EDITED BY Asier Perallos, Unai Hernandez-Jayo, Enrique Onieva Deusto Institute of Technology (DeustoTech) – University of Deusto, SpainIgnacio García-ZuazolaLoughborough University, UK

• About the Editors xvList of Contributors xviiForeword xxiiiAcknowledgements xxxiiPart 1 Intelligent Transportation Systems 11 Reference ITS Architectures in Europe 3Begoña Molinete, Sergio Campos, Ignacio (Iñaki) Olabarrieta and Ana Isabel Torre1.1 Introduction 31.2 FRAME: The European ITS Framework Architecture 31.2.1 Background 41.2.2 Scope 51.2.3 Methodology and Content 61.3 Cooperative Systems and Their Impact on the European ITS Architecture Definition 71.3.1 Research Projects and Initiatives 71.3.2 Pilots and Field Operational Tests 81.3.3 European Policy and Standardization Framework 91.3.4 Impact on FRAME Architecture 91.4 Experiences in ITS Architecture Design 101.4.1 Cybercars‐2: Architecture Design for a Cooperative Cybernetics Transport System 101.4.2 MoveUs Cloud‐Based Platform Architecture 13References 172 Architecture Reference of ITS in the USA 18Clifford D. Heise2.1 Introduction 182.2 National ITS Architecture in the USA 192.3 Origins of ITS Architecture in the USA 192.4 US National ITS Architecture Definition 202.4.1 The Development Process 202.4.2 User Services 222.4.3 Logical Architecture 222.4.4 Physical Architecture 232.4.5 Services 252.4.6 Standards Mapping 252.5 Impact on ITS Development in USA 262.5.1 Architecture and Standards Regulation 272.5.2 ITS Planning 282.5.3 ITS Project Development 292.5.4 Tools 322.6 Evolution of the National ITS Architecture 34References 35Part 2 Wireless Vehicular Communications 373 Wireless Communications in Vehicular Environments 39Pekka Eloranta and Timo Sukuvaara3.1 Background and History of Vehicular Networking 393.2 Vehicular Networking Approaches 463.3 Vehicular Ad‐hoc Networking 483.3.1 Vehicle‐to‐infrastructure Communication 503.3.2 Vehicle‐to‐vehicle Communication 513.3.3 Combined Vehicle‐to‐vehicle and Vehicle‐to‐infrastructure Communication 523.3.4 Hybrid Vehicular Network 533.3.5 LTE and Liquid Applications 54References 554 The Case for Wireless Vehicular Communications Supported by Roadside Infrastructure 57Tiago Meireles, José Fonseca and Joaquim Ferreira4.1 Introduction 574.1.1 Rationale for Infrastructure‐based Vehicle Communications for Safety Applications 594.2 MAC Solutions for Safety Applications in Vehicular Communications 614.2.1 Infrastructure‐based Collision‐free MAC Protocols 634.2.2 RT‐WiFi – TDMA Layer 654.2.3 Vehicular Deterministic Access (VDA) 654.2.4 Self‐organizing TDMA (STDMA) 664.2.5 MS‐Aloha 664.3 Vehicular Flexible Time‐triggered Protocol 684.3.1 Model for RSU Deployment in Motorways 684.3.2 RSU Infrastructure Window (IW) 694.3.3 V‐FTT Protocol Overview 714.3.4 Synchronous OBU Window (SOW) 744.4 V‐FTT Protocol Details 754.4.1 Trigger Message Size 754.4.2 Synchronous OBU Window Length (lsow) 774.4.3 V‐FTT Protocol Using IEEE 802.11p/WAVE / ITS G‐5 784.5 Conclusions 80References 815 Cyber Security Risk Analysis for Intelligent Transport Systems and In‐vehicle Networks 83Alastair R. Ruddle and David D. Ward5.1 Introduction 835.2 Automotive Cyber Security Vulnerabilities 845.2.1 Information Security 855.2.2 Electromagnetic Vulnerabilities 855.3 Standards and Guidelines 865.3.1 Risk Analysis Concepts 865.3.2 Functional Safety Standards 875.3.3 IT Security Standards 875.3.4 Combining Safety and Security Analysis 885.4 Threat Identification 885.4.1 Use Cases 885.4.2 Security Actors 895.4.3 Dark‐side Scenarios and Attack Trees 905.4.4 Identifying Security Requirements 935.5 Unified Analysis of Security and Safety Risks 935.5.1 Severity Classification 935.5.2 Probability Classification 955.5.3 Controllability Classification 955.5.4 Risk Classification 955.5.5 Evaluating Risk from Attack Trees 975.5.6 Prioritizing Security Functional Requirements 1005.5.7 Security Assurance and Safety Integrity Requirements 1015.6 Cyber Security Risk Management 1025.7 Conclusions 103Acknowledgements 104References 1046 Vehicle Interaction with Electromagnetic Fields and Implications for Intelligent Transport Systems (ITS) Development 107Lester Low and Alastair R. Ruddle6.1 Introduction 1076.2 In‐vehicle EM Field Investigation and Channel Characterization 1096.3 Field Simulation Tools and Techniques 1126.4 In‐vehicle EM Field Measurement 1166.5 Simulation of Field Distribution and Antenna Placement Optimization 1186.6 Occupant Field Exposure and Possible Field Mitigation Methods 1226.6.1 Human Exposure to Electromagnetic Fields 1226.6.2 Field Mitigation Methods 1256.7 Conclusions 127Acknowledgements 128References 1287 Novel In‐car Integrated and Roof‐mounted Antennas 131Rus Leelaratne†7.1 Introduction 1317.2 Antennas for Broadcast Radio 1327.2.1 Roof‐mounted Radio Antennas 1327.2.2 Hidden Glass Antennas 1347.2.3 Hidden and Integrated Antennas 1367.3 Antennas for Telematics 1377.3.1 Roof‐mounted Telematics Antennas 1377.3.2 Hidden Telematics Antennas 1407.3.3 Future Trend of Telematics Antennas 1417.4 Antennas for Intelligent Transportation Systems 1417.4.1 Car2Car Communication Antennas 1417.4.2 Emergency Call (E‐Call) Antennas 1437.4.3 Other ITS Antennas 1447.5 Intelligent and Smart Antennas 1457.5.1 Intelligent Antenna for Broadcast Radio 1457.5.2 Intelligent Antenna for GNSS 1467.6 Conclusions 147References 147Part 3 Sensors Networks and Surveillance at ITS 1498 Middleware Solution to Support ITS Services in IoT‐based Visual Sensor Networks 151Matteo Petracca, Claudio Salvadori, Andrea Azzarà, Daniele Alessandrelli,Stefano Bocchino, Luca Maggiani and Paolo Pagano8.1 Introduction 1518.2 Visual Sensor Networks and IoT Protocols 1538.2.1 Visual Sensor Networks 1538.2.2 Internet of Things 1568.3 Proposed Middleware Architecture for IoT‐based VSNs 1588.3.1 RESTful Web Service 1598.3.2 Configuration Manager 1608.3.3 Resource Processing Engine 1608.4 Middleware Instantiation for the Parking Lot Monitoring Use Case 1618.4.1 Use Case Scenario, Exposed Resources and Their Interaction 1618.4.2 Middleware Implementation 1638.5 Conclusions 164References 1659 Smart Cameras for ITS in Urban Environment 167Massimo Magrini, Davide Moroni, Gabriele Pieri and Ovidio Salvetti9.1 Introduction 1679.2 Applications to Urban Scenarios 1699.3 Embedded Vision Nodes 1719.3.1 Features of Available Vision Nodes 1729.3.2 Computer Vision on Embedded Nodes 1739.4 Implementation of Computer Vision Logics on Embedded Systems for ITS 1759.4.1 Traffic Status and Level of Service 1759.4.2 Parking Monitoring 1789.5 Sensor Node Prototype 1809.5.1 The Vision Board 1819.5.2 The Networking Board 1829.5.3 The Sensor 1829.5.4 Energy Harvesting and Housing 1829.5.5 The Board Layout 1839.6 Application Scenarios and Experimental Results 1849.7 Conclusions 185References 187Part 4 Data Processing Techniques at ITS 18910 Congestion Prediction by Means of Fuzzy Logic and Genetic Algorithms 191Xiao Zhang, Enrique Onieva, Victor C.S. Lee and Kai Liu10.1 Introduction 19110.2 Hierarchical Fuzzy Rule‐based System (HFRBS) 19310.3 Genetic Hierarchical Fuzzy Rule‐based System (GHFRBS) 19410.3.1 Triple Coding Scheme 19410.3.2 Genetic Operators 19610.3.3 Chromosome Evaluation 19710.3.4 Mechanism and Characteristics of the Algorithm Framework 19710.4 Dataset Configuration and Simplification 19710.5 Experimentation 19910.5.1 Experimental Setup 19910.5.2 Results 19910.5.3 Analysis of the Results 20110.6 Conclusions 202Acknowledgment 203References 20311 Vehicle Control in ADAS Applications: State of the Art 206Joshué Pérez, David Gonzalez and Vicente Milanés11.1 Introduction 20611.2 Vehicle Control in ADAS Application 20611.3 Control Levels 20711.4 Some Previous Works 20811.5 Key Factor for Vehicle Control in the Market 21011.6 ADAS Application From a Control Perspective 21111.6.1 Lane Change Assistant Systems 21211.6.2 Pedestrian Safety Systems 21211.6.3 Forward‐looking Systems 21311.6.4 Adaptive Light Control 21311.6.5 Park Assistant 21411.6.6 Night Vision Systems 21511.6.7 Cruise Control System 21511.6.8 Traffic Sign and Traffic Light Recognition 21511.6.9 Map Supported Systems 21611.6.10 Vehicle Interior Observation 21711.7 Conclusions 217References 21812 Review of Legal Aspects Relating to Advanced Driver Assistance Systems 220Alastair R. Ruddle and Lester Low12.1 Introduction 22012.2 Vehicle Type Approval 22112.3 Trends in Vehicle Automation 22312.3.1 EU Policy 22312.3.2 Brake Assist Systems 22312.3.3 Advanced Vehicle Systems 22512.3.4 Advanced Driving Assistance Systems 22612.3.5 Categorization of Vehicle Automation Levels 22712.4 Vienna Convention on Road Traffic 22712.4.1 Implications for Driving Assistance Systems 23012.4.2 Proposed Amendments 23112.4.3 Implications for Autonomous Driving 23312.5 Liability Issues 23412.5.1 Identifying Responsibilities 23412.5.2 Event Data Recorders 23612.6 Best Practice for Complex Systems Development 23712.6.1 Safety Case 23812.6.2 Safety Development Processes 23912.6.3 ECWVTA Requirements 24012.6.4 Cyber Security Issues 24112.7 Conclusions 242Acknowledgements 243References 243Part 5 Applications and Services for Users and Traffic Managers 24713 Traffic Management Systems 249António Amador, Rui Dias, Tiago Dias and Tomé Canas13.1 Introduction 24913.1.1 Objectives 24913.1.2 Traffic Management 25013.1.3 Traffic Environments 25113.2 Traffic Management Framework 25313.2.1 Inputs 25513.2.2 Analysis 26013.2.3 Outputs 26513.3 Key Stakeholders 26613.4 Traffic Management Centres 26613.4.1 Scope 26713.4.2 Operation Platforms 26813.5 Conclusions 270References 27114 The Use of Cooperative ITS in Urban Traffic Management 272Sadko Mandžuka, Edouard Ivanjko, Miroslav Vujić, Pero Škorputand Martin Gregurić14.1 Introduction 27214.2 Cooperative Ramp Metering 27414.2.1 Ramp Metering 27514.2.2 Cooperation between Local Ramp Meters 27714.2.3 Cooperation between Ramp Metering and Other Traffic Management Systems 27814.3 Incident Management in Urban Areas 28014.4 Public Transport Cooperative Priorities 28414.5 Conclusions 287Acknowledgment 287References 28815 Methodology for an Intelligent in‐Car Traffic Information Management System 289Nerea Aguiriano, Alfonso Brazalez and Luis Matey15.1 Introduction 28915.2 Validation Framework 29115.3 HMI Design Methodology 29215.3.1 Signal Model 29515.3.2 Interpretation Model 29615.3.3 Representation Model 30215.4 Case Study 30515.4.1 Signal Model for Received Messages 30515.4.2 Interpretation Model 30615.4.3 Representation Model 31015.5 Conclusions 311References 31116 New Approaches in User Services Development for Multimodal Trip Planning 313Asier Moreno, Itziar Salaberria and Diego Lopez‐de‐Ipiña16.1 Introduction 31316.1.1 Multimodal Transport 31416.1.2 Travel User Services 31516.2 Travel Planning Information Systems 31616.2.1 Standard Travel Planning Services 31616.2.2 Transit Information Formats and Standards 31916.2.3 New Trends in Transit Information 32016.3 Integrating Linked Open Data for Multimodal Transportation 32116.3.1 Related Work 32316.3.2 Management and Provision of Multimodal Transport Semantic Information 32416.4 Conclusions 328References 329Index 331

EDITED BY Asier Perallos, Unai Hernandez-Jayo, Enrique Onieva Deusto Institute of Technology (DeustoTech) – University of Deusto, SpainIgnacio García-ZuazolaLoughborough University, UK

• About the Editors xvList of Contributors xviiForeword xxiiiAcknowledgements xxxiiPart 1 Intelligent Transportation Systems 11 Reference ITS Architectures in Europe 3Begoña Molinete, Sergio Campos, Ignacio (Iñaki) Olabarrieta and Ana Isabel Torre1.1 Introduction 31.2 FRAME: The European ITS Framework Architecture 31.2.1 Background 41.2.2 Scope 51.2.3 Methodology and Content 61.3 Cooperative Systems and Their Impact on the European ITS Architecture Definition 71.3.1 Research Projects and Initiatives 71.3.2 Pilots and Field Operational Tests 81.3.3 European Policy and Standardization Framework 91.3.4 Impact on FRAME Architecture 91.4 Experiences in ITS Architecture Design 101.4.1 Cybercars‐2: Architecture Design for a Cooperative Cybernetics Transport System 101.4.2 MoveUs Cloud‐Based Platform Architecture 13References 172 Architecture Reference of ITS in the USA 18Clifford D. Heise2.1 Introduction 182.2 National ITS Architecture in the USA 192.3 Origins of ITS Architecture in the USA 192.4 US National ITS Architecture Definition 202.4.1 The Development Process 202.4.2 User Services 222.4.3 Logical Architecture 222.4.4 Physical Architecture 232.4.5 Services 252.4.6 Standards Mapping 252.5 Impact on ITS Development in USA 262.5.1 Architecture and Standards Regulation 272.5.2 ITS Planning 282.5.3 ITS Project Development 292.5.4 Tools 322.6 Evolution of the National ITS Architecture 34References 35Part 2 Wireless Vehicular Communications 373 Wireless Communications in Vehicular Environments 39Pekka Eloranta and Timo Sukuvaara3.1 Background and History of Vehicular Networking 393.2 Vehicular Networking Approaches 463.3 Vehicular Ad‐hoc Networking 483.3.1 Vehicle‐to‐infrastructure Communication 503.3.2 Vehicle‐to‐vehicle Communication 513.3.3 Combined Vehicle‐to‐vehicle and Vehicle‐to‐infrastructure Communication 523.3.4 Hybrid Vehicular Network 533.3.5 LTE and Liquid Applications 54References 554 The Case for Wireless Vehicular Communications Supported by Roadside Infrastructure 57Tiago Meireles, José Fonseca and Joaquim Ferreira4.1 Introduction 574.1.1 Rationale for Infrastructure‐based Vehicle Communications for Safety Applications 594.2 MAC Solutions for Safety Applications in Vehicular Communications 614.2.1 Infrastructure‐based Collision‐free MAC Protocols 634.2.2 RT‐WiFi – TDMA Layer 654.2.3 Vehicular Deterministic Access (VDA) 654.2.4 Self‐organizing TDMA (STDMA) 664.2.5 MS‐Aloha 664.3 Vehicular Flexible Time‐triggered Protocol 684.3.1 Model for RSU Deployment in Motorways 684.3.2 RSU Infrastructure Window (IW) 694.3.3 V‐FTT Protocol Overview 714.3.4 Synchronous OBU Window (SOW) 744.4 V‐FTT Protocol Details 754.4.1 Trigger Message Size 754.4.2 Synchronous OBU Window Length (lsow) 774.4.3 V‐FTT Protocol Using IEEE 802.11p/WAVE / ITS G‐5 784.5 Conclusions 80References 815 Cyber Security Risk Analysis for Intelligent Transport Systems and In‐vehicle Networks 83Alastair R. Ruddle and David D. Ward5.1 Introduction 835.2 Automotive Cyber Security Vulnerabilities 845.2.1 Information Security 855.2.2 Electromagnetic Vulnerabilities 855.3 Standards and Guidelines 865.3.1 Risk Analysis Concepts 865.3.2 Functional Safety Standards 875.3.3 IT Security Standards 875.3.4 Combining Safety and Security Analysis 885.4 Threat Identification 885.4.1 Use Cases 885.4.2 Security Actors 895.4.3 Dark‐side Scenarios and Attack Trees 905.4.4 Identifying Security Requirements 935.5 Unified Analysis of Security and Safety Risks 935.5.1 Severity Classification 935.5.2 Probability Classification 955.5.3 Controllability Classification 955.5.4 Risk Classification 955.5.5 Evaluating Risk from Attack Trees 975.5.6 Prioritizing Security Functional Requirements 1005.5.7 Security Assurance and Safety Integrity Requirements 1015.6 Cyber Security Risk Management 1025.7 Conclusions 103Acknowledgements 104References 1046 Vehicle Interaction with Electromagnetic Fields and Implications for Intelligent Transport Systems (ITS) Development 107Lester Low and Alastair R. Ruddle6.1 Introduction 1076.2 In‐vehicle EM Field Investigation and Channel Characterization 1096.3 Field Simulation Tools and Techniques 1126.4 In‐vehicle EM Field Measurement 1166.5 Simulation of Field Distribution and Antenna Placement Optimization 1186.6 Occupant Field Exposure and Possible Field Mitigation Methods 1226.6.1 Human Exposure to Electromagnetic Fields 1226.6.2 Field Mitigation Methods 1256.7 Conclusions 127Acknowledgements 128References 1287 Novel In‐car Integrated and Roof‐mounted Antennas 131Rus Leelaratne†7.1 Introduction 1317.2 Antennas for Broadcast Radio 1327.2.1 Roof‐mounted Radio Antennas 1327.2.2 Hidden Glass Antennas 1347.2.3 Hidden and Integrated Antennas 1367.3 Antennas for Telematics 1377.3.1 Roof‐mounted Telematics Antennas 1377.3.2 Hidden Telematics Antennas 1407.3.3 Future Trend of Telematics Antennas 1417.4 Antennas for Intelligent Transportation Systems 1417.4.1 Car2Car Communication Antennas 1417.4.2 Emergency Call (E‐Call) Antennas 1437.4.3 Other ITS Antennas 1447.5 Intelligent and Smart Antennas 1457.5.1 Intelligent Antenna for Broadcast Radio 1457.5.2 Intelligent Antenna for GNSS 1467.6 Conclusions 147References 147Part 3 Sensors Networks and Surveillance at ITS 1498 Middleware Solution to Support ITS Services in IoT‐based Visual Sensor Networks 151Matteo Petracca, Claudio Salvadori, Andrea Azzarà, Daniele Alessandrelli,Stefano Bocchino, Luca Maggiani and Paolo Pagano8.1 Introduction 1518.2 Visual Sensor Networks and IoT Protocols 1538.2.1 Visual Sensor Networks 1538.2.2 Internet of Things 1568.3 Proposed Middleware Architecture for IoT‐based VSNs 1588.3.1 RESTful Web Service 1598.3.2 Configuration Manager 1608.3.3 Resource Processing Engine 1608.4 Middleware Instantiation for the Parking Lot Monitoring Use Case 1618.4.1 Use Case Scenario, Exposed Resources and Their Interaction 1618.4.2 Middleware Implementation 1638.5 Conclusions 164References 1659 Smart Cameras for ITS in Urban Environment 167Massimo Magrini, Davide Moroni, Gabriele Pieri and Ovidio Salvetti9.1 Introduction 1679.2 Applications to Urban Scenarios 1699.3 Embedded Vision Nodes 1719.3.1 Features of Available Vision Nodes 1729.3.2 Computer Vision on Embedded Nodes 1739.4 Implementation of Computer Vision Logics on Embedded Systems for ITS 1759.4.1 Traffic Status and Level of Service 1759.4.2 Parking Monitoring 1789.5 Sensor Node Prototype 1809.5.1 The Vision Board 1819.5.2 The Networking Board 1829.5.3 The Sensor 1829.5.4 Energy Harvesting and Housing 1829.5.5 The Board Layout 1839.6 Application Scenarios and Experimental Results 1849.7 Conclusions 185References 187Part 4 Data Processing Techniques at ITS 18910 Congestion Prediction by Means of Fuzzy Logic and Genetic Algorithms 191Xiao Zhang, Enrique Onieva, Victor C.S. Lee and Kai Liu10.1 Introduction 19110.2 Hierarchical Fuzzy Rule‐based System (HFRBS) 19310.3 Genetic Hierarchical Fuzzy Rule‐based System (GHFRBS) 19410.3.1 Triple Coding Scheme 19410.3.2 Genetic Operators 19610.3.3 Chromosome Evaluation 19710.3.4 Mechanism and Characteristics of the Algorithm Framework 19710.4 Dataset Configuration and Simplification 19710.5 Experimentation 19910.5.1 Experimental Setup 19910.5.2 Results 19910.5.3 Analysis of the Results 20110.6 Conclusions 202Acknowledgment 203References 20311 Vehicle Control in ADAS Applications: State of the Art 206Joshué Pérez, David Gonzalez and Vicente Milanés11.1 Introduction 20611.2 Vehicle Control in ADAS Application 20611.3 Control Levels 20711.4 Some Previous Works 20811.5 Key Factor for Vehicle Control in the Market 21011.6 ADAS Application From a Control Perspective 21111.6.1 Lane Change Assistant Systems 21211.6.2 Pedestrian Safety Systems 21211.6.3 Forward‐looking Systems 21311.6.4 Adaptive Light Control 21311.6.5 Park Assistant 21411.6.6 Night Vision Systems 21511.6.7 Cruise Control System 21511.6.8 Traffic Sign and Traffic Light Recognition 21511.6.9 Map Supported Systems 21611.6.10 Vehicle Interior Observation 21711.7 Conclusions 217References 21812 Review of Legal Aspects Relating to Advanced Driver Assistance Systems 220Alastair R. Ruddle and Lester Low12.1 Introduction 22012.2 Vehicle Type Approval 22112.3 Trends in Vehicle Automation 22312.3.1 EU Policy 22312.3.2 Brake Assist Systems 22312.3.3 Advanced Vehicle Systems 22512.3.4 Advanced Driving Assistance Systems 22612.3.5 Categorization of Vehicle Automation Levels 22712.4 Vienna Convention on Road Traffic 22712.4.1 Implications for Driving Assistance Systems 23012.4.2 Proposed Amendments 23112.4.3 Implications for Autonomous Driving 23312.5 Liability Issues 23412.5.1 Identifying Responsibilities 23412.5.2 Event Data Recorders 23612.6 Best Practice for Complex Systems Development 23712.6.1 Safety Case 23812.6.2 Safety Development Processes 23912.6.3 ECWVTA Requirements 24012.6.4 Cyber Security Issues 24112.7 Conclusions 242Acknowledgements 243References 243Part 5 Applications and Services for Users and Traffic Managers 24713 Traffic Management Systems 249António Amador, Rui Dias, Tiago Dias and Tomé Canas13.1 Introduction 24913.1.1 Objectives 24913.1.2 Traffic Management 25013.1.3 Traffic Environments 25113.2 Traffic Management Framework 25313.2.1 Inputs 25513.2.2 Analysis 26013.2.3 Outputs 26513.3 Key Stakeholders 26613.4 Traffic Management Centres 26613.4.1 Scope 26713.4.2 Operation Platforms 26813.5 Conclusions 270References 27114 The Use of Cooperative ITS in Urban Traffic Management 272Sadko Mandžuka, Edouard Ivanjko, Miroslav Vujić, Pero Škorputand Martin Gregurić14.1 Introduction 27214.2 Cooperative Ramp Metering 27414.2.1 Ramp Metering 27514.2.2 Cooperation between Local Ramp Meters 27714.2.3 Cooperation between Ramp Metering and Other Traffic Management Systems 27814.3 Incident Management in Urban Areas 28014.4 Public Transport Cooperative Priorities 28414.5 Conclusions 287Acknowledgment 287References 28815 Methodology for an Intelligent in‐Car Traffic Information Management System 289Nerea Aguiriano, Alfonso Brazalez and Luis Matey15.1 Introduction 28915.2 Validation Framework 29115.3 HMI Design Methodology 29215.3.1 Signal Model 29515.3.2 Interpretation Model 29615.3.3 Representation Model 30215.4 Case Study 30515.4.1 Signal Model for Received Messages 30515.4.2 Interpretation Model 30615.4.3 Representation Model 31015.5 Conclusions 311References 31116 New Approaches in User Services Development for Multimodal Trip Planning 313Asier Moreno, Itziar Salaberria and Diego Lopez‐de‐Ipiña16.1 Introduction 31316.1.1 Multimodal Transport 31416.1.2 Travel User Services 31516.2 Travel Planning Information Systems 31616.2.1 Standard Travel Planning Services 31616.2.2 Transit Information Formats and Standards 31916.2.3 New Trends in Transit Information 32016.3 Integrating Linked Open Data for Multimodal Transportation 32116.3.1 Related Work 32316.3.2 Management and Provision of Multimodal Transport Semantic Information 32416.4 Conclusions 328References 329Index 331

EDITED BY Asier Perallos, Unai Hernandez-Jayo, Enrique Onieva Deusto Institute of Technology (DeustoTech) – University of Deusto, SpainIgnacio García-ZuazolaLoughborough University, UK

• About the Editors xvList of Contributors xviiForeword xxiiiAcknowledgements xxxiiPart 1 Intelligent Transportation Systems 11 Reference ITS Architectures in Europe 3Begoña Molinete, Sergio Campos, Ignacio (Iñaki) Olabarrieta and Ana Isabel Torre1.1 Introduction 31.2 FRAME: The European ITS Framework Architecture 31.2.1 Background 41.2.2 Scope 51.2.3 Methodology and Content 61.3 Cooperative Systems and Their Impact on the European ITS Architecture Definition 71.3.1 Research Projects and Initiatives 71.3.2 Pilots and Field Operational Tests 81.3.3 European Policy and Standardization Framework 91.3.4 Impact on FRAME Architecture 91.4 Experiences in ITS Architecture Design 101.4.1 Cybercars‐2: Architecture Design for a Cooperative Cybernetics Transport System 101.4.2 MoveUs Cloud‐Based Platform Architecture 13References 172 Architecture Reference of ITS in the USA 18Clifford D. Heise2.1 Introduction 182.2 National ITS Architecture in the USA 192.3 Origins of ITS Architecture in the USA 192.4 US National ITS Architecture Definition 202.4.1 The Development Process 202.4.2 User Services 222.4.3 Logical Architecture 222.4.4 Physical Architecture 232.4.5 Services 252.4.6 Standards Mapping 252.5 Impact on ITS Development in USA 262.5.1 Architecture and Standards Regulation 272.5.2 ITS Planning 282.5.3 ITS Project Development 292.5.4 Tools 322.6 Evolution of the National ITS Architecture 34References 35Part 2 Wireless Vehicular Communications 373 Wireless Communications in Vehicular Environments 39Pekka Eloranta and Timo Sukuvaara3.1 Background and History of Vehicular Networking 393.2 Vehicular Networking Approaches 463.3 Vehicular Ad‐hoc Networking 483.3.1 Vehicle‐to‐infrastructure Communication 503.3.2 Vehicle‐to‐vehicle Communication 513.3.3 Combined Vehicle‐to‐vehicle and Vehicle‐to‐infrastructure Communication 523.3.4 Hybrid Vehicular Network 533.3.5 LTE and Liquid Applications 54References 554 The Case for Wireless Vehicular Communications Supported by Roadside Infrastructure 57Tiago Meireles, José Fonseca and Joaquim Ferreira4.1 Introduction 574.1.1 Rationale for Infrastructure‐based Vehicle Communications for Safety Applications 594.2 MAC Solutions for Safety Applications in Vehicular Communications 614.2.1 Infrastructure‐based Collision‐free MAC Protocols 634.2.2 RT‐WiFi – TDMA Layer 654.2.3 Vehicular Deterministic Access (VDA) 654.2.4 Self‐organizing TDMA (STDMA) 664.2.5 MS‐Aloha 664.3 Vehicular Flexible Time‐triggered Protocol 684.3.1 Model for RSU Deployment in Motorways 684.3.2 RSU Infrastructure Window (IW) 694.3.3 V‐FTT Protocol Overview 714.3.4 Synchronous OBU Window (SOW) 744.4 V‐FTT Protocol Details 754.4.1 Trigger Message Size 754.4.2 Synchronous OBU Window Length (lsow) 774.4.3 V‐FTT Protocol Using IEEE 802.11p/WAVE / ITS G‐5 784.5 Conclusions 80References 815 Cyber Security Risk Analysis for Intelligent Transport Systems and In‐vehicle Networks 83Alastair R. Ruddle and David D. Ward5.1 Introduction 835.2 Automotive Cyber Security Vulnerabilities 845.2.1 Information Security 855.2.2 Electromagnetic Vulnerabilities 855.3 Standards and Guidelines 865.3.1 Risk Analysis Concepts 865.3.2 Functional Safety Standards 875.3.3 IT Security Standards 875.3.4 Combining Safety and Security Analysis 885.4 Threat Identification 885.4.1 Use Cases 885.4.2 Security Actors 895.4.3 Dark‐side Scenarios and Attack Trees 905.4.4 Identifying Security Requirements 935.5 Unified Analysis of Security and Safety Risks 935.5.1 Severity Classification 935.5.2 Probability Classification 955.5.3 Controllability Classification 955.5.4 Risk Classification 955.5.5 Evaluating Risk from Attack Trees 975.5.6 Prioritizing Security Functional Requirements 1005.5.7 Security Assurance and Safety Integrity Requirements 1015.6 Cyber Security Risk Management 1025.7 Conclusions 103Acknowledgements 104References 1046 Vehicle Interaction with Electromagnetic Fields and Implications for Intelligent Transport Systems (ITS) Development 107Lester Low and Alastair R. Ruddle6.1 Introduction 1076.2 In‐vehicle EM Field Investigation and Channel Characterization 1096.3 Field Simulation Tools and Techniques 1126.4 In‐vehicle EM Field Measurement 1166.5 Simulation of Field Distribution and Antenna Placement Optimization 1186.6 Occupant Field Exposure and Possible Field Mitigation Methods 1226.6.1 Human Exposure to Electromagnetic Fields 1226.6.2 Field Mitigation Methods 1256.7 Conclusions 127Acknowledgements 128References 1287 Novel In‐car Integrated and Roof‐mounted Antennas 131Rus Leelaratne†7.1 Introduction 1317.2 Antennas for Broadcast Radio 1327.2.1 Roof‐mounted Radio Antennas 1327.2.2 Hidden Glass Antennas 1347.2.3 Hidden and Integrated Antennas 1367.3 Antennas for Telematics 1377.3.1 Roof‐mounted Telematics Antennas 1377.3.2 Hidden Telematics Antennas 1407.3.3 Future Trend of Telematics Antennas 1417.4 Antennas for Intelligent Transportation Systems 1417.4.1 Car2Car Communication Antennas 1417.4.2 Emergency Call (E‐Call) Antennas 1437.4.3 Other ITS Antennas 1447.5 Intelligent and Smart Antennas 1457.5.1 Intelligent Antenna for Broadcast Radio 1457.5.2 Intelligent Antenna for GNSS 1467.6 Conclusions 147References 147Part 3 Sensors Networks and Surveillance at ITS 1498 Middleware Solution to Support ITS Services in IoT‐based Visual Sensor Networks 151Matteo Petracca, Claudio Salvadori, Andrea Azzarà, Daniele Alessandrelli,Stefano Bocchino, Luca Maggiani and Paolo Pagano8.1 Introduction 1518.2 Visual Sensor Networks and IoT Protocols 1538.2.1 Visual Sensor Networks 1538.2.2 Internet of Things 1568.3 Proposed Middleware Architecture for IoT‐based VSNs 1588.3.1 RESTful Web Service 1598.3.2 Configuration Manager 1608.3.3 Resource Processing Engine 1608.4 Middleware Instantiation for the Parking Lot Monitoring Use Case 1618.4.1 Use Case Scenario, Exposed Resources and Their Interaction 1618.4.2 Middleware Implementation 1638.5 Conclusions 164References 1659 Smart Cameras for ITS in Urban Environment 167Massimo Magrini, Davide Moroni, Gabriele Pieri and Ovidio Salvetti9.1 Introduction 1679.2 Applications to Urban Scenarios 1699.3 Embedded Vision Nodes 1719.3.1 Features of Available Vision Nodes 1729.3.2 Computer Vision on Embedded Nodes 1739.4 Implementation of Computer Vision Logics on Embedded Systems for ITS 1759.4.1 Traffic Status and Level of Service 1759.4.2 Parking Monitoring 1789.5 Sensor Node Prototype 1809.5.1 The Vision Board 1819.5.2 The Networking Board 1829.5.3 The Sensor 1829.5.4 Energy Harvesting and Housing 1829.5.5 The Board Layout 1839.6 Application Scenarios and Experimental Results 1849.7 Conclusions 185References 187Part 4 Data Processing Techniques at ITS 18910 Congestion Prediction by Means of Fuzzy Logic and Genetic Algorithms 191Xiao Zhang, Enrique Onieva, Victor C.S. Lee and Kai Liu10.1 Introduction 19110.2 Hierarchical Fuzzy Rule‐based System (HFRBS) 19310.3 Genetic Hierarchical Fuzzy Rule‐based System (GHFRBS) 19410.3.1 Triple Coding Scheme 19410.3.2 Genetic Operators 19610.3.3 Chromosome Evaluation 19710.3.4 Mechanism and Characteristics of the Algorithm Framework 19710.4 Dataset Configuration and Simplification 19710.5 Experimentation 19910.5.1 Experimental Setup 19910.5.2 Results 19910.5.3 Analysis of the Results 20110.6 Conclusions 202Acknowledgment 203References 20311 Vehicle Control in ADAS Applications: State of the Art 206Joshué Pérez, David Gonzalez and Vicente Milanés11.1 Introduction 20611.2 Vehicle Control in ADAS Application 20611.3 Control Levels 20711.4 Some Previous Works 20811.5 Key Factor for Vehicle Control in the Market 21011.6 ADAS Application From a Control Perspective 21111.6.1 Lane Change Assistant Systems 21211.6.2 Pedestrian Safety Systems 21211.6.3 Forward‐looking Systems 21311.6.4 Adaptive Light Control 21311.6.5 Park Assistant 21411.6.6 Night Vision Systems 21511.6.7 Cruise Control System 21511.6.8 Traffic Sign and Traffic Light Recognition 21511.6.9 Map Supported Systems 21611.6.10 Vehicle Interior Observation 21711.7 Conclusions 217References 21812 Review of Legal Aspects Relating to Advanced Driver Assistance Systems 220Alastair R. Ruddle and Lester Low12.1 Introduction 22012.2 Vehicle Type Approval 22112.3 Trends in Vehicle Automation 22312.3.1 EU Policy 22312.3.2 Brake Assist Systems 22312.3.3 Advanced Vehicle Systems 22512.3.4 Advanced Driving Assistance Systems 22612.3.5 Categorization of Vehicle Automation Levels 22712.4 Vienna Convention on Road Traffic 22712.4.1 Implications for Driving Assistance Systems 23012.4.2 Proposed Amendments 23112.4.3 Implications for Autonomous Driving 23312.5 Liability Issues 23412.5.1 Identifying Responsibilities 23412.5.2 Event Data Recorders 23612.6 Best Practice for Complex Systems Development 23712.6.1 Safety Case 23812.6.2 Safety Development Processes 23912.6.3 ECWVTA Requirements 24012.6.4 Cyber Security Issues 24112.7 Conclusions 242Acknowledgements 243References 243Part 5 Applications and Services for Users and Traffic Managers 24713 Traffic Management Systems 249António Amador, Rui Dias, Tiago Dias and Tomé Canas13.1 Introduction 24913.1.1 Objectives 24913.1.2 Traffic Management 25013.1.3 Traffic Environments 25113.2 Traffic Management Framework 25313.2.1 Inputs 25513.2.2 Analysis 26013.2.3 Outputs 26513.3 Key Stakeholders 26613.4 Traffic Management Centres 26613.4.1 Scope 26713.4.2 Operation Platforms 26813.5 Conclusions 270References 27114 The Use of Cooperative ITS in Urban Traffic Management 272Sadko Mandžuka, Edouard Ivanjko, Miroslav Vujić, Pero Škorputand Martin Gregurić14.1 Introduction 27214.2 Cooperative Ramp Metering 27414.2.1 Ramp Metering 27514.2.2 Cooperation between Local Ramp Meters 27714.2.3 Cooperation between Ramp Metering and Other Traffic Management Systems 27814.3 Incident Management in Urban Areas 28014.4 Public Transport Cooperative Priorities 28414.5 Conclusions 287Acknowledgment 287References 28815 Methodology for an Intelligent in‐Car Traffic Information Management System 289Nerea Aguiriano, Alfonso Brazalez and Luis Matey15.1 Introduction 28915.2 Validation Framework 29115.3 HMI Design Methodology 29215.3.1 Signal Model 29515.3.2 Interpretation Model 29615.3.3 Representation Model 30215.4 Case Study 30515.4.1 Signal Model for Received Messages 30515.4.2 Interpretation Model 30615.4.3 Representation Model 31015.5 Conclusions 311References 31116 New Approaches in User Services Development for Multimodal Trip Planning 313Asier Moreno, Itziar Salaberria and Diego Lopez‐de‐Ipiña16.1 Introduction 31316.1.1 Multimodal Transport 31416.1.2 Travel User Services 31516.2 Travel Planning Information Systems 31616.2.1 Standard Travel Planning Services 31616.2.2 Transit Information Formats and Standards 31916.2.3 New Trends in Transit Information 32016.3 Integrating Linked Open Data for Multimodal Transportation 32116.3.1 Related Work 32316.3.2 Management and Provision of Multimodal Transport Semantic Information 32416.4 Conclusions 328References 329Index 331