Smart Sensing for Traffic Monitoring (Transportation)

253 Pages·2021·33.281 MB·English

by  Nobuyuki Ozaki (editor)

IETTRANSPORTATION SERIES 17
Smart Sensing for Traffic
Monitoring
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Smart Sensing for Traffic
Monitoring
Edited by
Nobuyuki Ozaki
The Institution of Engineering andTechnology
PublishedbyTheInstitutionofEngineeringandTechnology,London,UnitedKingdom
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†TheInstitutionofEngineeringandTechnology2021
Firstpublished2020
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Contents
Abouttheeditor xi
Preface xiii
Part I:Regional activities 1
1 Japanperspective 3
Koichi Sakai
1.1 Historyof intelligent transport system development in Japan 3
1.2 Infrastructure sensorsand driving assistance usingV2I 5
1.2.1 What is an infrastructure sensor? 5
1.2.2 Events detected byinfrastructure sensors 6
1.2.3 Type of sensorsthat can be usedas infrastructure
sensors 8
1.2.4 Driving assistance usinginfrastructure sensors 9
1.3 Expressway case studies 10
1.3.1 Forward obstacle information provision (SangubashiCurve,
Metropolitan Expressway) 10
1.3.2 Forward obstacle information provision (Rinkai Fukutoshin
Slip Road, Metropolitan Expressway) 11
1.3.3 Forward obstacle information provision (Akasaka Tunnel,
Metropolitan Expressway) 12
1.3.4 Merging assistance (Tanimachi Junction, Higashi-Ikebukuro
Slip Road and soon, Metropolitan Expressway) 13
1.3.5 Smooth traffic flowassistance at sags(Yamato Sag,
Tomei Expressway) 14
1.4 Case studies onordinary roads 15
1.4.1 Rear-end collision prevention system 16
1.4.2 Crossingcollision prevention system 16
1.4.3 Left-turn collision prevention system 16
1.4.4 Right-turn collisionprevention system 17
1.4.5 Crossingpedestrian recognition enhancement
system 17
1.5 Driving safety assistance usingvehicle-to-vehicle (V2V)
communication 17
References 19
vi Smart sensingfor traffic monitoring
2 Europeanperspective of Cooperative Intelligent TransportSystems 21
Meng Lu,Robbin Blokpoel and Jacint Castells
2.1 Introduction 21
2.2 C-ITS development and deployment in Europe 22
2.3 European C-ITS platform 23
2.4 C-Roads initiative 23
2.5 C-ITS architecture 26
2.6 C-ITS services and usecases and operational guidelines 33
2.7 Conclusions 33
Acknowledgements 34
Appendix A 35
References 39
3 Singapore perspective: smart mobility 43
Kian-Keong Chin
3.1 Introduction 43
3.2 Challenges and transport strategy 43
3.3 Demand management –a key element of the transport strategy 44
3.4 Development of intelligent transport systems in Singapore 44
3.5 Integrating ITS ona common platform 48
3.6 Road pricing in Singapore 49
3.6.1 The manually operated Area LicensingScheme 49
3.6.2 Road pricing adopts intelligent technologies 50
3.6.3 Challenges with the ERPsystem 52
3.6.4 The next-generation road pricing system 52
3.7 Big data and analytics for traffic management and travellers 55
3.7.1 Quality of data and information 55
3.7.2 Travel information available from ITS inSingapore 56
3.8 Connected and autonomousvehicles 58
3.9 Concluding remarks 59
References 59
Part II:Traffic state sensingbyroadside unit 61
4 Traffic countingbystereo camera 63
ToshioSato
4.1 Introduction 63
4.2 General procedure traffic counting usingstereo vision 64
4.2.1 Stereo cameras 64
4.2.2 Calibration of camera images 66
4.2.3 Image rectification 68
4.2.4 Block matching to produce a depth map 68
4.2.5 Object detection 69
4.2.6 Object tracking and counting 70
4.2.7 Installation of stereo camera 70
Contents vii
4.3 Accurate vehicle counting usingroadside stereo camera 71
4.3.1 System configuration 72
4.3.2 Depth measurement based onbinocular stereo vision 73
4.3.3 Vehicle detection 74
4.3.4 Traffic counter 74
4.3.5 Results 76
4.4 Summary 78
References 78
5 Vehicle detectionat intersectionsbyLIDARsystem 81
Hikaru Ishikawa, YoshihisaYamauchi and Kentaro Mizouchi
5.1 Introduction 81
5.1.1 New trend 81
5.1.2 Target applications 81
5.1.3 Basic principal of LIDAR system 81
5.1.4 Types of LIDAR system 82
5.1.5 Performance of LIDAR system 85
5.1.6 Current deployment status 85
5.2 Application of vehicle detection byan IHI’s3Dlaser radar 85
5.2.1 Practical application of a 3Dlaser radar is close at hand
in playing a central role inthe Intelligent Transport
Systems 85
5.2.2 Eyes that tell vehicles the roadconditions at a nearby
intersection 85
5.2.3 Instant identification of objects with reflectedlaser light 88
5.2.4 Advantage of all-weather capability and fast data
processing 89
5.2.5 Pilot program in Singapore 90
References 95
6 Vehicle detectionat intersection byRADARsystem 97
Yoichi Nakagawa
6.1 Background 97
6.2 High-resolution millimetre-wave radar 98
6.3 Roadside radar system 100
6.4 Technical verification under severe weather condition 103
6.4.1 Objective 103
6.4.2 Design for heavy rainfall condition 103
6.4.3 Experiment in snowfall field 104
6.5 Detection accuracy verification on public road 106
6.6 Conclusion and discussion 110
Acknowledgements 111
References 111
viii Smartsensingfor traffic monitoring
Part III:Traffic state sensingbyonboard unit 113
7 GNSS-basedtraffic monitoring 115
Benjamin Wilson
7.1 Introduction 115
7.2 GNSSprobe data 115
7.3 GNSSprobe data attributes 115
7.4 Historical data 116
7.5 Probe data processing 116
7.6 Real-time traffic information 117
7.7 Example of probe data in use 118
7.8 Historical traffic services 119
7.8.1 Traffic speed average 119
7.8.2 Historical traffic analytics information 119
7.9 Advanced traffic features 120
7.10 Split lane traffic 120
7.11 Wide moving jam (safety messages) 120
7.12 Automated road closures 121
7.13 Quality testing 122
7.14 Ground truth testing 122
7.15 Probesas ground truth 122
7.16 Q-Bench 123
7.17 Conclusion 123
8 Traffic state monitoring byclose coupling logic withOBUand
cloudapplications 125
Nobuyuki Ozaki, Hideki Ueno,ToshioSato, Yoshihiko Suzuki,
Chihiro Nishikata, Hiroshi Sakai and YoshikazuOoba
8.1 Introduction 126
8.2 Smart transport cloud system 127
8.2.1 Concept 127
8.2.2 Key technology 129
8.3 Usage case 1:estimation of traffic volume at highway 131
8.3.1 System description 131
8.3.2 Traffic volume estimation 133
8.4 Usage case 2:estimation of traffic congestion and volume of
pedestrian crowds 135
8.4.1 Benefits from the system 135
8.4.2 System description 137
8.4.3 Logic design 138
8.4.4 Evaluation 144
8.4.5 Other possibilities for estimating traffic: finding parked
vehicles 145
8.5 Conclusion 146
Acknowledgments 147
References 147
Contents ix
Part IV:Detection andcounting of vulnerable road users 149
9 Monitoring cycle traffic: detectionandcounting methods
andanalytical issues 151
John Parkin, Paul Jackson and Andy Cope
9.1 Introduction 151
9.1.1 Importance of monitoring cycle traffic 151
9.1.2 Nature of cycle traffic 153
9.2 Current methodsof detecting and counting 154
9.2.1 Overview 154
9.2.2 Manual classified counts 155
9.2.3 Surface and subsurface equipment 156
9.2.4 Above-grounddetection 159
9.3 Procedures, protocols and analysis 161
9.3.1 Procedures and protocols 161
9.3.2 Analysis 164
9.4 Innovationsin cycle-counting technology 166
9.4.1 Harvesting digital crowdsourced data 166
9.4.2 Issuesand a future trajectory 167
Acknowledgements 168
References 168
10 Crowddensity estimation from a surveillance
camera 171
Viet-Quoc Pham
10.1 Introduction 171
10.2 Related works 173
10.3 COUNT forest 174
10.3.1 BuildingCOUNT forest 175
10.3.2 Prediction model 177
10.3.3 Density estimation by COUNTforest 177
10.4 Robust density estimation 179
10.4.1 Crowdednessprior 179
10.4.2 Forest permutation 180
10.4.3 Semiautomatic training 180
10.5 Experiments 182
10.5.1 Counting performance 182
10.5.2 Robustness 185
10.5.3 Semiautomatic training 185
10.5.4 Application 1: traffic count 186
10.5.5 Application 2: stationary time 188
10.6 Conclusions 189
References 189