Urban Traffic Flow Forecasting Using Neural-Statistic Hybrid Modeling

In this paper we show a hybrid modeling approach which combines Artificial Neural Networks and a simple statistical approach in order to provide a one hour forecast of urban traffic flow rates. Experimentation has been carried out on three different classes of real streets and results show that the proposed approach clearly outperforms the best of the methods it combines.

[1]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[2]  Wei-Chiang Hong,et al.  Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm , 2011, Neurocomputing.

[3]  Laurence R. Rilett,et al.  Spectral Basis Neural Networks for Real-Time Travel Time Forecasting , 1999 .

[4]  L. Bucur,et al.  An adaptive fuzzy neural network for traffic prediction , 2010, 18th Mediterranean Conference on Control and Automation, MED'10.

[5]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[6]  Matthew G. Karlaftis,et al.  A multivariate state space approach for urban traffic flow modeling and prediction , 2003 .

[7]  B. G. Cetiner,et al.  A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL , 2010 .

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

[9]  Sherif Ishak,et al.  OPTIMIZATION OF DYNAMIC NEURAL NETWORKS PERFORMANCE FOR SHORT-TERM TRAFFIC PREDICTION , 2003 .

[10]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[11]  Preeti Bajaj,et al.  A Design Approach to Traffic Flow Forecasting with Soft Computing Tools , 2010, 2010 3rd International Conference on Emerging Trends in Engineering and Technology.

[12]  Der-Horng Lee,et al.  Short-term freeway traffic flow prediction : Bayesian combined neural network approach , 2006 .

[13]  T. Pamuła Road traffic parameters prediction in urban traffic management systems using neural networks , 2011 .

[14]  Harris Drucker,et al.  Improving Regressors using Boosting Techniques , 1997, ICML.

[15]  L. Cooper,et al.  When Networks Disagree: Ensemble Methods for Hybrid Neural Networks , 1992 .

[16]  Emilio Corchado,et al.  Soft computing models to identify typical meteorological days , 2011, Log. J. IGPL.

[17]  Erel Avineri Soft Computing Applications in Traffic and Transport Systems: A Review , 2005 .

[18]  Deirdre R. Meldrum,et al.  Freeway traffic data prediction using neural networks , 1995, Pacific Rim TransTech Conference. 1995 Vehicle Navigation and Information Systems Conference Proceedings. 6th International VNIS. A Ride into the Future.

[19]  Anders Krogh,et al.  Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.

[20]  Yang Lu,et al.  Urban Traffic Flow Forecasting Based on Adaptive Hinging Hyperplanes , 2009, AICI.

[21]  Ponnuthurai N. Suganthan,et al.  Multi-objective robust PID controller tuning using two lbests multi-objective particle swarm optimization , 2011, Inf. Sci..

[22]  Emilio Corchado,et al.  A soft computing method for detecting lifetime building thermal insulation failures , 2010, Integr. Comput. Aided Eng..

[23]  Nathan Intrator,et al.  Boosting Regression Estimators , 1999, Neural Computation.

[24]  Amanda J. C. Sharkey,et al.  Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems , 1999 .

[25]  J. Nazuno Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[26]  Xin Yao,et al.  Ensemble learning via negative correlation , 1999, Neural Networks.

[27]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[28]  Ajith Abraham Editorial - Hybrid Soft Computing and Applications , 2009, Int. J. Comput. Intell. Appl..

[29]  S. P. Hoogendoorn,et al.  Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks , 2002 .