Artificial Intelligence (AI) for Smart and Sustainable Urban Transportation

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.

• ContentsList of Contributors xxiAbout the Editors xxvii1 Recent Trends in Intelligent Transportation Systems 1Kathi Durgesh, Siddharth Garia, and Vishal Kumar Narnoli1.1 Introduction 11.2 Methodology 31.3 Results 91.4 Discussion 10References 10Further Reading 122 Artificial Intelligence and IoT Applications Transforming the Automotive Industry 15Raviprakash R Salagame2.1 Introduction 152.1.1 Overview of Key Digital Technologies and Applications 162.1.2 IoT (Internet of Things) and Sensors 172.1.3 Artificial Intelligence (AI) and Machine Learning 182.1.3.1 Machine Learning 192.1.3.2 AI and ML Applications in Automotive 202.1.4 Cloud Computing 222.1.5 Cybersecurity 222.2 Automotive AI Applications and Urban Use Cases 232.2.1 Autonomous Vehicles 232.2.1.1 How Autonomous Vehicles (AV) Work 252.2.1.2 How AI Is Used in Autonomous Systems 262.2.1.3 Perception and Localization 262.2.1.4 Path Planning 272.2.1.5 Synthetic Data Generation 272.2.1.6 Verification and Validation of AV Systems 282.2.1.7 Safe Deployment of AV Systems 282.2.1.8 Autonomous Urban Use Cases 282.3 Connected Vehicles 302.3.1 How AI Is Used in Connected Vehicles 312.3.1.1 Personalized User experience 312.3.1.2 Navigation Guidance 312.3.1.3 Vehicle Status Monitoring 312.3.1.4 Connected Vehicle Urban Use Cases 322.4 Electric Vehicles (EV) 322.4.1 AI-based Urban Use Cases of EVs 332.4.1.1 Remote Monitoring of EV Fleets 332.4.1.2 EV Charging Infrastructure Management 342.4.1.3 Enhancing User Experience 342.5 Shared Mobility 352.6 Future Trends Toward Smart Transportation 352.7 Conclusion 36References 373 Artificial Intelligence in Transportation Using Automated Grading, Adaptive Learning, and Predictive Maintenance to Increase Efficiency 41S. Cyciliya Pearline Christy, K. Merriliance, and Mary Immaculate Sheela Lourdusamy3.1 Introduction 413.1.1 Background of AI in Transportation 413.1.2 Purpose and Scope of the Chapter 423.1.3 Importance of Efficiency in Modern Transportation Systems 423.2 Overview of Artificial Intelligence in Transportation 423.2.1 What is AI and How It Applies to Transportation 423.2.2 Current Trends in AI Adoption 433.2.3 Challenges in Implementing AI Technologies 433.3 Automated Grading Systems in Transportation 443.3.1 Definition and Role in Infrastructure Evaluation 443.3.2 AI Techniques Used (e.g., Computer Vision, ML) 443.3.3 Real-world Applications (e.g., Road Surface Assessment, Bridge Safety) 453.3.4 Benefits and Limitations 453.4 Adaptive Learning in Traffic Management and Logistics 463.4.1 What Is Adaptive Learning? 463.4.2 Traffic Signal Optimization and Route Planning 463.4.3 AI-driven Logistics and Fleet Management 473.4.4 Case Studies and Pilot Projects 473.5 Predictive Maintenance in Transportation Systems 483.5.1 Introduction to Predictive Maintenance 483.5.2 Sensors and Data Collection Techniques 483.5.3 Machine Learning Models for Failure Prediction 493.5.4 Impact on Cost, Downtime, and Safety 493.6 Ethical, Regulatory, and Security Considerations 503.6.1 Data Privacy in AI Systems 503.6.2 AI Bias and Fairness 503.6.3 Regulatory Frameworks and Standards 513.6.4 Cybersecurity in AI-driven Infrastructure 513.7 Future Outlook and Emerging Technologies 513.7.1 Role of Generative AI and Digital Twins 523.7.2 Integration with Smart Cities 523.7.3 Autonomous Vehicles and AI Coordination 523.8 Conclusion 533.8.1 Summary of Key Points 533.8.2 Implications for Policymakers and Practitioners 533.8.3 Final Thoughts on the Future of AI in Transportation 54References 544 Autonomous Vehicles and Smart Mobility 57P. Sudheer, S. Ashmad, M. Saravanan, and A. Immanuel4.1 Introduction 574.2 Challenges in Smart Mobility and Autonomous Vehicles 624.3 Case Studies and Practical Applications of Smart Mobility and Self-driving Cars 644.4 Policies, Ethics, and Governance in the Autonomous Vehicle Ecosystem 66References 695 Artificial Intelligence (AI) for Smart and Sustainable Urban Transportation 73Ishika Gupta, Hriday Gupta, Siddharth Gupta, and Prerna Ajmani5.1 Introduction 735.2 Background 745.2.1 Introduction to 6G, Smart Cities, and EVs 745.2.2 Evolution from 5G to 6G: Key Features and Capabilities 745.2.3 Concept of Sustainable Smart Cities, EVs, and Their Importance 755.2.3.1 Sustainable Smart Cities 755.2.3.2 Electric Vehicles 765.2.4 Role of AI and IoT in Building Smart and Sustainable Urban Environments 775.2.5 Role of AI and IoT in Building Electric Vehicles 775.2.5.1 AI in Electric Vehicles 775.2.5.2 IoT in Electric Vehicles 785.2.5.3 Synergy of AI, IoT, and 6G 785.3 Enabling Technologies 785.4 Components 845.4.1 Smart City 845.4.2 Electric Vehicles 895.5 AI-driven Sustainable Solutions 925.6 Security and Privacy in AI and IoT for Smart Cities and Electric Vehicles 955.6.1 Smart Cities 955.6.2 Security and Privacy in AI and IoT for Electric Vehicles 985.6.3 Key Security Challenges in Connected Electric Vehicles 985.6.3.1 Role of AI in Security for Electric Vehicles 995.6.3.2 Role of Blockchain and DLT for Privacy and Trust 995.6.3.3 6G and the Future of EV Cybersecurity 995.7 Case Studies and Real-world Implementations 1005.7.1 Smart Cities 1005.7.2 Electric Vehicles 1035.8 Challenges for 6G 1055.9 Future Directions 1065.10 Conclusion 110References 1116 Smart Mobility: Integrating AI for Sustainable Urban Transportation Solutions 113A. Jothi Kumar6.1 Introduction 1136.2 AI in Traffic Management Systems 1146.2.1 Real-time Traffic and Incident Detection 1146.2.2 Modeling Predictive Traffic Flow 1156.2.3 Dynamic Traffic Signal Optimization 1156.2.4 Route Optimization and Traffic Demand Management 1166.2.5 Integration with Connected and Autonomous Vehicles 1166.3 Virtual Architecture for AI-based Traffic Management Systems 1176.3.1 Cloud-edge Cooperation 1186.3.2 Security and Computer Management 1186.4 AI Applications in Sustainable Urban Mobility 1196.4.1 Predictive Maintenance of Public Transport Infrastructure 1196.4.2 Dynamic Riding Sharing and Micro Mobility Integration 1206.4.3 Electric Vehicles (EV) Charging Optimization and Fleet Handling 1206.5 Data-driven Mobility Solution 1216.6 Case Studies of AI Implementation in Smart Cities 1216.7 Moral and Political Views 1216.8 Future Trends and Innovations 1226.9 Conclusion 123References 1237 Reinforcement Learning for Energy-efficient Urban Freight Transportation 125Nancy Jasmine Goldena and R. Rashia Subashree7.1 Introduction 1257.2 Fundamentals of RL 1267.3 RL Applications in Energy-efficient Urban Freight Transportation 1277.3.1 Dynamic Timetable (OR) Route Customization 1277.3.2 Delivery Scheduling 1297.3.3 Fleet Management 1297.3.4 Vehicle Platooning and Coordinated Driving 1297.3.5 Examples and Use of Cases 1307.4 Integration of RL Applications with Smart Logistics and IoT 1317.4.1 IOT’s Role in Smart Logistics 1317.4.2 Takes RL-powered Decisions Using IoT Data 1327.4.3 Integration Architecture 1337.4.4 Use Cases in Urban Goods 1347.4.5 Benefits of RL–IoT Integration 1357.5 Challenges and Limitations 1367.5.1 Data Quality and Availability 1367.5.2 Calculation Complexity 1367.5.3 Integration with Legacy Systems 1367.5.4 Security and Reliability Problem 1377.5.5 Moral and Regulatory Questions 1377.6 Future Directions 1377.7 Conclusion 138References 1398 Advancements and Challenges in Autonomous Vehicles and Smart Mobility: The Role of AI in Transforming Transportation 141A. Jane, Dr. K. Merriliance, and Dr. Mary Immaculate Sheela Lourdusamy8.1 Introduction 1418.2 Advancements in Autonomous Vehicles and Smart Mobility 1448.3 Artificial Intelligence in Autonomous Vehicles 1458.3.1 Understanding AI in the Context of Self-driving Cars 1458.3.2 Machine Learning in Autonomous Vehicles 1458.3.3 Deep Learning in Autonomous Vehicles 1468.4 Perception and Fusion of Sensors for AI-powered Automobiles 1488.5 Advantages of AI in Autonomous Vehicles 1508.5.1 Safety Improvements and Accident Reduction 1508.5.2 Enhanced Traffic Efficiency and Reduced Congestion 1518.5.3 Environmental Impact (Reduced Emission) 1518.6 Challenges in Autonomous Vehicles and Smart Mobility 1518.7 Future Directions and Conclusion 152References 153Further Reading 1549 Enhancing Urban Traffic Management with Multi-scale Hierarchical GANs 159Ashik Shah Jahangeer and P Shanmugavadivu9.1 A System Stuck in Time 1599.1.1 Literature Review 1619.2 When GANs Hit the Road: The Gaps in Current AI Models 1629.3 Reimagining Intelligence: The Architecture of MSH-GAN 1649.4 The City in Layers: Micro and Macro-level Generators 1679.4.1 Micro-level Generators: Learning the Pulse of the Street 1679.4.2 Macro-level Generators: Capturing Systemic Flow 1689.4.3 The Synchronization Mechanism 1689.4.4 Real-world Implications 1699.5 Listening to the City: Real-time IoT Data Integration 1699.6 Understanding the Why: Hierarchical Modeling and Contextual Awareness 1729.7 Thinking at the Edge: Decentralized Computation for Faster Response 1749.8 Measuring Intelligence: Evaluating the Performance of MSH-GAN 1769.8.1 Fidelity: Ensuring the Real Truth Is Not Lost 1769.8.2 Accuracy: Predicting the What and the When 1779.8.3 Responsiveness: Learning as Conditions Evolve 1789.8.4 Simulation Utility: Planning Before Acting 1789.8.5 Robustness: When the Unexpected Happens 1799.9 From Control to Care: MSH-GAN and the Future of Smart Cities 1799.9.1 Sustainable Mobility: From Emissions to Efficiency 1809.9.2 Emergency Responsiveness: Making Every Second Count 1809.9.3 Citizen-centric Planning: From Data to Dignity 1819.9.4 Adaptation and Governance: A Living Urban System 1819.9.5 A Philosophy of Coexistence 1829.10 Looking Ahead: The Road Beyond MSH-GAN 1829.10.1 Federated and Privacy-preserving Models 1839.10.2 Integration with Reinforcement Learning (RL) 1839.10.3 Multi-agent and Human-aware Traffic Models 1839.10.4 Diffusion Models and Beyond-GAN Architectures 1849.10.5 From Research to Deployment: Bridging the Gap 1849.10.6 A Message to the Reader 184References 18410 IoT and AI Integration in Traffic Management 187J. Steffi, K. Merriliance, and Mary Immaculate Sheela Lourdusamy10.1 Introduction 18710.1.1 Urbanization and Traffic Challenges 18710.1.2 Limitations of Traditional Traffic Management 18710.1.3 Emergence of Smart Traffic Management 18810.1.4 The Role of IoT and AI 18810.1.5 Objectives and Benefits 18810.2 Role of IoT in Traffic Management 18810.2.1 IoT in Urban Traffic Systems 18810.2.2 Key IoT Components in Traffic Infrastructure 18810.2.3 Data Collection and Real-time Monitoring 18910.2.4 Predictive Maintenance and Infrastructure Management 19010.2.5 Vehicle-to-infrastructure (V2I) Communication 19010.3 AI Applications in Traffic Optimization 19110.3.1 AI-observation in the Traffic System 19110.3.2 Machine Learning for Pattern Detection 19210.3.3 Deep Learning for Object and Scenario Recognition 19210.3.4 Reinforcement Learning for Dynamic Signal Control 19210.3.5 Future Staging Analysis and Traffic Forecast 19210.3.6 AI in Automated Decision-making Systems 19210.3.7 Key Benefits of AI in Traffic Optimization 19310.4 Smart Traffic Signals and AI-driven Control Systems 19310.4.1 Introduction to Smart Traffic Signals 19310.4.2 Integration with IoT Sensors 19310.4.3 Use of Reinforcement Learning for Signal Optimization 19410.4.4 Real-time Adaptive Signal Control 19410.4.5 Multi-interest Coordination 19410.4.6 Emergency Vehicle Prioritization 19510.4.7 Benefits of AI-driven Traffic Signals 19510.5 Incident Detection and Emergency Response 19510.5.1 Introduction to Incident Detection 19510.5.2 AI-driven Computer Vision Systems 19510.5.3 Automated Alert Systems 19610.5.4 Smart Rerouting and Traffic Adjustment 19610.5.5 Integration with Emergency Services 19610.5.6 Benefits of AI-enabled Emergency Response 19610.6 Public Transport Enhancement with IoT and AI 19610.6.1 Overview of Public Transport Challenges 19610.6.2 IoT-based Real-time Fleet Monitoring 19710.6.3 Route and Schedule Optimization Using AI 19710.6.4 Passenger Information Systems 19710.6.5 Multimodal Transport Integration 19710.6.6 Maintenance and Safety Management 19710.6.7 Sustainability and Emission Reduction 19710.6.8 Key Benefits of AI and IoT in Public Transport 19810.7 Environmental and Sustainability Benefits 19810.7.1 Reduction in Greenhouse Gas Emissions 19810.7.2 Promotion of Public and Shared Mobility 19810.7.3 Energy Efficiency of Traffic Infrastructure 19810.7.4 Better Air Quality and Reduced Noise 19910.7.5 Support for Sustainable Urban Policy and Planning 19910.7.6 Promotion of Green Vehicles 19910.7.7 Circular Economy and Smart Waste Reduction 19910.8 Challenges and Future Trends 20010.8.1 Challenges in IoT and AI-driven Traffic Systems 20010.8.1.1 Data Security and Privacy Issues 20010.8.1.2 Maintenance and Infrastructure Costs 20010.8.1.3 Legacy System Integration 20010.8.1.4 Data Quality and Sensor Reliability 20010.8.1.5 Ethical and Social Implications 20010.8.2 Future Trends in Smart Traffic Management 20110.8.2.1 Integration with 5G Networks 20110.8.2.2 Autonomous and Connected Vehicles (CAVs) 20110.8.2.3 Edge and Fog Computing 20110.8.2.4 AI-driven Urban Planning 20110.8.2.5 Green and Sustainable Mobility 20110.8.2.6 Digital Twins and Simulation 20110.8.2.7 AI Regulation and Governance 202References 20211 Intelligent Urbanism: AI and Big Data-driven Approaches to Planning, Design, and Transportation 205R. Saradha11.1 Introduction 20511.2 Literature Background 20711.3 Methodology 21011.3.1 Types of AI-based Tools for Urban Planning 21211.3.2 New Approaches to the Complexity of Urban Planning 21311.3.2.1 New Framework for Conceptualization 21411.3.3 Data Sources Supporting AI-based Urban Analysis 21511.3.3.1 Role of Data Analytics and AI in Smart City 21511.3.3.2 Data Sources 21611.3.3.3 Urban Survey and Statistical Information 21711.3.3.4 Urban Big Data Sources 21811.3.4 Feasibility of AI-related Tools in Urban Design 21811.4 Results and Discussion 22111.5 Conclusion 22311.5.1 Future Direction of Studies 225References 22612 AI-driven Public Transport Solutions 231Shantanu Bindewari, Prakhar Consul, Hilal Ahmed Shah, Basab Nath, and Mansi Trivedi12.1 Introduction 23112.2 AI Applications in Transportation 23412.2.1 AI in Traffic Management 23412.2.2 Predictive Maintenance and Diagnostics 23412.2.3 AI in Logistics and Fleet Management 23512.2.4 Autonomous Vehicles 23512.2.5 Public Transport Optimization 23612.2.6 Infrastructure Monitoring and Smart Cities 23612.2.7 Emergency Response and Incident Management 23712.2.8 Environmental Monitoring and Sustainability 23712.3 Introduction to AI in Automation and Ticketing 23712.3.1 AI in Ticket Generation and Validation 23812.3.2 AI-based Ticket Collectors and Validators 23812.3.3 Self-handling Ticketing Systems 23912.3.4 AI-enabled Women Safety Alarm Systems 23912.3.5 Integration with Smart Mobility Platforms 24012.3.6 AI in Dynamic Pricing and Revenue Management 24012.3.7 Challenges and Considerations 24012.3.8 Case Studies and Real-world Implementations 24112.4 AI for Safety and Security 24112.4.1 AI in Surveillance and Monitoring 24112.4.2 Real-time Incident Detection 24212.4.3 AI for Emergency Management 24212.4.4 Women and Vulnerable Passenger Safety 24212.4.5 Preventive Maintenance for Safety 24212.4.6 Cybersecurity in AI-enabled Systems 24312.4.7 AI Use in Driver Aid and Autonomous Cars 24312.4.8 Ethical and Legal Implications 24312.5 Dynamic Route Optimization Systems 24312.5.1 AI in Real-time Traffic Monitoring 24412.5.2 Personalized Route Recommendations 24412.5.3 Environmental and Economic Benefits 24512.5.4 AI and Emergency Response Routing 24512.6 AI in Public vs. Private Transportation 24512.6.1 Rise of AI in Transportation 24512.6.2 Use Cases in Public Transit 24612.6.3 Smart Mobility in Ride-sharing and Autonomous Vehicles 24712.6.4 Comparing AI in Public vs. Private Transportation 24712.6.5 Case Studies 24712.6.6 The Future of AI in Transportation 24912.7 Challenges and Ethical Considerations 24912.8 Future Trends and Innovations 25012.9 Conclusion 251References 25213 AI-driven Data Analytics for Smart Urban Transport: Innovations, Challenges, and Future Trends 255A. Jasmine Sugil, K. Merriliance, and Mary Immaculate Sheela Lourdusamy13.1 Introduction 25513.2 AI-powered Data Sources in Urban Transport 26013.2.1 Intelligent Infrastructure and IoT Sensors 26213.2.2 GPS and Location-based Data 26313.2.3 Mobile Applications and User Behavior 26313.2.4 Smart Ticketing and Fare Data 26313.2.5 Video Surveillance and AI Vision 26413.2.6 Social Feedback and Public Media 26413.2.7 Connected and AVs 26413.2.8 Weather and Environmental Conditions 26413.2.9 City Maintenance and Infrastructure Data 26513.2.10 Government and Agency Open Data 26513.2.11 Statistical Insights on Urban Transport 26513.2.11.1 Development in Urban Transport Information 26513.2.11.2 Traffic Mobbing Costs 26513.2.11.3 Implementation of Intelligent Transport Systems 26513.2.11.4 Public Transport Use and Optimization 26613.2.11.5 Influence of Real-time Analytics 26613.3 AI Techniques for Urban Transport Analytics 26613.3.1 Predictive Modeling and Machine Learning 26613.3.2 Deep Learning in High-density Urban Areas 26613.3.3 Natural Language Processing for Public Feedback 26713.3.4 Computer Vision in Real-time Observation 26713.3.5 Traffic Optimization Through Reinforcement Learning 26713.3.6 Pattern Recognition and Clustering 26813.3.7 Simulation and Scenario Modeling 26813.3.8 The Data Analysis Tool for Urban Transport 26813.4 Key Applications of AI in Urban Transport 27013.5 Case Studies and Real-world Implementations 27213.6 Challenges and Ethical Considerations 27513.6.1 Technical Challenges: The Complexity of Urban Infrastructure 27513.6.2 Data Privacy and Security: Safeguarding Personal Information 27513.6.3 Job Displacement: The Employment Impact 27613.6.4 Accessibility and Inclusivity: Getting AI to Work for All 27613.7 Future Trends in AI for Urban Transport 27713.7.1 The Rise of AVs 27713.7.2 Intelligent Transportation Systems (ITS) 27713.7.3 Shared Mobility and Micro-mobility Solutions 27813.7.4 Data-driven Urban Planning 27813.7.5 AI in Sustainability and Green Mobility 27813.7.6 The Growing Role of AI in Safety and Security 27913.7.7 The Road Ahead 27913.8 Conclusion 280References 28014 Transforming Smart Mobility: L4S and NaaS APIs for Real-time Traffic Management and Autonomous Transport 283L. Ameer Shohail14.1 Introduction 28314.2 Architectural Foundation for Real-time and Autonomous Mobility 28414.2.1 Role of Programmable Interfaces in Transport Systems 28414.2.2 5G Core Components Supporting Application-level Control 28514.2.2.1 Network Exposure Function (NEF) 28514.2.2.2 Policy Control Function (PCF) 28614.2.2.3 User Plane Function (UPF) 28614.2.3 Enabling Low-latency Handling with L4S 28714.2.3.1 Dual Queue Architecture 28714.2.3.2 API-driven Activation of L4S 28714.2.4 Low Latency Queuing with L4S 28714.3 Current Directions in Programmable Transport Networks and LatencyControl 28814.3.1 Evolution of Network Exposure in Transport Systems 28914.3.2 Advancements in Policy Control and Flow Enforcement 28914.3.3 L4S in Context-aware Transport Scenarios 29014.3.4 Research Gaps and Architectural Integration 29114.4 System Design and Implementation Strategy for Real-time Mobility Control 29114.4.1 Simulation Architecture and Environment Setup 29214.4.2 API Workflow and Service Control Logic 29214.4.3 Flow Classification and L4S Enforcement 29314.4.4 Charging and Policy Enforcement Integration 29314.5 Results from Real-time Policy and Queue Enforcement 29314.5.1 Traffic Flow Differentiation and Queue Selection 29314.5.2 Latency and Queue Performance Under Load 29414.5.3 Real-time Charging Trigger Accuracy 29514.5.4 Observations and Summary 29514.6 Reflections on Programmable Responsiveness in Urban Mobility 29614.6.1 Aligning Network Responsiveness with Transport System Needs 29714.6.2 Relevance to Sustainable and AI-driven Urban Mobility 29714.6.3 Operational and Deployment Considerations 29714.6.4 Contribution to the Field and Future Integration Potential 29814.7 Conclusion 298Acknowledgments 299Nomenclature 299Abbreviations 299References 30015 AI-driven Public Transportation: Enhancing Efficiency, Sustainability, and User Experience 301M. Robinson Joel15.1 Introduction 30115.2 Existing AI Uses in Public Transportation 30415.3 Recognizing AI’s Significance in Transportation 30515.4 AI Applications in Transportation: Exemplary Instances 30715.5 Traffic Management Systems Using AI 30915.6 Top AI Resources for Public Transportation 31015.6.1 Moovit App 31115.6.2 Citymapper 31115.6.3 Uber Movement 31215.6.4 Trapeze Group 31415.6.5 Trainline 31515.6.6 Transit 31615.6.7 Waze for Cities 31715.6.8 BusIt 31715.7 AI Improve Public Transportation Efficiency 31815.8 Safety Benefits of AI in Public Transportation 31915.9 Flowchart for GPS-based Vehicle Tracking 32115.10 Build Your Own ESP32 GPS Tracker with Live Tracking 32415.11 Market Share of AI in Transportation by Different Elements 32615.11.1 Analysis Based on Component 32615.11.2 Analysis Based on Technology 32715.11.3 Analysis Based on Application 32715.11.4 Analysis Regional Wise 32915.11.5 Analysis of Key Players 33015.12 Related Work 33115.13 Conclusion 336References 33716 Cognitive AI for Adaptive and Resilient Urban Transportation: A Data-driven Approach to Sustainable Mobility 343Vishal Jain, Archan Mitra, and Sanchita Paul16.1 Introduction 34316.1.1 Contextual Overview of Urban Mobility Challenges in the Era of SmartCities 34316.1.2 Limitations of Traditional and Rule-based AI Systems in Transport 34416.1.3 Emergence and Importance of Cognitive AI in Solving Complex Transport Dynamics 34416.1.4 Problem Statement 34516.1.5 Research Objectives 34516.1.6 Significance of the Study 34616.2 Conceptual Framework and Literature Review 34616.2.1 Understanding Cognitive Artificial Intelligence in the Urban MobilityContext 34616.2.2 Advanced Applications of Cognitive AI in Traffic Management 34716.2.3 AI-enhanced Public Transit Systems and Passenger Experience 34716.2.4 Predictive Maintenance and Asset Management Using AI 34816.2.5 Role of Cognitive AI in Autonomous Vehicle Integration 34816.2.6 Sustainable Transport Solutions Powered by AI 34816.2.7 Theoretical Constructs: Adaptation, Resilience, and Data-driven Urbanism 34916.2.8 Research Gaps and Future Directions 34916.3 Methodology 35016.3.1 Research Design 35016.3.2 Data Sources and Collection 35016.3.3 Tools and Platforms 35116.3.4 Analytical Approaches 35116.4 Integrated Cognitive AI Framework for Urban Transportation 35216.4.1 Description of the Proposed Architecture 35316.4.1.1 Data Input Layer 35316.4.1.2 Cognitive AI Processing Core 35316.4.1.3 Feedback and Adaptation Loop 35316.4.2 Key Modules of the Framework 35416.4.2.1 Dynamic Traffic Control 35416.4.2.2 Predictive Public Transport Scheduling 35416.4.2.3 Autonomous Vehicle Coordination 35416.5 Data Analysis and Empirical Findings 35516.5.1 Dynamic Traffic Control 35516.5.2 Predictive Public Transport Scheduling 35616.5.3 Autonomous Vehicle Coordination 35616.5.4 Carbon Emission Tracking 35716.5.5 AI-led Predictive Maintenance 35716.6 Discussion 35816.6.1 Integrating Real-time Decision-making with Adaptive SystemDesign 35816.6.2 Predictive Scheduling as a Catalyst for Public Transit Optimization 35916.6.3 Multimodal and Cooperative AV Ecosystems 35916.6.4 Environmental Sustainability Through Data-driven Carbon Reduction 35916.6.5 Resilient Infrastructure Through Predictive Maintenance 36016.6.6 Socio-technical and Governance Challenges 36016.6.7 Pathways for Future Research and Policy Integration 36116.7 Conclusion 361References 36217 Optimizing Urban Traffic with Graph Analytics: A Case Study of a Metropolitan Transportation Network 367S. Rakshika and Sudeepa Roy Dey17.1 Introduction 36717.2 Related Work 37017.3 Types of Routing Algorithm 37217.4 Work 375References 38418 Urban Mobility Reimagined: AMRUT Interventions and the 2041 Outlook 387S. Thangapriya, Nancy Jasmine Goldena, T. S. Vasughi, M. Kannan, and Barath Ramesh18.1 Introduction 38718.2 Geospatial Mapping of Tirunelveli Using Advanced Technologies 38818.3 Identifying Research Gaps in Tirunelveli for Sustainable Regional Development 38918.3.1 Topography 38918.4 Climate and Rainfall 39118.5 Precipitation 39218.6 Soil Type Analysis and Resource-efficient Agricultural Planning in Tirunelveli Region 39318.7 Geomorphology 39418.8 A Road map for Tirunelveli’s Future Economy 39718.9 AI-based Urban Housing Analytics and Slum Rehabilitation Forecasting for Tirunelveli LPA 39818.10 Tirunelveli 2041 as a Sustainable Growth Use Case 39918.11 Conclusion 403References 40419 GIS-based Analysis of Road Accidents: A Case Study on Hotspot Identification and Safety Improvement 40719.1 Introduction 40719.2 Methodology 40819.3 Results and Discussion 40819.4 Data Collection and Preparation 40819.5 Analysis 41219.6 Identifying Blackspots Using GIS 41419.7 Key Insights 41619.8 Conclusion 41719.9 Recommendations 41719.10 Way Forward 417References 418Index 421