List of Figures xiii List of Tables xiv Preface xv Acknowledgements xvii List of Abbreviations xviii SECTION 1 Foundations of ITS in Freight Logistics 1 1 The Role of Intelligent Transport Systems in Modern Freight Supply Chains 3 1.1 Introduction 3 1.2 Evolution of Freight and Logistics Systems 4 1.3 Understanding ITS 5 1.4 ITS in Modern Supply Chains 8 1.5 Key Components and Technologies in ITS for Freight 10 1.5.1 Vehicle Telematics and Fleet Monitoring 10 1.
5.2 GNSS, AVL, and Real-Time Tracking 12 1.5.3 V2I and V2V Communication Technologies 12 1.5.4 Digital Freight Platforms and Aggregators 12 1.5.5 Geofencing and Access Management Systems 12 1.
5.6 Smart Parking and Loading Bay Management 13 1.5.7 Electronic Proof of Delivery 13 1.5.8 Urban Freight Control Centres 13 1.5.9 AI-Based Freight Decision Support Systems 13 1.
5.10 Blockchain for Freight Security and Traceability 14 1.6 Integration of ITS with Urban Planning 14 1.7 Global Best Practices in Freight ITS Implementation 15 1.8 Challenges and Barriers to ITS Adoption 15 1.8.1 Infrastructure and Technological Gaps 16 1.8.
2 High Capital and Operational Costs 16 1.8.3 Data Privacy, Security, and Ownership Issues 16 1.8.4 Institutional Fragmentation and Regulatory Delays 16 1.8.5 Behavioural and Skill Barriers 17 1.8.
6 Limited Demonstration Projects and Evidence Base 17 1.9 Summary and Key Takeaways 17 References 18 2 ITS Solutions for Last-Mile Delivery and E-Commerce 22 2.1 Introduction 22 2.2 ITS Applications for Last-Mile Delivery 23 2.3 Digital Technologies Enabling E-Commerce Logistics 24 2.4 Comparative Approaches: E-Commerce vs Q-Commerce ITS Needs 25 2.5 ITS-Integrated Emerging Delivery Models 26 2.5.
1 Micro-Hubs 27 2.5.2 Drone Deliveries 27 2.5.3 Autonomous Delivery Robots 27 2.6 Case Studies of ITS in Last-Mile Freight 29 2.6.1 DHL''s Sensor-Driven Parcel Locker Network, Germany 29 2.
6.2 Yamato Transport''s Dynamic Route Optimization, Japan 30 2.6.3 Flipkart''s AI-Powered Predictive Routing, India 30 2.6.4 Barcelona''s Micro-Distribution Centres with ITS Integration, Spain 31 2.6.5 Autonomous Delivery Robots in Singapore''s Business Districts 31 2.
6.6 Comparative Insights 31 2.7 Challenges in Adoption and Governance 32 2.8 Future Outlook and Innovation Pathways 33 2.9 Summary and Key Takeaways 34 References 35 3 Urban Freight Management with ITS 37 3.1 Introduction 37 3.2 Architecture of Integrated Freight ITS 38 3.3 Data Flow and Interoperability Standards 40 3.
3.1 Structure of Data Flow in Freight ITS 40 3.3.2 Role of Interoperability Frameworks 40 3.3.3 Security and Reliability in Data Exchange 40 3.4 Optimization Models in Freight ITS 41 3.4.
1 Core Vehicle Routing Formulation 41 3.4.2 Time Windows and Service Times 43 3.4.3 Energy Aware or Range-Constrained Routing 44 3.4.4 Fleet Assignment as a Network Flow 44 3.4.
5 Scheduling with Tardiness or Make Span Objectives 44 3.4.6 Demand Forecasting with ARIMA and Count Models 44 3.4.7 Predictive Control with Reinforcement Learning 45 3.4.8 Multi-Objective Cost and Emission Optimization 45 3.5 Decision Support Systems for Freight Planning 45 3.
5.1 Structure and Core Components 46 3.5.2 Multi-Criteria Decision-Making in Freight Planning 46 3.5.3 Integration with Predictive and Prescriptive Analytics 47 3.6 Integration with Urban Mobility and Smart City Systems 49 3.7 Case Studies of Integrated ITS Freight Solutions 50 3.
8 Barriers to Integration and Optimization 53 3.9 Summary and Key Takeaways 54 References 54 SECTION 2 Emerging Technologies in Freight Logistics 59 4 Digital Twins in Freight and Supply Chains 61 4.1 Introduction 61 4.2 Concept and Evolution of Digital Twins 62 4.3 Framework for Digital Twin Implementation in Freight 63 4.3.1 Data Acquisition 63 4.3.
2 Integration Platforms 63 4.3.3 Analytics Engines 63 4.3.4 Visualization Tools 64 4.4 Applications in Freight and Supply Chain Operations 64 4.5 Integration with Other ITS Technologies 66 4.6 Evidence and Models 67 4.
6.1 Case-Based Evidence 67 4.6.2 Conceptual Predictive Freight Flow Framework 68 4.7 Implementation Challenges 69 4.8 Innovation and Future Trajectories 71 4.8.1 Data-Driven Predictive Freight Ecosystems 71 4.
8.2 Autonomous and Semi-Autonomous Freight Operations 71 4.8.3 Integration of Green and Circular Logistics Principles 71 4.8.4 Hyperconnected Multi-Modal Freight Networks 72 4.8.5 Policy, Governance, and Institutional Transformation 73 4.
8.6 Research and Development Priorities 73 4.9 Summary and Key Takeaways 73 References 74 5 Big Data Analytics in Freight ITS 76 5.1 Introduction 76 5.2 Data Sources in Freight ITS 77 5.3 Data Collection, Storage, and Processing Frameworks 81 5.3.1 Data Collection 81 5.
3.2 Data Storage 81 5.3.3 Data Processing 82 5.4 Analytical Techniques in Freight ITS 83 5.4.1 Descriptive Analytics 83 5.4.
2 Predictive Analytics 84 5.4.3 Prescriptive Analytics 84 5.4.4 Comparative Perspective 85 5.5 Big Data-Driven DSS 85 5.6 Applications in Urban Freight and Last-Mile Delivery 87 5.6.
1 Real-Time Traffic Prediction 87 5.6.2 Dynamic Load Balancing and Fleet Reallocation 87 5.6.3 Delivery Time Window Optimization 88 5.6.4 Sustainable and Low-Emission Freight Routing 88 5.6.
5 On-Demand Delivery Orchestration 88 5.6.6 Predictive Maintenance for Urban Fleets 88 5.6.7 Warehouse and Micro-Fulfilment Optimization 89 5.6.8 Crowdshipping and Platform-Based Logistics 89 5.7 Case Studies 89 5.
7.1 Case 1: India Big Data in E-Commerce Fleet Analytics 90 5.7.2 Case 2: Port of Rotterdam Digital Twin 90 5.8 Challenges and Risks 91 5.8.1 Data Privacy and Ownership Concerns 91 5.8.
2 Interoperability Issues in Heterogeneous ITS Systems 91 5.8.3 Infrastructure, Skills, and Cost Barriers 92 5.8.4 Cybersecurity Risks 92 5.8.5 Organizational Resistance and Change Management 92 5.9 Summary and Key Takeaways 92 References 93 6 Blockchain Applications in Freight ITS 97 6.
1 Introduction 97 6.2 Fundamentals of Blockchain Technology for Logistics 99 6.3 Blockchain Applications in Freight ITS 100 6.3.1 Secure Data Exchange 100 6.3.2 End-to-End Visibility and Traceability 100 6.3.
3 Automated Freight Payments and Settlements 100 6.3.4 Digital Identity for Vehicles, Shipments, and Assets 101 6.4 Integration with ITS 101 6.4.1 Blockchain and IoT Integration 101 6.4.2 Interoperability with Big Data Analytics 103 6.
4.3 Blockchain in ITS-Enabled Policy Mechanisms 103 6.4.4 Towards a Unified Freight ITS Ecosystem 103 6.5 Case Studies 104 6.5.1 Case 1: India - TradeLens at Major Ports 104 6.5.
2 Case 2: Germany - DHL''s Pharmaceutical Cold Chain Pilot 105 6.6 Challenges in Adoption 105 6.6.1 Technical Challenges 105 6.6.2 Regulatory Challenges 106 6.6.3 Organizational Challenges 107 6.
7 Future Pathways 107 6.7.1 Blockchain and AI for Predictive Freight 107 6.7.2 Blockchain and IoT for Automated Compliance 108 6.7.3 Decentralized Freight Marketplaces 108 6.7.
4 Policy and Sustainability Dimensions 108 6.8 Summary and Key Takeaways 109 References 110 7 Autonomous Freight Systems and Logistics Automation 113 7.1 Introduction 113 7.2 Evolution of Automation in Freight Logistics 114 7.3 Core Technologies Behind Freight Automation 116 7.3.1 LIDAR 116 7.3.
2 Radar and Ultrasonic Sensors 116 7.3.3 Computer Vision 116 7.3.4 AI/ML 116 7.3.5 V2X Communication 117 7.3.
6 Digital Twins and Simulation 17 7.4 AFVs 117 7.4.1 Long-Haul Autonomous Trucks 119 7.4.2 Middle-Mile and Urban Delivery Vans 119 7.4.3 Last-Mile Delivery Robots and Drones 119 7.
4.4 Regulatory and Safety Considerations 120 7.5 Integration with ITS 120 7.5.1 Real-Time Traffic Coordination 120 7.5.2 Freight Signal Priority 120 7.5.
3 Truck Platooning and Cooperative Driving 120 7.5.4 Connection to Logistics Platforms 121 7.5.5 Data Sharing and Cybersecurity 121 7.6 Robotics in Freight Terminals and Warehouses 121 7.6.1 AGVs and Autonomous Mobile Robots 121 7.
6.2 Drones for Inventory and Delivery 121 7.6.3 Cobots 123 7.6.4 Automated Storage and Retrieval Systems 123 7.6.5 Autonomous Forklifts, Robotic Arms, and Palletizing Robots 123 7.
6.6 Robotic Sortation Systems and Conveyors 123 7.6.7 Swarm Robotics and Digital Twins 123 7.6.8 Vision-Based Picking Robots 124 7.6.9 Exoskeletons (Wearable Robotics) 124 7.
7 Case Studies of Freight Automation 124 7.7.1 U.S. Autonomous Trucking Pilots 124 7.7.2 Warehouse Robotics in Europe and Asia 125 7.8 Challenges and Risks 125 7.
8.1 Technical Limitations and Infrastructure Readiness 126 7.8.2 Cybersecurity and Data Vulnerabilities 126 7.8.3 Legal and Ethical Considerations 126 7.8.4 Workforce Transition and Socio-Economic Impacts 126 7.
8.5 Interoperability and Standardization Issues 127 7.9 Summary and Key Takeaways 127 References 128 SECTION 3 Sustainability and Future Directions 1.