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Predictive Analytics on Real-Time Traffic Management Systems

Edited by Sukanta Das, Santosh Kumar Sahoo, Sandeep Kumar Panda, S. Balamurugan and Sheng-Lung Peng
Series: Industry 5.0 Transformation Applications
Copyright: 2027   |   Expected Pub Date:2026/10/30
ISBN: 9781394315529  |  Hardcover  |  
372 pages
Price: $225 USD
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One Line Description
Master smart transportation with this definitive, research-backed guide that delivers actionable AI, edge computing, and IoT frameworks to engineer safer, greener, and congestion-free traffic management.

Audience
Researchers, academics, entrepreneurs, and engineers looking to innovate intelligent traffic systems based on big data, predictive analytics, computer vision, Internet of Things, and machine learning research.

Description
Traffic management systems are under pressure to evolve and become leaner, greener, and more connected. These systems include a wide range of products, applications, and services that share the common goal of improving transportation safety, efficiency, and sustainability while simultaneously reducing traffic congestion. This book is a comprehensive guide to modern transportation networks evolving through cutting-edge technology. With contributions from leading researchers and practitioners, the book bridges empirical studies with field-tested applications, detailing innovations in computer vision, edge-AI, reinforcement learning, blockchain, swarm intelligence, and IoT-driven automation. Real-world case studies explore key advancements ranging from predictive traffic modeling and emergency vehicle prioritization to metaverse integration in luxury manufacturing logistics. Essential for system architects, developers, researchers, and transportation engineers, this volume equips readers with the practical tools and strategic frameworks needed to build safer, leaner, and more sustainable connected mobility systems.
Readers will find the volume:
• Features contributions from leading authors in the field of intelligent systems around the globe;
• Showcases recent advancements in intelligent systems in advanced manufacturing in terms of analysis, processes, and applications;
• Focuses on the consensus mechanism, optimization, prediction, and recommendation, using various allied technologies.

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Author / Editor Details
Sukanta Das, PhD is an Assistant Professor in the Department of Artificial Intelligence and Data Science in the School of Science and Technology at the ICFAI Foundation for Higher Education, Hyderabad,
Telangana, India, with more than ten years of experience. He has published several research articles in various journals and conferences of repute. His areas of interest include the vehicular cloud, data science, and machine learning.

Santosh Kumar Sahoo, PhD is an Assistant Professor in the Department of Computer Science and Engineering in the School of Science and Technology at the ICFAI Foundation for Higher Education, Hyderabad, Telangana, India, with more than 20 years of experience. He has published several research articles in various journals and conferences. His research areas include computer networks, wireless sensor networks, ad-hoc and vehicular ad-hoc networks, cloud computing, and machine learning.

Sandeep Kumar Panda, PhD is a Professor and the Associate Dean of the School of Science and Technology at the ICFAI Foundation for Higher Education, Hyderabad, Telangana, India. He has published several book chapters, ten edited books, and more than 100 articles in international journals and conferences. His research interests include blockchain technology web 3.0, Metaverse, Internet of Things, AI, and cloud computing.

S. Balamurugan, PhD is the Director of Intelligent Research Consultancy Services, Coimbatore, India. He has published more than 70 books, 300 articles in international and national journals and conferences, and 300 patents. He is a research consultant for many companies, startups, SMEs, and MSMEs.

Sheng-Lung Peng, PhD is a Professor and the Director of the Department of Creative Technologies and Product Design at the National Taipei University of Business, Taiwan. He has published more than 100 articles in international journals and conferences of repute. His research interests include designing and analyzing algorithms for bioinformatics, combinatorics, data mining, and networks.

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Table of Contents
Preface
Acknowledgement
1. Robotics, AI and Human-Centric Automation in Luxury Automotive Manufacturing: Bridging Craftsmanship, Efficiency,
and Sustainability
Alok Ranjan Tripathy, Chandrakanta Sahoo and Alakananda Tripathy
1.1 Introduction
1.2 Robotics in Luxury Automotive OEMs
1.2.1 Mercedes Benz
1.2.2 BMW
1.2.3 Audi
1.2.4 Porsche
1.2.5 Jaguar Land Rover
1.3 Critical Analysis of the Literature
1.3.1 The Evidence Base: Peer-Reviewed Research vs. Industry Narratives
1.3.2 Humanoid Robotics: Promise vs. Reality
1.3.3 Human-Robot Collaboration (HRC): Ergonomics vs. Throughput
1.3.4 Robotic Painting and Surface Finishing: Efficiency vs. Aesthetic Metrics
1.3.5 Inspection and Quality Assurance: Measurement vs. Value
1.3.6 Digital Twins and Connectivity: Potential vs. Practicality
1.3.7 Workforce and Organizational Impacts: Complementarity vs. Disruption
1.3.8 Impact of AI in Luxury Automotive
1.3.9 Sustainability and Ethics: Narrow vs. Holistic Perspectives
1.3.10 Comparative Insights across OEMs
1.4 Conceptual Framework
1.5 Conclusion and Future Direction
References
2. Agent-Driven Deep Learning for Traffic Flow Forecasting:
Multi-Agent Approach
Guda Vanitha and Srujana Inturi
2.1 Introduction
2.1.1 Introduction to the Key Terms
2.2 Literature Study
2.2.1 Study on Spatial-Temporal Attention
2.3 Fundamentals of Traffic Flow Forecasti
and Deep Learning in Traffic Flow Forecasting
2.3.1 Fundamentals of Traffic Flow Forecasting
2.3.2 Deep Learning in Traffic Flow Forecasting
2.4 Multi-Agent Systems (MAS) and Components
2.4.1 Agent-Driven Deep Learning for Traffic Flow Forecasting
2.4.2 Forecasting Model
2.5 Model Architecture
2.5.1 Architecture of STCN
2.5.2 Framework Design
2.5.3 Agent Design
2.6 Implementation
2.6.1 Training and Testing
2.6.2 Agent Coordination
2.7 Experimental Results
2.7.1 Results and Analysis
2.7.2 Evaluating Contributions
2.8 Conclusion
References
3. Intelligent Traffic Flow Monitoring Using Computer Vision – Techniques, Challenges, and Future Perspectives
Pradumn Kumar, Deepak Gupta, Satyasundara Mahapatra, Praveen Kumar Shukla and Animesh Malviya
3.1 Introduction
3.1.1 The Urban Mobility Crisis: Context and Motivation
3.1.2 Computer Vision: A Transformative Technology for Traffic Management
3.1.3 Technical Foundations of Intelligent Traffic Monitoring
3.1.4 Edge Computing and Deployment Architectures
3.1.5 Scope and Organization of This Chapter
3.2 Fundamentals of Vision-Based Traffic Monitoring
3.2.1 Computer Vision Pipeline for Traffic Analysis
3.2.2 Classical Image Processing Techniques
3.2.2.1 Background Subtraction and Foreground Detection
3.2.2.2 Image Enhancement and Preprocessing
3.2.3 Object Tracking Methodologies
3.2.3.1 Tracking-by-Detection Framework
3.2.3.2 SORT: Online and Real-Time Tracking
3.2.3.3 DeepSORT: Deep Appearance-Based Tracking
3.2.3.4 BytesTrack: High-Robustness Association Strategy
3.2.4 Techniques of Analysis Based on Traffic
3.2.4.1 Speed Estimation
3.2.4.2 Vehicle Counting and Classification
3.2.4.3 Congestion Detection and Traffic Flow Characterization
3.2.4.4 Incident and Anomaly Detection
3.2.5 Performance Measures and Review
3.2.5.1 Detection Metrics
3.2.5.2 Tracking Metrics
3.2.5.3 Speed Estimation Accuracy
3.3 Deep Learning Architectures for Traffic Detection and Classification and Advanced Computer Vision Techniques for Traffic Monitoring
3.3.1 Evolution of Deep Learning Object Detection Architectures
3.3.1.1 Convolutional Neural Networks (CNNs)
3.3.1.2 Two-Stage Detectors: The R-CNN Lineage
3.3.1.3 Single-Stage Detectors: Optimizing the Speed–Accuracy Balance
3.3.2 The YOLO Revolution: From YOLOv1 to YOLO12
3.3.3 Transformer-Based Architectures for Traffic Detection
3.3.3.1 Vision Transformers (ViT) Fundamentals
3.3.3.2 Traffic Application ViT Variants
3.3.3.3 Traffic Sign Recognition Transformers
3.3.4 Advanced Computer Vision Techniques: Multi-Modal Sensor Fusion
3.3.4.1 Sensor Fusion Paradigms
3.3.4.2 Camera–LiDAR Fusion Architectures
3.3.4.3 Transformer-Based Fusion
3.3.4.4 Camera–Radar Fusion
3.3.4.5 Multi-Sensor Fusion for Autonomous Vehicles
3.3.5 Attention Mechanisms and Attention Applications
3.3.5.1 Spatial and Channel Attention
3.3.5.2 Cross-Modal Attention
3.3.5.3 Graph Attention for Vision
3.3.6 Model Optimization and Deployment Strategies
3.3.6.1 Model Compression Techniques
3.3.6.2 Architecture Search and Scaling
3.3.6.3 Real-Time Optimization Strategies
3.4 Applications of Vision-Based Traffic Monitoring Systems
3.4.1 Adaptive Traffic Signal Control
3.4.2 Smart Parking Management Systems
3.4.3 Traffic Violence Detection Systems
3.4.4 Highway and Corridor Surveillance
3.4.5 Integration with ITS
3.5 Difficulties with Vision-Based Traffic Surveillance
3.5.1 Environmental and Operational Issues of Challenge
3.5.2 Technical and Computational Problems
3.5.3 Privacy and Ethical Concerns
3.5.4 Problems of Accuracy and Reliability
3.5.5 Integration and Interoperability Issues
3.6 Edge Computing and Deployment Strategies
3.6.1 Edge Computing Basics
3.6.2 Edge Hardware Platforms
3.6.3 Software Structures and Software Optimization
3.6.4 Deployment Considerations
3.6.5 Fleet Management and Operations
3.7 Conclusions and Future Directions
3.7.1 Persisting Limitations
3.7.2 Emerging Research Trajectories
3.7.3 Standardization and Governance
References
4. Leveraging Metaverse and Blockchain for Intelligent Transportation Systems
Sathya A.R.
4.1 Introduction
4.2 Transportation Using Metaverse
4.3 Intelligent Transportation Systems and the Metaverse
4.3.1 Vehicle Fault Detection
4.3.2 Intelligent Safety Systems
4.3.3 New Technology Testing
4.4 Metaverse for ITS vs. Blockchain
4.5 Data Storage: Challenges of IoT in Metaverse
4.6 Privacy: Challenges of AI in Metaverse
4.7 Security: Challenges of Digital Twin in Metaverse
4.8 Data Governance: Challenges of Big Data in Metaverse
4.9 Conclusion
References
5. AI-Powered Urban Mobility: Reinforcement Learning in Traffic Management Systems
B. Indira, Vanitha Guda, Ramesh Ponnala, Keshetti Sreekala and G. Vijaya Laxmi
5.1 Introduction
5.1.1 Related Works
5.2 Reinforcement Learning and Its Components
5.2.1 Models of Reinforcement Learning
5.2.2 Reinforcement Learning for Traffic Management
5.3 Methodology
5.4 Workflow
5.4.1 Implementation and Data Set
5.5 Results and Observations
5.5.1 Advantages and Limitations
5.6 Conclusions
References
6. Detection of Abnormal Traffic Flow for Traffic Management
Ankit Chaudhary, Adarsh Kumar, Karan V. Dayal, Sushil Kumar Saroj and Amrendra Chaudhary
6.1 Introduction
6.1.1 Existing Traffic Management Techniques
6.1.1.1 Traditional Traffic Management Systems
6.1.1.2 Sensor-Based and Actuated Control Systems
6.1.1.3 Camera and Image Processing-Based Systems
6.1.1.4 Intelligent Transportation Systems (ITS)
6.1.1.5 IoT-Based Traffic Management Systems
6.1.1.6 IoT in Traffic Control Systems
6.1.2 System Design and Architecture
6.1.2.1 Overview of the Proposed System
6.1.2.2 Hardware Components
6.1.2.3 Software Components
6.1.2.4 System Block Diagram
6.1.2.5 Data Flow Diagram/Communication Flow
6.1.3 Advantages and Limitations
6.1.3.1 Key Advantages
6.1.3.2 Limitations of the Current Approach
6.1.4 Future Scope
6.1.4.1 AI and ML Integration
6.1.4.2 Vision-Based Detection
6.1.4.3 Smart City Integration
References
7. Blockchain Technology and Its Integration with Cloud
Computing
Atul Gupta, Divakar Singh Yadav and Raghuraj Singh Suryavanshi
7.1 Introduction to Blockchain Technology
7.2 Contributions
7.3 Blockchain Platform
7.3.1 Bitcoin Blockchain
7.3.2 Ethereum Blockchain
7.3.3 Hyperledger Blockchain
7.3.4 Multichain Blockchain
7.4 Blockchain and Its Applications
7.4.1 Data Breaches Cost Reduction
7.4.2 Trans Border Cost Reduction
7.4.3 Eliminate Inefficient Supply Chain and Reduction in Cost
7.4.4 Blockchain Used in Health Care Management
7.4.5 Blockchain Used in Cyber Security
7.4.6 Blockchain Used in Smart Contracts
7.5 Challenges in Blockchain Technology
7.5.1 Energy Consumption and Hardware are Major Challenges
7.5.2 Blockchain Scalability
7.6 Cloud Computing
7.7 Incorporation of Blockchain with Cloud Computing
7.8 Blockchain Use in Cloud Computing
7.9 Blockchain and Cloud Computing Integration: Applications
7.9.1 Healthcare Industry
7.9.2 Smart Home Automation Using Blockchain
7.9.3 Blockchain Used in Automated Transportation
7.9.4 Blockchain Used in Manufacturing Industry
7.10 Conclusion and Future Work
References
8. Ant Colony Optimization for Multi-Phase Traffic Signal Control: A Monograph on Swarm Intelligence Strategies for Stochastic Urban Mobility Networks
Samadrita Samanta, Shubam Chakraborty, Utsov Mohanty, Lipika Mohanty and Minakhi Rout
8.1 Introduction and Problem Statement
8.1.1 The Global Crisis of Urban Mobility
8.1.2 The Economic and Environmental Imperative
8.1.3 The Evolution of Traffic Control Paradigms
8.1.4 Fixed-Time (Pre-Timed) Control
8.1.5 Vehicle-Actuated Control
8.1.6 Adaptive Traffic Control Systems (ATCS)
8.1.7 The Swarm Intelligence Paradigm
8.1.8 Research Objectives and Contributions
8.2 Theoretical Framework of Traffic Engineering
8.2.1 Fundamental Definitions and Terminology
8.2.2 Traffic Flow Theory: Macroscopic Models
8.2.3 The Lighthill-Whitham-Richards (LWR) Model
8.2.4 Shockwave Theory
8.2.5 Saturation Flow and Capacity
8.2.6 Performance Metrics: Delay and Level of Service
8.3 Swarm Intelligence and Optimization Theory
8.3.1 Combinatorial Optimization Problems
8.3.2 Biological Inspiration: The Ant Colony Metaphor
8.3.3 Ant Colony Optimization (ACO) Meta-Heuristic
8.3.4 Elitist Ant System (EAS)
8.3.5 Rank-Based Ant System (RBAS)
8.3.6 Hybrid Approaches
8.3.7 Decentralized and Networked Control
8.3.8 Gaps in Current Research
8.4 System Mod Intersection Topology and NEMA Phasing
8.4.1 State-Space Representation and Flow Conservation
8.4.2 Search Space Representation
8.4.3 Ant System (AS) vs. Rank-Based Ant System (RBAS)
8.4.4 Algorithmic Complexity
8.5 Simulation Framework and Experimental Setup
8.5.1 Simulation Architecture
8.5.2 Experimental Parameters
8.5.3 Traffic Scenarios
8.6 Results and Discussion
8.6.1 Baseline Comparison: ACO vs. Fixed-Time
8.6.2 Algorithmic Convergence: AS vs. RBAS
8.6.3 Sensitivity Analysis: Comparison with Fully Actuated
Control (FAC)
8.7 Conclusion and Future Work
8.7.1 Key Findings
8.7.2 Future Directions
References
9. TAGE-Net: A Residual-Corrected Hybrid Ensemble Framework
for Robust Short‑Term Traffic Forecasting Using TCNTransformer
and Gradient Boosting
Anjishnu Saw, Minakhi Rout, Lipika Mohanty, Ajaya Kumar Parida, Junali Jasmine Jena and Ajay Kumar Jena
9.1 Introduction
9.1.1 Background and Significance of Traffic Forecasting
9.1.2 Evolution of Traffic Forecasting Methodologies
9.1.3 Challenges and Research Gaps in Short-Term Traffic Forecasting
9.1.4 Our Contribution
9.1.5 Organization of the Paper
9.2 Related Work and Background Study
9.2.1 Advanced Sequence Modeling for Traffic
9.2.2 Ensemble and Hybrid Forecasting Strategies Post-2019
9.2.3 Comparative Analysis of Recent Relevant Work
9.3 TAGE-Net Methodology
9.3.1 Data Preprocessing and Feature Engineering
9.3.2 The TCN-Transformer Backbone (Stage 1)
9.3.3 Residual Correction Formulation (Stage 2)
9.4 Experimental Setup
9.5 Results and Discussion
9.5.1 Training Convergence Analysis
9.5.2 Quantitative Performance Evaluation
9.5.3 Ablation Study and Residual Impact
9.5.4 Error Distribution Analysis
9.6 Conclusion and Future Work
9.6.1 Conclusion
9.6.2 Future Work
References
10. Smart City Traffic Monitoring System: A Computer Vision
and Machine Learning Approach
Subhra Swetanisha, Ajay Kumar Jena and Dayal Kumar Behera
10.1 Introduction
10.2 Computer Vision Techniques for Traffic Monitoring
10.2.1 Vehicle Detection and Tracking
10.2.2 Speed Estimation
10.2.3 Lane Detection
10.2.4 Vehicle Classification
10.2.5 Traffic Flow Visualization
10.3 Machine Learning Models in Traffic Flow Analysis
10.3.1 Traffic Flow Prediction
10.3.2 Traffic Pattern Recognition
10.3.3 Anomaly Detection
10.3.4 Real-Time Traffic Monitoring and Control
10.4 Implementation Challenges
10.4.1 Data Quality and Availability
10.4.2 Computational Complexity
10.4.3 Algorithmic Challenges
10.4.4 Integration with Existing Infrastructure
10.5 Case Studies and Practical Applications
10.5.1 Urban Traffic Management Systems
10.5.2 Practical Applications
10.6 System Implementation and Validation
10.6.1 System Architecture
10.7 Conclusion
References
11. Real-Time Abnormal Traffic Flow Detection and Management
Keshetti Sreekala, B. Indira, Ramesh Ponnala, Vanitha Guda, G. Vijaya Laxmi and CRK Reddy
11.1 Introduction
11.2 Related Works
11.3 Proposed Methodology: A Smart System for Abnormal Traffic Flow Detection
11.3.1 System Components and Functionalities
11.3.1.1 Synthetic Data Generation
11.3.1.2 Traffic Analysis Algorithm with Anomaly Detection
11.4 Results and Discussion
11.4.1 Data Format for YOLO
11.4.2 Adapting Sensor Data
11.4.2.1 Sensor Fusion with Images/Video
11.4.3 Feature Extraction for Machine Learning Models
11.4.4 Machine Learning Model Training
11.4.5 Traffic Parameter Estimation for New Data
11.4.6 Anomaly Detection
11.5 Challenges and Considerations
11.6 Conclusion and Future Scope
References
12. Object Distance Measurement Detection with Real-Time
Camera for Surveillance Mechanism
Ritwik Dalmia, Soumili Chandra and Sudhir Kumar Mohapatra
12.1 Introduction
12.2 Objective
12.2.1 General Objective
12.2.2 Specific Objective
12.2.3 Field and Scope of Application
12.3 Methodology
12.3.1 What is the Traditional Classification Approach Used for Object Detection and Distance Measurement?
12.3.2 Physics and Mathematics behind Our Experiment?
12.4 Equations
12.5 Implementation
12.5.1 Feasibility Study
12.5.2 Dataset Preparation
12.5.3 Requirement of Calibration for the Project
12.5.4 Working of the Model
12.5.5 Key Benefits of Our Researched Model
12.6 Results
12.7 Related Work
12.8 Conclusion and Limitations
References
13. Real-Time Traffic Monitoring and Control: IoT Solutions
for Smart Traffic Lights
Anita Mohanty, Ambarish G. Mohapatra, Subrat Kumar Mohanty, Pradyumna Kumar Tripathy, Ashish Khanna and Bal Virdee
13.1 Introduction
13.1.1 Overview of IoT Technologies in Urban Traffic Control
13.1.2 Importance of Real-Time Data Acquisition in Traffic Systems
13.2 Components of Smart Traffic Light Systems
13.2.1 Discussion on Various Sensors and Their Roles
13.2.2 Overview of Communication Protocols Used in IoT Systems
13.3 Data Collection and Analytics
13.3.1 Methods of Data Acquisition from Traffic Sensors
13.3.2 Techniques for Real-Time Data Analysis to Identify Traffic Patterns
13.4 Adaptive Control Algorithms
13.4.1 Mechanisms for Adjusting Traffic Light Timings Dynamically Based on Real-Time Inputs
13.4.2 Benefits of Adaptive Control in Reducing Waiting Times and Congestion
13.5 Instances of Successful IoT Implementations in Traffic Management
13.5.1 Analysis of Performance Metrics Before and After Deployment
13.6 Challenges and Future Directions
13.6.1 Discussion of the Barriers to Widespread Implementation of IoT in Traffic Systems
13.6.2 Exploration of Future Trends in Smart Traffic Management and Potential Technological Advancements
13.7 Conclusion
13.7.1 Summary of the Significance of IoT in Enhancing Traffic Management
13.7.2 Final Thoughts on the Evolving Landscape of Urban Mobility Solutions
References
14. Ensemble Approach for Short-Term Traffic Speed Prediction
Vaibhav Mishra, Sri Karthikeya Sahith Chaganti and Sandeep Kumar Panda
14.1 Introduction
14.1.1 Background and Motivation
14.1.2 Challenges in Traffic Speed Prediction
14.1.3 Traditional vs. Modern Approaches
14.1.4 Need for Ensemble Methods
14.1.5 Scope and Structure of the Chapter
14.2 Literature Review
14.2.1 Classical Models for Traffic Speed Prediction
14.2.2 Machine Learning Approaches
14.2.3 Deep Learning Paradigms
14.2.4 Hybrid and Ensemble Models
14.2.5 Research Gaps and Emerging Directions
14.3 Methodology
14.3.1 Data Acquisition and Preprocessing
14.3.2 Statistical Modeling Layer
14.3.3 Machine Learning and Deep Learning Components
14.3.4 Hybrid-Ensemble Fusion Mechanism
14.3.5 Evaluation Framework and Implementation
14.4 Results and Discussion
14.4.1 Quantitative Performance Evaluation
14.4.2 Qualitative Analysis and Interpretability
14.4.3 Comparative Insights and Limitations
14.5 Conclusion
Bibliography
15. Predicting Traffic Accidents Using the Conflict Coefficient
Priyadarshani Mehta, Vidhan Tiwari, Parnika Gupta, Priyanshu Kumar and Pradumn Kumar
15.1 Introduction
15.2 Fundamentals of Traffic Conflicts and Conflict Coefficients
15.2.1 What is Traffic Conflict
15.2.2 Surrogate Safety Measures (SSMs) for Conflict Quantification
15.2.2.1 Time-to-Collision (TTC)
15.2.2.2 Post-Encroachment Time (PET)
15.2.2.3 Deceleration Rate to Avoid Collision (DRAC)
15.2.3 From Conflicts to the Conflict Coefficient
15.3 Methodology for Using the Conflict Coefficient in Accident Prediction
15.3.1 The Eyes of the Analysis: Data Collection and Trajectory Extraction
15.3.2 The Analysis of Classified Conflicts
15.3.3 Computation of the Conflict Coefficient
15.3.4 Incorporating the Conflict Coefficient within Modern Crash Forecasting Models
15.3.4.1 Machine Learning Models
15.3.4.2 Deep Learning Models
15.4 Applications
15.5 Conclusion
15.6 Future Scope
References
16. An Intelligent Traffic Management System Utilizing the Internet of Things
Dileep Kumar Murala, Santosh Kumar Swain and Santosh Kumar Sahoo
16.1 Introduction
16.1.1 Constraints of the Current System
16.2 Literary Study
16.2.1 Updates on Traffic in Real Time
16.2.2 Wireless Sensors for Vehicle Data Collection
16.3 The Role of IoT in the Management of Traffic in Smart Cities
16.3.1 Advantages of an Intelligent Traffic Management System (ITMS)
16.3.2 Application of IoT in the Domain of Traffic Management
16.3.3 Characteristics of an ITMS
16.4 Proposed System Description
16.4.1 Hardware Design and Description
16.4.2 Software Requirement
16.5 Future Research Directions
Conclusion
References
Index

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