Overview

Exploration of how cutting-edge digital technologies can power humanity’s collective efforts towards urban transport environmental sustainability Artificial Intelligence (AI) for Smart and Sustainable Urban Transportation delves into the nexus between urbanization, transportation, and climate change, providing a comprehensive analysis of how traditional transportation systems contribute to greenhouse gas emissions and air pollution, thereby undermining our collective efforts towards environmental sustainability. Through lucid explanations and real-world examples, the book explores how cutting-edge digital technologies, including Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain, can revolutionize urban transportation. Readers will gain insights into the role of AI-powered platforms and management software solutions that optimize energy usage, enhance efficiency, and promote sustainable mobility. With contributions for a variety of experts in the fields of transportation, environmental science, and artificial intelligence, this book delivers perspectives on topics including: Intelligent traffic management and the impact of computer vision in smart transportation Connected lighting and AI-empowered battery management systems Edge computing and edge AI for real-time insights Route optimization and navigation, smart parking solutions, and fleet and fuel optimization Implementation of smart microgrids and renewable energy sources such as solar and wind Whether you are a policymaker, urban planner, transportation professional, or simply a concerned citizen, Artificial Intelligence (AI) for Smart and Sustainable Urban Transportation serves as a vital resource for understanding the challenges and opportunities in transitioning towards a more sustainable future in the field of urban transportation.

Full Product Details

Author:   Sathyan Munirathinam (ASML) ,  Pethuru Raj Chelliah (Reliance Jio Platforms Ltd., Bangalore, India) ,  Peter Augustine (CHRIST (Christ College and Christ University), Deemed to be University, India) ,  Beaulah Soundarabai (CHRIST (Christ College and Christ University), Deemed to be University, India)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-IEEE Press
ISBN:  

9781394351060

ISBN 10:   1394351062
Pages:   464
Publication Date:   02 June 2026
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Awaiting stock  
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Table of Contents

Contents List of Contributors xxi About the Editors xxvii 1 Recent Trends in Intelligent Transportation Systems 1 Kathi Durgesh, Siddharth Garia, and Vishal Kumar Narnoli 1.1 Introduction 1 1.2 Methodology 3 1.3 Results 9 1.4 Discussion 10 References 10 Further Reading 12 2 Artificial Intelligence and IoT Applications Transforming the Automotive Industry 15 Raviprakash R Salagame 2.1 Introduction 15 2.1.1 Overview of Key Digital Technologies and Applications 16 2.1.2 IoT (Internet of Things) and Sensors 17 2.1.3 Artificial Intelligence (AI) and Machine Learning 18 2.1.3.1 Machine Learning 19 2.1.3.2 AI and ML Applications in Automotive 20 2.1.4 Cloud Computing 22 2.1.5 Cybersecurity 22 2.2 Automotive AI Applications and Urban Use Cases 23 2.2.1 Autonomous Vehicles 23 2.2.1.1 How Autonomous Vehicles (AV) Work 25 2.2.1.2 How AI Is Used in Autonomous Systems 26 2.2.1.3 Perception and Localization 26 2.2.1.4 Path Planning 27 2.2.1.5 Synthetic Data Generation 27 2.2.1.6 Verification and Validation of AV Systems 28 2.2.1.7 Safe Deployment of AV Systems 28 2.2.1.8 Autonomous Urban Use Cases 28 2.3 Connected Vehicles 30 2.3.1 How AI Is Used in Connected Vehicles 31 2.3.1.1 Personalized User experience 31 2.3.1.2 Navigation Guidance 31 2.3.1.3 Vehicle Status Monitoring 31 2.3.1.4 Connected Vehicle Urban Use Cases 32 2.4 Electric Vehicles (EV) 32 2.4.1 AI-based Urban Use Cases of EVs 33 2.4.1.1 Remote Monitoring of EV Fleets 33 2.4.1.2 EV Charging Infrastructure Management 34 2.4.1.3 Enhancing User Experience 34 2.5 Shared Mobility 35 2.6 Future Trends Toward Smart Transportation 35 2.7 Conclusion 36 References 37 3 Artificial Intelligence in Transportation Using Automated Grading, Adaptive Learning, and Predictive Maintenance to Increase Efficiency 41 S. Cyciliya Pearline Christy, K. Merriliance, and Mary Immaculate Sheela Lourdusamy 3.1 Introduction 41 3.1.1 Background of AI in Transportation 41 3.1.2 Purpose and Scope of the Chapter 42 3.1.3 Importance of Efficiency in Modern Transportation Systems 42 3.2 Overview of Artificial Intelligence in Transportation 42 3.2.1 What is AI and How It Applies to Transportation 42 3.2.2 Current Trends in AI Adoption 43 3.2.3 Challenges in Implementing AI Technologies 43 3.3 Automated Grading Systems in Transportation 44 3.3.1 Definition and Role in Infrastructure Evaluation 44 3.3.2 AI Techniques Used (e.g., Computer Vision, ML) 44 3.3.3 Real-world Applications (e.g., Road Surface Assessment, Bridge Safety) 45 3.3.4 Benefits and Limitations 45 3.4 Adaptive Learning in Traffic Management and Logistics 46 3.4.1 What Is Adaptive Learning? 46 3.4.2 Traffic Signal Optimization and Route Planning 46 3.4.3 AI-driven Logistics and Fleet Management 47 3.4.4 Case Studies and Pilot Projects 47 3.5 Predictive Maintenance in Transportation Systems 48 3.5.1 Introduction to Predictive Maintenance 48 3.5.2 Sensors and Data Collection Techniques 48 3.5.3 Machine Learning Models for Failure Prediction 49 3.5.4 Impact on Cost, Downtime, and Safety 49 3.6 Ethical, Regulatory, and Security Considerations 50 3.6.1 Data Privacy in AI Systems 50 3.6.2 AI Bias and Fairness 50 3.6.3 Regulatory Frameworks and Standards 51 3.6.4 Cybersecurity in AI-driven Infrastructure 51 3.7 Future Outlook and Emerging Technologies 51 3.7.1 Role of Generative AI and Digital Twins 52 3.7.2 Integration with Smart Cities 52 3.7.3 Autonomous Vehicles and AI Coordination 52 3.8 Conclusion 53 3.8.1 Summary of Key Points 53 3.8.2 Implications for Policymakers and Practitioners 53 3.8.3 Final Thoughts on the Future of AI in Transportation 54 References 54 4 Autonomous Vehicles and Smart Mobility 57 P. Sudheer, S. Ashmad, M. Saravanan, and A. Immanuel 4.1 Introduction 57 4.2 Challenges in Smart Mobility and Autonomous Vehicles 62 4.3 Case Studies and Practical Applications of Smart Mobility and Self-driving Cars 64 4.4 Policies, Ethics, and Governance in the Autonomous Vehicle Ecosystem 66 References 69 5 Artificial Intelligence (AI) for Smart and Sustainable Urban Transportation 73 Ishika Gupta, Hriday Gupta, Siddharth Gupta, and Prerna Ajmani 5.1 Introduction 73 5.2 Background 74 5.2.1 Introduction to 6G, Smart Cities, and EVs 74 5.2.2 Evolution from 5G to 6G: Key Features and Capabilities 74 5.2.3 Concept of Sustainable Smart Cities, EVs, and Their Importance 75 5.2.3.1 Sustainable Smart Cities 75 5.2.3.2 Electric Vehicles 76 5.2.4 Role of AI and IoT in Building Smart and Sustainable Urban Environments 77 5.2.5 Role of AI and IoT in Building Electric Vehicles 77 5.2.5.1 AI in Electric Vehicles 77 5.2.5.2 IoT in Electric Vehicles 78 5.2.5.3 Synergy of AI, IoT, and 6G 78 5.3 Enabling Technologies 78 5.4 Components 84 5.4.1 Smart City 84 5.4.2 Electric Vehicles 89 5.5 AI-driven Sustainable Solutions 92 5.6 Security and Privacy in AI and IoT for Smart Cities and Electric Vehicles 95 5.6.1 Smart Cities 95 5.6.2 Security and Privacy in AI and IoT for Electric Vehicles 98 5.6.3 Key Security Challenges in Connected Electric Vehicles 98 5.6.3.1 Role of AI in Security for Electric Vehicles 99 5.6.3.2 Role of Blockchain and DLT for Privacy and Trust 99 5.6.3.3 6G and the Future of EV Cybersecurity 99 5.7 Case Studies and Real-world Implementations 100 5.7.1 Smart Cities 100 5.7.2 Electric Vehicles 103 5.8 Challenges for 6G 105 5.9 Future Directions 106 5.10 Conclusion 110 References 111 6 Smart Mobility: Integrating AI for Sustainable Urban Transportation Solutions 113 A. Jothi Kumar 6.1 Introduction 113 6.2 AI in Traffic Management Systems 114 6.2.1 Real-time Traffic and Incident Detection 114 6.2.2 Modeling Predictive Traffic Flow 115 6.2.3 Dynamic Traffic Signal Optimization 115 6.2.4 Route Optimization and Traffic Demand Management 116 6.2.5 Integration with Connected and Autonomous Vehicles 116 6.3 Virtual Architecture for AI-based Traffic Management Systems 117 6.3.1 Cloud-edge Cooperation 118 6.3.2 Security and Computer Management 118 6.4 AI Applications in Sustainable Urban Mobility 119 6.4.1 Predictive Maintenance of Public Transport Infrastructure 119 6.4.2 Dynamic Riding Sharing and Micro Mobility Integration 120 6.4.3 Electric Vehicles (EV) Charging Optimization and Fleet Handling 120 6.5 Data-driven Mobility Solution 121 6.6 Case Studies of AI Implementation in Smart Cities 121 6.7 Moral and Political Views 121 6.8 Future Trends and Innovations 122 6.9 Conclusion 123 References 123 7 Reinforcement Learning for Energy-efficient Urban Freight Transportation 125 Nancy Jasmine Goldena and R. Rashia Subashree 7.1 Introduction 125 7.2 Fundamentals of RL 126 7.3 RL Applications in Energy-efficient Urban Freight Transportation 127 7.3.1 Dynamic Timetable (OR) Route Customization 127 7.3.2 Delivery Scheduling 129 7.3.3 Fleet Management 129 7.3.4 Vehicle Platooning and Coordinated Driving 129 7.3.5 Examples and Use of Cases 130 7.4 Integration of RL Applications with Smart Logistics and IoT 131 7.4.1 IOT’s Role in Smart Logistics 131 7.4.2 Takes RL-powered Decisions Using IoT Data 132 7.4.3 Integration Architecture 133 7.4.4 Use Cases in Urban Goods 134 7.4.5 Benefits of RL–IoT Integration 135 7.5 Challenges and Limitations 136 7.5.1 Data Quality and Availability 136 7.5.2 Calculation Complexity 136 7.5.3 Integration with Legacy Systems 136 7.5.4 Security and Reliability Problem 137 7.5.5 Moral and Regulatory Questions 137 7.6 Future Directions 137 7.7 Conclusion 138 References 139 8 Advancements and Challenges in Autonomous Vehicles and Smart Mobility: The Role of AI in Transforming Transportation 141 A. Jane, Dr. K. Merriliance, and Dr. Mary Immaculate Sheela Lourdusamy 8.1 Introduction 141 8.2 Advancements in Autonomous Vehicles and Smart Mobility 144 8.3 Artificial Intelligence in Autonomous Vehicles 145 8.3.1 Understanding AI in the Context of Self-driving Cars 145 8.3.2 Machine Learning in Autonomous Vehicles 145 8.3.3 Deep Learning in Autonomous Vehicles 146 8.4 Perception and Fusion of Sensors for AI-powered Automobiles 148 8.5 Advantages of AI in Autonomous Vehicles 150 8.5.1 Safety Improvements and Accident Reduction 150 8.5.2 Enhanced Traffic Efficiency and Reduced Congestion 151 8.5.3 Environmental Impact (Reduced Emission) 151 8.6 Challenges in Autonomous Vehicles and Smart Mobility 151 8.7 Future Directions and Conclusion 152 References 153 Further Reading 154 9 Enhancing Urban Traffic Management with Multi-scale Hierarchical GANs 159 Ashik Shah Jahangeer and P Shanmugavadivu 9.1 A System Stuck in Time 159 9.1.1 Literature Review 161 9.2 When GANs Hit the Road: The Gaps in Current AI Models 162 9.3 Reimagining Intelligence: The Architecture of MSH-GAN 164 9.4 The City in Layers: Micro and Macro-level Generators 167 9.4.1 Micro-level Generators: Learning the Pulse of the Street 167 9.4.2 Macro-level Generators: Capturing Systemic Flow 168 9.4.3 The Synchronization Mechanism 168 9.4.4 Real-world Implications 169 9.5 Listening to the City: Real-time IoT Data Integration 169 9.6 Understanding the Why: Hierarchical Modeling and Contextual Awareness 172 9.7 Thinking at the Edge: Decentralized Computation for Faster Response 174 9.8 Measuring Intelligence: Evaluating the Performance of MSH-GAN 176 9.8.1 Fidelity: Ensuring the Real Truth Is Not Lost 176 9.8.2 Accuracy: Predicting the What and the When 177 9.8.3 Responsiveness: Learning as Conditions Evolve 178 9.8.4 Simulation Utility: Planning Before Acting 178 9.8.5 Robustness: When the Unexpected Happens 179 9.9 From Control to Care: MSH-GAN and the Future of Smart Cities 179 9.9.1 Sustainable Mobility: From Emissions to Efficiency 180 9.9.2 Emergency Responsiveness: Making Every Second Count 180 9.9.3 Citizen-centric Planning: From Data to Dignity 181 9.9.4 Adaptation and Governance: A Living Urban System 181 9.9.5 A Philosophy of Coexistence 182 9.10 Looking Ahead: The Road Beyond MSH-GAN 182 9.10.1 Federated and Privacy-preserving Models 183 9.10.2 Integration with Reinforcement Learning (RL) 183 9.10.3 Multi-agent and Human-aware Traffic Models 183 9.10.4 Diffusion Models and Beyond-GAN Architectures 184 9.10.5 From Research to Deployment: Bridging the Gap 184 9.10.6 A Message to the Reader 184 References 184 10 IoT and AI Integration in Traffic Management 187 J. Steffi, K. Merriliance, and Mary Immaculate Sheela Lourdusamy 10.1 Introduction 187 10.1.1 Urbanization and Traffic Challenges 187 10.1.2 Limitations of Traditional Traffic Management 187 10.1.3 Emergence of Smart Traffic Management 188 10.1.4 The Role of IoT and AI 188 10.1.5 Objectives and Benefits 188 10.2 Role of IoT in Traffic Management 188 10.2.1 IoT in Urban Traffic Systems 188 10.2.2 Key IoT Components in Traffic Infrastructure 188 10.2.3 Data Collection and Real-time Monitoring 189 10.2.4 Predictive Maintenance and Infrastructure Management 190 10.2.5 Vehicle-to-infrastructure (V2I) Communication 190 10.3 AI Applications in Traffic Optimization 191 10.3.1 AI-observation in the Traffic System 191 10.3.2 Machine Learning for Pattern Detection 192 10.3.3 Deep Learning for Object and Scenario Recognition 192 10.3.4 Reinforcement Learning for Dynamic Signal Control 192 10.3.5 Future Staging Analysis and Traffic Forecast 192 10.3.6 AI in Automated Decision-making Systems 192 10.3.7 Key Benefits of AI in Traffic Optimization 193 10.4 Smart Traffic Signals and AI-driven Control Systems 193 10.4.1 Introduction to Smart Traffic Signals 193 10.4.2 Integration with IoT Sensors 193 10.4.3 Use of Reinforcement Learning for Signal Optimization 194 10.4.4 Real-time Adaptive Signal Control 194 10.4.5 Multi-interest Coordination 194 10.4.6 Emergency Vehicle Prioritization 195 10.4.7 Benefits of AI-driven Traffic Signals 195 10.5 Incident Detection and Emergency Response 195 10.5.1 Introduction to Incident Detection 195 10.5.2 AI-driven Computer Vision Systems 195 10.5.3 Automated Alert Systems 196 10.5.4 Smart Rerouting and Traffic Adjustment 196 10.5.5 Integration with Emergency Services 196 10.5.6 Benefits of AI-enabled Emergency Response 196 10.6 Public Transport Enhancement with IoT and AI 196 10.6.1 Overview of Public Transport Challenges 196 10.6.2 IoT-based Real-time Fleet Monitoring 197 10.6.3 Route and Schedule Optimization Using AI 197 10.6.4 Passenger Information Systems 197 10.6.5 Multimodal Transport Integration 197 10.6.6 Maintenance and Safety Management 197 10.6.7 Sustainability and Emission Reduction 197 10.6.8 Key Benefits of AI and IoT in Public Transport 198 10.7 Environmental and Sustainability Benefits 198 10.7.1 Reduction in Greenhouse Gas Emissions 198 10.7.2 Promotion of Public and Shared Mobility 198 10.7.3 Energy Efficiency of Traffic Infrastructure 198 10.7.4 Better Air Quality and Reduced Noise 199 10.7.5 Support for Sustainable Urban Policy and Planning 199 10.7.6 Promotion of Green Vehicles 199 10.7.7 Circular Economy and Smart Waste Reduction 199 10.8 Challenges and Future Trends 200 10.8.1 Challenges in IoT and AI-driven Traffic Systems 200 10.8.1.1 Data Security and Privacy Issues 200 10.8.1.2 Maintenance and Infrastructure Costs 200 10.8.1.3 Legacy System Integration 200 10.8.1.4 Data Quality and Sensor Reliability 200 10.8.1.5 Ethical and Social Implications 200 10.8.2 Future Trends in Smart Traffic Management 201 10.8.2.1 Integration with 5G Networks 201 10.8.2.2 Autonomous and Connected Vehicles (CAVs) 201 10.8.2.3 Edge and Fog Computing 201 10.8.2.4 AI-driven Urban Planning 201 10.8.2.5 Green and Sustainable Mobility 201 10.8.2.6 Digital Twins and Simulation 201 10.8.2.7 AI Regulation and Governance 202 References 202 11 Intelligent Urbanism: AI and Big Data-driven Approaches to Planning, Design, and Transportation 205 R. Saradha 11.1 Introduction 205 11.2 Literature Background 207 11.3 Methodology 210 11.3.1 Types of AI-based Tools for Urban Planning 212 11.3.2 New Approaches to the Complexity of Urban Planning 213 11.3.2.1 New Framework for Conceptualization 214 11.3.3 Data Sources Supporting AI-based Urban Analysis 215 11.3.3.1 Role of Data Analytics and AI in Smart City 215 11.3.3.2 Data Sources 216 11.3.3.3 Urban Survey and Statistical Information 217 11.3.3.4 Urban Big Data Sources 218 11.3.4 Feasibility of AI-related Tools in Urban Design 218 11.4 Results and Discussion 221 11.5 Conclusion 223 11.5.1 Future Direction of Studies 225 References 226 12 AI-driven Public Transport Solutions 231 Shantanu Bindewari, Prakhar Consul, Hilal Ahmed Shah, Basab Nath, and Mansi Trivedi 12.1 Introduction 231 12.2 AI Applications in Transportation 234 12.2.1 AI in Traffic Management 234 12.2.2 Predictive Maintenance and Diagnostics 234 12.2.3 AI in Logistics and Fleet Management 235 12.2.4 Autonomous Vehicles 235 12.2.5 Public Transport Optimization 236 12.2.6 Infrastructure Monitoring and Smart Cities 236 12.2.7 Emergency Response and Incident Management 237 12.2.8 Environmental Monitoring and Sustainability 237 12.3 Introduction to AI in Automation and Ticketing 237 12.3.1 AI in Ticket Generation and Validation 238 12.3.2 AI-based Ticket Collectors and Validators 238 12.3.3 Self-handling Ticketing Systems 239 12.3.4 AI-enabled Women Safety Alarm Systems 239 12.3.5 Integration with Smart Mobility Platforms 240 12.3.6 AI in Dynamic Pricing and Revenue Management 240 12.3.7 Challenges and Considerations 240 12.3.8 Case Studies and Real-world Implementations 241 12.4 AI for Safety and Security 241 12.4.1 AI in Surveillance and Monitoring 241 12.4.2 Real-time Incident Detection 242 12.4.3 AI for Emergency Management 242 12.4.4 Women and Vulnerable Passenger Safety 242 12.4.5 Preventive Maintenance for Safety 242 12.4.6 Cybersecurity in AI-enabled Systems 243 12.4.7 AI Use in Driver Aid and Autonomous Cars 243 12.4.8 Ethical and Legal Implications 243 12.5 Dynamic Route Optimization Systems 243 12.5.1 AI in Real-time Traffic Monitoring 244 12.5.2 Personalized Route Recommendations 244 12.5.3 Environmental and Economic Benefits 245 12.5.4 AI and Emergency Response Routing 245 12.6 AI in Public vs. Private Transportation 245 12.6.1 Rise of AI in Transportation 245 12.6.2 Use Cases in Public Transit 246 12.6.3 Smart Mobility in Ride-sharing and Autonomous Vehicles 247 12.6.4 Comparing AI in Public vs. Private Transportation 247 12.6.5 Case Studies 247 12.6.6 The Future of AI in Transportation 249 12.7 Challenges and Ethical Considerations 249 12.8 Future Trends and Innovations 250 12.9 Conclusion 251 References 252 13 AI-driven Data Analytics for Smart Urban Transport: Innovations, Challenges, and Future Trends 255 A. Jasmine Sugil, K. Merriliance, and Mary Immaculate Sheela Lourdusamy 13.1 Introduction 255 13.2 AI-powered Data Sources in Urban Transport 260 13.2.1 Intelligent Infrastructure and IoT Sensors 262 13.2.2 GPS and Location-based Data 263 13.2.3 Mobile Applications and User Behavior 263 13.2.4 Smart Ticketing and Fare Data 263 13.2.5 Video Surveillance and AI Vision 264 13.2.6 Social Feedback and Public Media 264 13.2.7 Connected and AVs 264 13.2.8 Weather and Environmental Conditions 264 13.2.9 City Maintenance and Infrastructure Data 265 13.2.10 Government and Agency Open Data 265 13.2.11 Statistical Insights on Urban Transport 265 13.2.11.1 Development in Urban Transport Information 265 13.2.11.2 Traffic Mobbing Costs 265 13.2.11.3 Implementation of Intelligent Transport Systems 265 13.2.11.4 Public Transport Use and Optimization 266 13.2.11.5 Influence of Real-time Analytics 266 13.3 AI Techniques for Urban Transport Analytics 266 13.3.1 Predictive Modeling and Machine Learning 266 13.3.2 Deep Learning in High-density Urban Areas 266 13.3.3 Natural Language Processing for Public Feedback 267 13.3.4 Computer Vision in Real-time Observation 267 13.3.5 Traffic Optimization Through Reinforcement Learning 267 13.3.6 Pattern Recognition and Clustering 268 13.3.7 Simulation and Scenario Modeling 268 13.3.8 The Data Analysis Tool for Urban Transport 268 13.4 Key Applications of AI in Urban Transport 270 13.5 Case Studies and Real-world Implementations 272 13.6 Challenges and Ethical Considerations 275 13.6.1 Technical Challenges: The Complexity of Urban Infrastructure 275 13.6.2 Data Privacy and Security: Safeguarding Personal Information 275 13.6.3 Job Displacement: The Employment Impact 276 13.6.4 Accessibility and Inclusivity: Getting AI to Work for All 276 13.7 Future Trends in AI for Urban Transport 277 13.7.1 The Rise of AVs 277 13.7.2 Intelligent Transportation Systems (ITS) 277 13.7.3 Shared Mobility and Micro-mobility Solutions 278 13.7.4 Data-driven Urban Planning 278 13.7.5 AI in Sustainability and Green Mobility 278 13.7.6 The Growing Role of AI in Safety and Security 279 13.7.7 The Road Ahead 279 13.8 Conclusion 280 References 280 14 Transforming Smart Mobility: L4S and NaaS APIs for Real-time Traffic Management and Autonomous Transport 283 L. Ameer Shohail 14.1 Introduction 283 14.2 Architectural Foundation for Real-time and Autonomous Mobility 284 14.2.1 Role of Programmable Interfaces in Transport Systems 284 14.2.2 5G Core Components Supporting Application-level Control 285 14.2.2.1 Network Exposure Function (NEF) 285 14.2.2.2 Policy Control Function (PCF) 286 14.2.2.3 User Plane Function (UPF) 286 14.2.3 Enabling Low-latency Handling with L4S 287 14.2.3.1 Dual Queue Architecture 287 14.2.3.2 API-driven Activation of L4S 287 14.2.4 Low Latency Queuing with L4S 287 14.3 Current Directions in Programmable Transport Networks and LatencyControl 288 14.3.1 Evolution of Network Exposure in Transport Systems 289 14.3.2 Advancements in Policy Control and Flow Enforcement 289 14.3.3 L4S in Context-aware Transport Scenarios 290 14.3.4 Research Gaps and Architectural Integration 291 14.4 System Design and Implementation Strategy for Real-time Mobility Control 291 14.4.1 Simulation Architecture and Environment Setup 292 14.4.2 API Workflow and Service Control Logic 292 14.4.3 Flow Classification and L4S Enforcement 293 14.4.4 Charging and Policy Enforcement Integration 293 14.5 Results from Real-time Policy and Queue Enforcement 293 14.5.1 Traffic Flow Differentiation and Queue Selection 293 14.5.2 Latency and Queue Performance Under Load 294 14.5.3 Real-time Charging Trigger Accuracy 295 14.5.4 Observations and Summary 295 14.6 Reflections on Programmable Responsiveness in Urban Mobility 296 14.6.1 Aligning Network Responsiveness with Transport System Needs 297 14.6.2 Relevance to Sustainable and AI-driven Urban Mobility 297 14.6.3 Operational and Deployment Considerations 297 14.6.4 Contribution to the Field and Future Integration Potential 298 14.7 Conclusion 298 Acknowledgments 299 Nomenclature 299 Abbreviations 299 References 300 15 AI-driven Public Transportation: Enhancing Efficiency, Sustainability, and User Experience 301 M. Robinson Joel 15.1 Introduction 301 15.2 Existing AI Uses in Public Transportation 304 15.3 Recognizing AI’s Significance in Transportation 305 15.4 AI Applications in Transportation: Exemplary Instances 307 15.5 Traffic Management Systems Using AI 309 15.6 Top AI Resources for Public Transportation 310 15.6.1 Moovit App 311 15.6.2 Citymapper 311 15.6.3 Uber Movement 312 15.6.4 Trapeze Group 314 15.6.5 Trainline 315 15.6.6 Transit 316 15.6.7 Waze for Cities 317 15.6.8 BusIt 317 15.7 AI Improve Public Transportation Efficiency 318 15.8 Safety Benefits of AI in Public Transportation 319 15.9 Flowchart for GPS-based Vehicle Tracking 321 15.10 Build Your Own ESP32 GPS Tracker with Live Tracking 324 15.11 Market Share of AI in Transportation by Different Elements 326 15.11.1 Analysis Based on Component 326 15.11.2 Analysis Based on Technology 327 15.11.3 Analysis Based on Application 327 15.11.4 Analysis Regional Wise 329 15.11.5 Analysis of Key Players 330 15.12 Related Work 331 15.13 Conclusion 336 References 337 16 Cognitive AI for Adaptive and Resilient Urban Transportation: A Data-driven Approach to Sustainable Mobility 343 Vishal Jain, Archan Mitra, and Sanchita Paul 16.1 Introduction 343 16.1.1 Contextual Overview of Urban Mobility Challenges in the Era of SmartCities 343 16.1.2 Limitations of Traditional and Rule-based AI Systems in Transport 344 16.1.3 Emergence and Importance of Cognitive AI in Solving Complex Transport Dynamics 344 16.1.4 Problem Statement 345 16.1.5 Research Objectives 345 16.1.6 Significance of the Study 346 16.2 Conceptual Framework and Literature Review 346 16.2.1 Understanding Cognitive Artificial Intelligence in the Urban MobilityContext 346 16.2.2 Advanced Applications of Cognitive AI in Traffic Management 347 16.2.3 AI-enhanced Public Transit Systems and Passenger Experience 347 16.2.4 Predictive Maintenance and Asset Management Using AI 348 16.2.5 Role of Cognitive AI in Autonomous Vehicle Integration 348 16.2.6 Sustainable Transport Solutions Powered by AI 348 16.2.7 Theoretical Constructs: Adaptation, Resilience, and Data-driven Urbanism 349 16.2.8 Research Gaps and Future Directions 349 16.3 Methodology 350 16.3.1 Research Design 350 16.3.2 Data Sources and Collection 350 16.3.3 Tools and Platforms 351 16.3.4 Analytical Approaches 351 16.4 Integrated Cognitive AI Framework for Urban Transportation 352 16.4.1 Description of the Proposed Architecture 353 16.4.1.1 Data Input Layer 353 16.4.1.2 Cognitive AI Processing Core 353 16.4.1.3 Feedback and Adaptation Loop 353 16.4.2 Key Modules of the Framework 354 16.4.2.1 Dynamic Traffic Control 354 16.4.2.2 Predictive Public Transport Scheduling 354 16.4.2.3 Autonomous Vehicle Coordination 354 16.5 Data Analysis and Empirical Findings 355 16.5.1 Dynamic Traffic Control 355 16.5.2 Predictive Public Transport Scheduling 356 16.5.3 Autonomous Vehicle Coordination 356 16.5.4 Carbon Emission Tracking 357 16.5.5 AI-led Predictive Maintenance 357 16.6 Discussion 358 16.6.1 Integrating Real-time Decision-making with Adaptive SystemDesign 358 16.6.2 Predictive Scheduling as a Catalyst for Public Transit Optimization 359 16.6.3 Multimodal and Cooperative AV Ecosystems 359 16.6.4 Environmental Sustainability Through Data-driven Carbon Reduction 359 16.6.5 Resilient Infrastructure Through Predictive Maintenance 360 16.6.6 Socio-technical and Governance Challenges 360 16.6.7 Pathways for Future Research and Policy Integration 361 16.7 Conclusion 361 References 362 17 Optimizing Urban Traffic with Graph Analytics: A Case Study of a Metropolitan Transportation Network 367 S. Rakshika and Sudeepa Roy Dey 17.1 Introduction 367 17.2 Related Work 370 17.3 Types of Routing Algorithm 372 17.4 Work 375 References 384 18 Urban Mobility Reimagined: AMRUT Interventions and the 2041 Outlook 387 S. Thangapriya, Nancy Jasmine Goldena, T. S. Vasughi, M. Kannan, and Barath Ramesh 18.1 Introduction 387 18.2 Geospatial Mapping of Tirunelveli Using Advanced Technologies 388 18.3 Identifying Research Gaps in Tirunelveli for Sustainable Regional Development 389 18.3.1 Topography 389 18.4 Climate and Rainfall 391 18.5 Precipitation 392 18.6 Soil Type Analysis and Resource-efficient Agricultural Planning in Tirunelveli Region 393 18.7 Geomorphology 394 18.8 A Road map for Tirunelveli’s Future Economy 397 18.9 AI-based Urban Housing Analytics and Slum Rehabilitation Forecasting for Tirunelveli LPA 398 18.10 Tirunelveli 2041 as a Sustainable Growth Use Case 399 18.11 Conclusion 403 References 404 19 GIS-based Analysis of Road Accidents: A Case Study on Hotspot Identification and Safety Improvement 407 19.1 Introduction 407 19.2 Methodology 408 19.3 Results and Discussion 408 19.4 Data Collection and Preparation 408 19.5 Analysis 412 19.6 Identifying Blackspots Using GIS 414 19.7 Key Insights 416 19.8 Conclusion 417 19.9 Recommendations 417 19.10 Way Forward 417 References 418 Index 421

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Sathyan Munirathinam, PhD, is a Senior Manager of Data & Analytics at ASML Corporation in USA. Pethuru R. Chelliah, PhD, is the Vice President of Reliance Jio Platforms Ltd. in Bangalore, India. Peter Augustine, PhD, is a Professor in the Department of Computer Science at CHRIST (Deemed to be University) in Bangalore, India. Beaulah Soundarabai, PhD, is an Associate Professor in the Department of Computer Science at CHRIST (Deemed to be University) in Bangalore, India.

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