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Wavelets in Intelligent Transportation Systems / Edition 1

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This book shows how wavelets can be used to enhance computational intelligence for chaotic and complex pattern recognition problems.  By integrating wavelets with other soft computing techniques such as neurocomputing and fuzzy logic, complicated and noisy pattern recognition problems can be solved effectively. The book focuses on applications in intelligent transportation systems (ITS) where a number of very complicated pattern recognition problems have eluded researchers over the past few decades.

Advancing the frontiers of computational intelligence, this book:

  • Describes ingenious computational models based on novel problem solving and computing techniques such as Case-Based Reasoning, Neurocomputing, and Wavelets, and presents examples to illustrate their importance and use.
  • Presents a multi-paradigm intelligent systems approach to the freeway traffic incident detection and construction work zone management problems.
  • Advocates application and integration of wavelets, neural networks and fuzzy logic for modeling the complex traffic flow behaviors leading to effective and efficient control and management solutions.
  • Presents efficient, reliable, and robust algorithms for automatic detection of incidents on freeways.

Wavelets in Intelligent Transportation Systems is an invaluable resource for computational intelligence researchers and transportation engineers involved in the application of advanced computational techniques for ITS.

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Editorial Reviews

From the Publisher
"It is well-written and clearly structured. The book succeeds in giving a good understandable introduction of the topic…" (Zentralblatt MATH, 1106, 110)
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Product Details

  • ISBN-13: 9780470867426
  • Publisher: Wiley
  • Publication date: 12/2/2005
  • Edition number: 1
  • Pages: 242
  • Product dimensions: 6.26 (w) x 9.29 (h) x 0.73 (d)

Meet the Author

Hojjat Adeli is currently Professor of Civil and Environmental Engineering and Geodetic Science and Lichtenstein Professor in Infrastructure Engineering at The Ohio State University. A contributor to 70 Different scholarly journals, he has authored over 400 research and scientific publications in diverse areas of engineering, computer science, and applied mathematics since he received his PhD from Stanford University in 1976 at the age of 26. He has authored/co-authored nine pioneering books. His recent books are: Machine Learning - Neural Networks, Genetic, Algorithms, and Fuzzy Systems, John Wiley & Sons, 1995; Neurocomputing for Design Automation, CRC Press, 1998; Distributed Computer-Aided Engineering, CRC Press, 1999; High Performance computing in Structural Engineering, CRC Press, 1999; and Control, Optimization and Smart Structures - High-Performance bridges and Buildings of the Future, John Wiley & Sons, 1999. He has also edited twelve books including Intelligent Information Systems, IEEE computer Society, 1997. He is the Editor-in-Chiffon two research journals: Computer-Aided Civil and Infrastructure Engineering, which he founded in 1986, and Integrated Computer-Aided Engineering, which he found in 1993. He has been a Keynote/Plenary Lecturer at 53 International computing conference held in 32 different countries. On September 29, 1998, he was awarded a patent for a "Method and apparatus for efficient design automation and optimization, and structures produced thereby (United States Patent Number 5,815,394) (with a former PhD Student). He is the recipient of numerous academic, research and leadership awards, honors, and recognition. In 1998, he was awarded the University Distinguished Scholar Award by the Ohio State University ' 'in recognition of extraordinary accomplishment in research and scholarship' and the Senate of the General Assembly of State of Ohio passed a resolution honoring him as an ' Outstanding Ohioan'. He is the quadruple winner of the Ohio State University College of Engineering Lumley Outstanding Research Award. He has been an organizer or a member of the organization/Scientific/program committee of 245 conference held in 54 different countries. His research has been Sponsored by 20 different organizations, including government funding agencies such as the National Science Foundation, US Air Force Dynamics Laboratory, and US Army Construction Engineering Research Laboratory, Fede3ral Highway Administration state funding agencies such as the Ohio Department of transportation, Ohio Department of Development, and the State of Ohio Research Challenge Program, professional societies such as the American Iron and  Steel Institute and the American Institute of Steel construction, and corporations such as Cray Research Inc., US Steel, and Bethlehem Steel corporation.

Asim Karim is currently Assistant Professor of Computer Science and Engineering at Lahore University of manageme4nt Sciences (LUMS), Pakistan, He conducts research in diverse areas of computer science and engineering including applied artificial intelligence, intelligent data analysis and data mining, intelligent transportation systems, and high-performance computing His work has been published in 18 research articles in international journals and conferences. He is also the co-author of the book Construction Scheduling, Cost Optimization, and Management - A New Model Based on Neurocomputing and Object Technologies published by Spon Press in 2001. He received his BSc (with honors) from the University of Engineering and Technology, Lahore, Pakistan, in 1994 and his PhD from the Ohio State University in 2002.

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Table of Contents



About the Authors.

1. Introduction.

2. Introduction to Wavelet Analysis.

2.1 Introduction.

2.2 Basic Concept of Wavelets and Wavelet Analysis.

2.2.1 What is a Wavelet?.

2.2.2 Wavelet Analysis.

2.2.3 Types of Wavelets and Wavelet Transforms.

2.3 Mathematical Foundations.

2.3.1 Sets and Spaces.

2.3.2 Sequence and Function Spaces.

2.3.3 Independent and Basis Sets.

2.3.4 Metric, Normed and Inner Product Spaces.

2.3.5 The L2(R) and L2(Z) Spaces.

2.3.6 Orthogonality.

2.4 The Discrete Wavelet Transform (DWT).

2.5 Multi-resolution Analysis.

2.6 Wavelet Bases.

2.6.1 Constructing Wavelet Bases.

2.6.2 Example Wavelet Systems.

2.7 Computing the DWT.

2.7.1 Pyramid Algorithm.

2.7.2 Practical Considerations.

3. Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant Analysis.

3.1 Introduction.

3.2 Incident Detection Algorithms.

3.3 Discrete Wavelet Transform (DWT) of Traffic Signals.

3.4 Linear Discriminant Analysis (LDA).

3.5 Data Acquisition.

3.6 Results.

4. Adaptive Conjugate Neural Network-Wavelet Model for Traffic Incident Detection.

4.1 Introduction.

4.2 Improving Traffic Incident Detection.

4.3 Adaptive Conjugate Gradient Neural Network Model.

4.4 Incident Detection Results Using Various Approaches.

4.4.1 LDA.

4.4.2 DWT and LDA.

4.4.3 ACGNN.

4.4.4 DWT, LDA, and ACGNN.

4.5 Effect of Data Filtering Using DWT.

4.6 Relative Contribution of DWT and LDA for Feature Extraction.

4.7 Effects of Freeway Geometry on Incident Detection.

4.7.1 Effect of Curvature.

4.7.2 Effect of Number of Lanes.

4.8 Conclusion.

5. Enhancing Fuzzy Neural Network Algorithms Using Neural Networks.

5.1 Introduction.

5.2 Discrete Wavelet Transform.

5.3 Architecture.

5.4 Training of the Network.

5.5 Filtering of the Traffic Data Using DWT.

5.6 Incident Detection Results.

6. Fuzzy-Wavelet Radial Basis Function Neural Network Model for Freeway Incident Detection.

6.1 Introduction.

6.2 A New Traffic Incident Detection Methodology.

6.3 Selection of Type and Number of Traffic Data.

6.4 Wavelet-Based De-noising.

6.5 Fuzzy Data Clustering.

6.6 Radial Basis Function Neural Network Classifier.

6.7 Fuzzy-Wavelet RBFNN Model for Incident Detection.

6.8 Example.

6.8.1 Training of Algorithm.

6.8.2 Testing of Algorithm Using Simulated Data.

6.8.3 Testing of Algorithm Using Real Data.

6.9 Conclusion.

7. Comparison of Fuzzy-Wavelet RBFNN Freeway Incident Detection Model with California Algorithm.

7.1 Introduction.

7.2 California Algorithm #8.

7.3 Evaluation of the Model.

7.3.1 Introduction.

7.3.2 Evaluation Criteria.

7.3.3 Traffic Data.

7.3.4 Training and Calibration.

7.3.5 First Simulation Test - Parametric Evaluation.

7.3.6 Second Simulation Test - Freeway with On- and Off-Ramps.

7.3.7 Test Using Real Data.

7.4 Concluding Remarks.

8. Incident Detection Algorithm Using Wavelet Energy Representation of Traffic Patterns.

8.1 Introduction.

8.2 Freeway Incident Detection and Patterns in Traffic Flow.

8.2.1 Single-Station Versus Two-Station Incident Detection Approaches.

8.2.2 Upstream and Downstream Flow Patterns.

8.3 Discrete Wavelet Transform and Signal Energy.

8.4 Traffic Pattern Feature Enhancement and De-noising.

8.5 Pattern Classification Using Radial Basis Function Neural Networks.

8.6 Wavelet Energy Freeway Incident Detection Algorithm.

8.7 Model Testing.

8.7.1 Introduction.

8.7.2 Training.

8.7.3 First Test Using Simulated Data: Two-lane Freeway.

8.7.4 Second Test Using Simulated Data: Three-lane Freeway.

8.7.5 Third Test Using Simulated Data: Compression Waves.

8.7.6 Fourth Test Using Real Data: FSP Project's I-880 Database.

8.7.7 Result Summary and Comparison.

8.8 Conclusion.

9. Parametric Evaluation of the Wavelet Energy Freeway Incident Detection Algorithm.

9.1 Introduction.

9.2 Factors to Consider in Rural Freeway Incident Detection.

9.3 Evaluation and Parametric Investigation.

9.3.1 Goals.

9.3.2 Data.

9.3.3 Training and Calibration.

9.4 Parametric Evaluation Using Simulated Data on Typical Urban Freeways.

9.5 False Alarm Performance in the Vicinity of On- and Off-Ramps.

9.6 Evaluation on Rural Freeways.

9.7 Evaluation Using Real Data.

9.8 Performance Summary and Conclusion.

10. Case-Based Reasoning Model for Work Zone Traffic Management.

10.1 Introduction.

10.2 Work Zones and Traffic Management.

10.3 Case-Based Reasoning.

10.4 Objectives.

10.5 Scope and Categorization of Parameters.

10.5.1 Work Zone Type.

10.5.2 Work Zone Layout.

10.5.3 Work Characteristics.

10.5.4 Traffic flow characteristics.

10.5.5 Phases of Work.

10.5.6 Traffic Control Measures.

10.5.7 Road User Cost.

10.6 A Four-Set Case Model for the Work Zone Traffic Management Domain.

10.7 Hierarchical Object-Oriented Case Model.

10.8 Case Representation.

10.9 Similarity Measures.

10.10 Case Retrieval.

10.11 Creation of the Case Base.

10.12 Creation of Work Zone Traffic Control Plans Using the CBR System.

10.13 Illustrative Examples.

10.13.1 Example 1.

10.13.2 Example 2.

10.13.3 Example 3.

10.14 Conclusion.

11. Mesoscopic-Wavelet Freeway Work Zone Flow and Congestion Model.

11.1 Introduction.

11.2 Macroscopic Models.

11.3 Microscopic Models.

11.4 A Mesoscopic Flow Model for a Freeway Work Zone.

11.5 Traffic Feature Enhancement Using Discrete Wavelet Transform.

11.6 Concluding Remarks.



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