VEHICLE IDENIFICATION DATA FOR TRAVEL TIME ESTIMATION IN HONG KONG , 2008 .

[38]  Michael J Demetsky,et al.  TRAFFIC FLOW FORECASTING: COMPARISON OF MODELING APPROACHES , 1997 .

[39]  Yf F. Tang,et al.  Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census , 2006 .

[40]  Shing Chung Josh Wong,et al.  Urban traffic flow prediction using a fuzzy-neural approach , 2002 .

[41]  Yunlong Zhang,et al.  Bayesian Approach Based on Geographic Information System to Identify Hazardous Roadway Segments for Traffic Crashes , 2007 .