Smart sensing for traffic monitoring / edited by Nobuyuki Ozaki.

Format:

Book

Contributor:

Ozaki, Nobuyuki, editor.

Series:

Transportation Series

Language:

English

Subjects (All):

Traffic monitoring--Technological innovations.

Traffic monitoring.

Intelligent transportation systems.

Physical Description:

1 online resource (266 pages) : illustrations

Edition:

1st ed.

Place of Publication:

London, England : Institution of Engineering & Technology, [2021]

Summary:

Intelligent infrastructure has the potential to revolutionise traffic management, and to play a key role in the future automation of vehicles. The book systematically covers the key elements of intelligent infrastructure for an audience of researchers, practitioners and advanced students.

Contents:

Intro

Title

Copyright

Contents

About the editor

Preface

Part I: Regional activities

1 Japan perspective

1.1 History of intelligent transport system development in Japan

1.2 Infrastructure sensors and driving assistance using V2I

1.2.1 What is an infrastructure sensor?

1.2.2 Events detected by infrastructure sensors

1.2.3 Type of sensors that can be used as infrastructure sensors

1.2.4 Driving assistance using infrastructure sensors

1.3 Expressway case studies

1.3.1 Forward obstacle information provision (Sangubashi Curve, Metropolitan Expressway) [3]

1.3.2 Forward obstacle information provision (Rinkai Fukutoshin Slip Road, Metropolitan Expressway) [6]

1.3.3 Forward obstacle information provision (Akasaka Tunnel, Metropolitan Expressway) [4]

1.3.4 Merging assistance (Tanimachi Junction, Higashi-Ikebukuro Slip Road and so on, Metropolitan Expressway) [8]

1.3.5 Smooth traffic flow assistance at sags (Yamato Sag, Tomei Expressway) [5]

1.4 Case studies on ordinary roads

1.4.1 Rear-end collision prevention system [7]

1.4.2 Crossing collision prevention system [7,11]

1.4.3 Left-turn collision prevention system [7]

1.4.4 Right-turn collision prevention system [7]

1.4.5 Crossing pedestrian recognition enhancement system [7]

1.5 Driving safety assistance using vehicle-to-vehicle (V2V) communication

References

2 European perspective of Cooperative Intelligent Transport Systems

2.1 Introduction

2.2 C-ITS development and deployment in Europe

2.3 European C-ITS platform

2.4 C-Roads initiative

2.5 C-ITS architecture

2.6 C-ITS services and use cases and operational guidelines

2.7 Conclusions

Acknowledgements

Appendix A

3 Singapore perspective: smart mobility

3.1 Introduction

3.2 Challenges and transport strategy.

3.3 Demand management __amp__#8211

a key element of the transport strategy

3.4 Development of intelligent transport systems in Singapore

3.5 Integrating ITS on a common platform

3.6 Road pricing in Singapore

3.6.1 The manually operated Area Licensing Scheme

3.6.2 Road pricing adopts intelligent technologies

3.6.3 Challenges with the ERP system

3.6.4 The next-generation road pricing system

3.7 Big data and analytics for traffic management and travellers

3.7.1 Quality of data and information

3.7.2 Travel information available from ITS in Singapore

3.8 Connected and autonomous vehicles

3.9 Concluding remarks

Part II Traffic state sensing by roadside unit

4 Traffic counting by stereo camera

4.1 Introduction

4.2 General procedure traffic counting using stereo vision

4.2.1 Stereo cameras

4.2.2 Calibration of camera images

4.2.3 Image rectification

4.2.4 Block matching to produce a depth map

4.2.5 Object detection

4.2.6 Object tracking and counting

4.2.7 Installation of stereo camera

4.3 Accurate vehicle counting using roadside stereo camera [14]

4.3.1 System configuration

4.3.2 Depth measurement based on binocular stereo vision

4.3.3 Vehicle detection

4.3.4 Traffic counter

4.3.5 Results

4.4 Summary

5 Vehicle detection at intersections by LIDAR system

5.1 Introduction

5.1.1 New trend

5.1.2 Target applications

5.1.3 Basic principle of LIDAR system

5.1.4 Types of LIDAR system

5.1.5 Performance of LIDAR system

5.1.6 Current deployment status

5.2 Application of vehicle detection by an IHI__amp__#8217

s 3D laser radar

5.2.1 Practical application of a 3D laser radar is close at hand in playing a central role in the Intelligent Transport Systems.

5.2.2 Eyes that tell vehicles the road conditions at a nearby intersection

5.2.3 Instant identification of objects with reflected laser light

5.2.4 Advantage of all-weather capability and fast data processing

5.2.5 Pilot program in Singapore

6 Vehicle detection at intersection by RADAR system

6.1 Background

6.2 High-resolution millimetre-wave radar

6.3 Roadside radar system

6.4 Technical verification under severe weather condition

6.4.1 Objective

6.4.2 Design for heavy rainfall condition

6.4.3 Experiment in snowfall field

6.5 Detection accuracy verification on public road

6.6 Conclusion and discussion

Part III Traffic state sensing by on board unit

7 GNSS-based traffic monitoring

7.1 Introduction

7.2 GNSS probe data

7.3 GNSS probe data attributes

7.4 Historical data

7.5 Probe data processing

7.6 Real-time traffic information

7.7 Example of probe data in use

7.8 Historical traffic services

7.8.1 Traffic speed average

7.8.2 Historical traffic analytics information

7.9 Advanced traffic features

7.10 Split lane traffic

7.11 Wide moving jam (safety messages)

7.12 Automated road closures

7.13 Quality testing

7.14 Ground truth testing

7.15 Probes as ground truth

7.16 Q-Bench

7.17 Conclusion

8 Traffic state monitoring by close coupling logic with OBU and cloud applications

8.1 Introduction

8.2 Smart transport cloud system

8.2.1 Concept

8.2.2 Key technology

8.3 Usage case 1: estimation of traffic volume at highway

8.3.1 System description

8.3.2 Traffic volume estimation

8.4 Usage case 2: estimation of traffic congestion and volume of pedestrian crowds

8.4.1 Benefits from the system

8.4.2 System description

8.4.3 Logic design

8.4.4 Evaluation.

8.4.5 Other possibilities for estimating traffic: finding parked vehicles

8.5 Conclusion

Acknowledgments

Part IV Detection and counting of vulnerable road users

9 Monitoring cycle traffic: detection and counting methods and analytical issues

9.1 Introduction

9.1.1 Importance of monitoring cycle traffic

9.1.2 Nature of cycle traffic

9.2 Current methods of detecting and counting

9.2.1 Overview

9.2.2 Manual classified counts

9.2.3 Surface and subsurface equipment

9.2.4 Above-ground detection

9.3 Procedures, protocols and analysis

9.3.1 Procedures and protocols

9.3.2 Analysis

9.4 Innovations in cycle-counting technology

9.4.1 Harvesting digital crowdsourced data

9.4.2 Issues and a future trajectory

10 Crowd density estimation from a surveillance camera

10.1 Introduction

10.2 Related works

10.3 COUNT forest

10.3.1 Building COUNT forest

10.3.2 Prediction model

10.3.3 Density estimation by COUNT forest

10.4 Robust density estimation

10.4.1 Crowdedness prior

10.4.2 Forest permutation

10.4.3 Semiautomatic training

10.5 Experiments

10.5.1 Counting performance

10.5.2 Robustness

10.5.3 Semiautomatic training

10.5.4 Application 1: traffic count

10.5.5 Application 2: stationary time

10.6 Conclusions

Part V Detecting factors affecting traffic

11 Incident detection

11.1 Introduction

11.2 Incident detection in the context of the incident management process

11.3 Key parameters for incident detection

11.4 Incident detection using traffic-parameter-based technologies and techniques

11.4.1 Flow in vehicles per hour per lane or per direction

11.4.2 Average speed per time interval at a specific location

11.4.3 Average speed over a distance, or journey time, per time interval.

11.4.4 Headway (time) in seconds average per lane per time interval

11.4.5 Detector occupancy

11.5 Sensor technologies

11.5.1 Inductive loops

11.5.2 Fixed-beam RADAR

11.5.3 Computer vision

11.5.4 Journey time measurement using licence plates

11.5.5 Journey time measurement using Bluetooth and Wi-Fi

11.6 Wide-area incident detection techniques

11.6.1 Computer vision

11.6.2 Scanning radar

11.6.3 Use of linear radar

11.6.4 Light detection and ranging

11.6.5 Longitudinal optic fibre

11.6.6 Mobile phone, probe vehicle and connected-autonomous-vehicle-based techniques

11.6.7 Social media and crowd-sourcing techniques

11.7 Comment on incident detection technology

12 Sensing of heavy precipitation__amp__#8212

development of phased-array weather radar

12.1 Introduction

12.2 Background

12.3 Problems

12.4 Phased-array weather radar

12.5 Observations

12.6 Future

Index.

Notes:

Includes index.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

1-83724-662-9

1-78561-775-3

OCLC:

1236261632