Fuzzy Rule-Based System Approach to Combining Traffic Count Forecasts

Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting traffic counts because such information has been identified as vital for the deployment of advanced transportation management and information systems. In this paper, a meta-analysis framework is presented for improving forecasted information of traffic counts, based on an adaptive data processing scheme. In particular, a framework for combining traffic count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an efficient real-time forecasting mechanism. Results from the application of the proposed framework on data acquired from realistic signalized urban network data (of Athens, Greece) and for a diversity of locations exhibit its potential.

[1]  Athanasios Kehagias,et al.  A Bayesian Multiple Models Combination Method for Time Series Prediction , 2001, J. Intell. Robotic Syst..

[2]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[3]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[4]  Chiu Liu,et al.  Kalman filtering estimation of traffic counts for two network links in tandem , 2003 .

[5]  Wei Wang,et al.  Application of Neural Network Ensembles to Incident Detection , 2007, 2007 IEEE International Conference on Integration Technology.

[6]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[7]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[8]  Robert Michael Lewis,et al.  Pattern Search Algorithms for Bound Constrained Minimization , 1999, SIAM J. Optim..

[9]  Matthew G. Karlaftis,et al.  An Information Fusion Framework of Traffic Counts Forecasts Based on Concepts from Fuzzy Set Theory. , 2009, CTS 2009.

[10]  O. SIAMJ.,et al.  ON THE CONVERGENCE OF PATTERN SEARCH ALGORITHMS , 1997 .

[11]  Eleni I. Vlahogianni,et al.  Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume , 2006 .

[12]  Arnold Zellner,et al.  "Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rates" , 2004 .

[13]  Mark J. Kamstra,et al.  Forecast combining with neural networks , 1996 .

[14]  Antony Stathopoulos,et al.  Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow , 2008, Comput. Aided Civ. Infrastructure Eng..

[15]  N. Draper,et al.  Applied Regression Analysis: Draper/Applied Regression Analysis , 1998 .

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

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

[18]  R. Clemen.  Combining forecasts: A review and annotated bibliography , 1989 .

[19]  Tamara G. Kolda,et al.  Stationarity Results for Generating Set Search for Linearly Constrained Optimization , 2006, SIAM J. Optim..

[20]  Markos Papageorgiou,et al.  Real-time estimation of vehicle-count within signalized links , 2008 .

[21]  Antony Stathopoulos,et al.  Temporal and Spatial Variations of Real-Time Traffic Data in Urban Areas , 2001 .

[22]  Gerald J. Hahn,et al.  Applied Regression Analysis (2nd Ed.) , 2012 .

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

[24]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[25]  A. Fiordaliso.  A nonlinear forecasts combination method based on Takagi–Sugeno fuzzy systems , 1998 .

[26]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Richard F. Gunst,et al.  Applied Regression Analysis , 1999, Technometrics.

[28]  Antony Stathopoulos,et al.  Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow , 2008 .

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

[30]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[31]  Zhou Zhi,et al.  Neural Network Ensemble , 2002 .

[32]  Francisco Herrera,et al.  Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , 2002, Advances in Fuzzy Systems - Applications and Theory.