Smart sensing for traffic monitoring

Smart sensing for traffic monitoring

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Bibliographic Details
Corporate Author: ProQuest (Firm)
Other Authors: Ozaki, Nobuyuki (Editor)
Format: Electronic eBook
Language:English
Published: Stevenage The Institution of Engineering and Technology 2020
Series:IET transportation series ; 17.
Subjects:
Intelligent transportation systems.
Traffic monitoring > Technological innovations.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)

MARC

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020 |a 9781785617751  |q (electronic bk.) 
020 |z 9781785617744 
020 |z 1785617745 
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245 0 0 |a Smart sensing for traffic monitoring  |c edited by Dr. Nobuyuki Ozaki 
264 1 |a Stevenage  |b The Institution of Engineering and Technology  |c 2020 
264 4 |c ©2021 
300 |a 1 online resource  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a IET transportation series  |v 17 
504 |a Includes bibliographical references and index 
505 0 0 |a Machine generated contents note:   |g pt. I   |t Regional activities --   |g 1.  |t Japan Perspective /  |r Koichi Sakai --   |g 1.1.  |t History of intelligent transport system development in Japan --   |g 1.2.  |t Infrastructure sensors and driving assistance using V2I --   |g 1.2.1.  |t What is an infrastructure sensor? --   |g 1.2.2.  |t Events detected by infrastructure sensors --   |g 1.2.3.  |t Type of sensors that can be used as infrastructure sensors --   |g 1.2.4.  |t Driving assistance using infrastructure sensors --   |g 1.3.  |t Expressway case studies --   |g 1.3.1.  |t Forward obstacle information provision (Sangubashi Curve, Metropolitan Expressway) --   |g 1.3.2.  |t Forward obstacle information provision (Rinkai Fukutoshin Slip Road, Metropolitan Expressway) --   |g 1.3.3.  |t Forward obstacle information provision (Akasaka Tunnel, Metropolitan Expressway) --   |g 1.3.4.  |t Merging assistance (Tanimachi Junction, Higashi-Ikebukuro Slip Road and so on, Metropolitan Expressway) --   |g 1.3.5.  |t Smooth traffic flow assistance at sags (Yamato Sag, Tomei Expressway) --   |g 1.4.  |t Case studies on ordinary roads --   |g 1.4.1.  |t Rear-end collision prevention system --   |g 1.4.2.  |t Crossing collision prevention system --   |g 1.4.3.  |t Left-turn collision prevention system --   |g 1.4.4.  |t Right-turn collision prevention system --   |g 1.4.5.  |t Crossing pedestrian recognition enhancement system --   |g 1.5.  |t Driving safety assistance using vehicle-to-vehicle (V2V) communication --   |t References --   |g 2.  |t European Perspective Of Cooperative Intelligent Transport Systems /  |r Jacint Castells --   |g 2.1.  |t Introduction --   |g 2.2.  |t C-ITS development and deployment in Europe --   |g 2.3.  |t European C-ITS platform --   |g 2.4.  |t C-Roads initiative --   |g 2.5.  |t C-ITS architecture --   |g 2.6.  |t C-ITS services and use cases and operational guidelines --   |g 2.7.  |t Conclusions --   |t Acknowledgements --   |t Appendix A --   |t References --   |g 3.  |t Singapore Perspective: Smart Mobility /  |r Kian-Keong Chin --   |g 3.1.  |t Introduction --   |g 3.2.  |t Challenges and transport strategy --   |g 3.3.  |t Demand management -- a key element of the transport strategy --   |g 3.4.  |t Development of intelligent transport systems in Singapore --   |g 3.5.  |t Integrating ITS on a common platform --   |g 3.6.  |t Road pricing in Singapore --   |g 3.6.1.  |t manually operated Area Licensing Scheme --   |g 3.6.2.  |t Road pricing adopts intelligent technologies --   |g 3.6.3.  |t Challenges with the ERP system --   |g 3.6.4.  |t next-generation road pricing system --   |g 3.7.  |t Big data and analytics for traffic management and travellers --   |g 3.7.1.  |t Quality of data and information --   |g 3.7.2.  |t Travel information available from ITS in Singapore --   |g 3.8.  |t Connected and autonomous vehicles --   |g 3.9.  |t Concluding remarks --   |t References --   |g pt. II   |t Traffic state sensing by roadside unit --   |g 4.  |t Traffic Counting By Stereo Camera /  |r Toshio Sato --   |g 4.1.  |t Introduction --   |g 4.2.  |t General procedure traffic counting using stereo vision --   |g 4.2.1.  |t Stereo cameras --   |g 4.2.2.  |t Calibration of camera images --   |g 4.2.3.  |t Image rectification --   |g 4.2.4.  |t Block matching to produce a depth map --   |g 4.2.5.  |t Object detection --   |g 4.2.6.  |t Object tracking and counting --   |g 4.2.7.  |t Installation of stereo camera --   |g 4.3.  |t Accurate vehicle counting using roadside stereo camera --   |g 4.3.1.  |t System configuration --   |g 4.3.2.  |t Depth measurement based on binocular stereo vision --   |g 4.3.3.  |t Vehicle detection --   |g 4.3.4.  |t Traffic counter --   |g 4.3.5.  |t Results --   |g 4.4.  |t Summary --   |t References --   |g 5.  |t Vehicle Detection At Intersections By Lidar System /  |r Kentaro Mizouchi --   |g 5.1.  |t Introduction --   |g 5.1.1.  |t New trend --   |g 5.1.2.  |t Target applications --   |g 5.1.3.  |t Basic principal of LIDAR system --   |g 5.1.4.  |t Types of LIDAR system --   |g 5.1.5.  |t Performance of LIDAR system --   |g 5.1.6.  |t Current deployment status --   |g 5.2.  |t Application of vehicle detection by an IHI's 3D laser radar --   |g 5.2.1.  |t Practical application of a 3D laser radar is close at hand in playing a central role in the Intelligent Transport Systems --   |g 5.2.2.  |t Eyes that tell vehicles the road conditions at a nearby intersection --   |g 5.2.3.  |t Instant identification of objects with reflected laser light --   |g 5.2.4.  |t Advantage of all-weather capability and fast data processing --   |g 5.2.5.  |t Pilot program in Singapore --   |t References --   |g 6.  |t Vehicle Detection At Intersection By Radar System /  |r Yoichi Nakagawa --   |g 6.1.  |t Background --   |g 6.2.  |t High-resolution millimetre-wave radar --   |g 6.3.  |t Roadside radar system --   |g 6.4.  |t Technical verification under severe weather condition --   |g 6.4.1.  |t Objective --   |g 6.4.2.  |t Design for heavy rainfall condition --   |g 6.4.3.  |t Experiment in snowfall field --   |g 6.5.  |t Detection accuracy verification on public road --   |g 6.6.  |t Conclusion and discussion --   |t Acknowledgements --   |t References --   |g pt. III   |t Traffic state sensing by on board unit --   |g 7.  |t Gnss-Based Traffic Monitoring /  |r Benjamin Wilson --   |g 7.1.  |t Introduction --   |g 7.2.  |t GNSS probe data --   |g 7.3.  |t GNSS probe data attributes --   |g 7.4.  |t Historical data --   |g 7.5.  |t Probe data processing --   |g 7.6.  |t Real-time traffic information --   |g 7.7.  |t Example of probe data in use --   |g 7.8.  |t Historical traffic services --   |g 7.8.1.  |t Traffic speed average --   |g 7.8.2.  |t Historical traffic analytics information --   |g 7.9.  |t Advanced traffic features --   |g 7.10.  |t Split lane traffic --   |g 7.11.  |t Wide moving jam (safety messages) --   |g 7.12.  |t Automated road closures --   |g 7.13.  |t Quality testing --   |g 7.14.  |t Ground truth testing --   |g 7.15.  |t Probes as ground truth --   |g 7.16.  |t Q-Bench --   |g 7.17.  |t Conclusion --   |g 8.  |t Traffic State Monitoring By Close Coupling Logic With Obu And Cloud Applications /  |r Yoshikazu Ooba --   |g 8.1.  |t Introduction --   |g 8.2.  |t Smart transport cloud system --   |g 8.2.1.  |t Concept --   |g 8.2.2.  |t Key technology --   |g 8.3.  |t Usage case 1: estimation of traffic volume at highway --   |g 8.3.1.  |t System description --   |g 8.3.2.  |t Traffic volume estimation --   |g 8.4.  |t Usage case 2: estimation of traffic congestion and volume of pedestrian crowds --   |g 8.4.1.  |t Benefits from the system --   |g 8.4.2.  |t System description --   |g 8.4.3.  |t Logic design --   |g 8.4.4.  |t Evaluation --   |g 8.4.5.  |t Other possibilities for estimating traffic: finding parked vehicles --   |g 8.5.  |t Conclusion --   |t Acknowledgments --   |t References --   |g pt. IV   |t Detection and counting of vulnerable road users --   |g 9.  |t Monitoring Cycle Traffic: Detection And Counting Methods And Analytical Issues /  |r Andy Cope --   |g 9.1.  |t Introduction --   |g 9.1.1.  |t Importance of monitoring cycle traffic --   |g 9.1.2.  |t Nature of cycle traffic --   |g 9.2.  |t Current methods of detecting and counting --   |g 9.2.1.  |t Overview --   |g 9.2.2.  |t Manual classified counts --   |g 9.2.3.  |t Surface and subsurface equipment --   |g 9.2.4.  |t Above-ground detection --   |g 9.3.  |t Procedures, protocols and analysis --   |g 9.3.1.  |t Procedures and protocols --   |g 9.3.2.  |t Analysis --   |g 9.4.  |t Innovations in cycle-counting technology --   |g 9.4.1.  |t Harvesting digital crowdsourced data --   |g 9.4.2.  |t Issues and a future trajectory --   |t Acknowledgements --   |t References --   |g 10.  |t Crowd Density Estimation From A Surveillance Camera /  |r Viet-Quoc Pham --   |g 10.1.  |t Introduction --   |g 10.2.  |t Related works --   |g 10.3.  |t COUNT forest --   |g 10.3.1.  |t Building COUNT forest --   |g 10.3.2.  |t Prediction model --   |g 10.3.3.  |t Density estimation by COUNT forest --   |g 10.4.  |t Robust density estimation --   |g 10.4.1.  |t Crowdedness prior --   |g 10.4.2.  |t Forest permutation --   |g 10.4.3.  |t Semiautomatic training --   |g 10.5.  |t Experiments --   |g 10.5.1.  |t Counting performance --   |g 10.5.2.  |t Robustness --   |g 10.5.3.  |t Semiautomatic training --   |g 10.5.4.  |t Application 1: traffic count --   |g 10.5.5.  |t Application 2: stationary time --   |g 10.6.  |t Conclusions --   |t References --   |g pt. V   |t Detecting factors affecting traffic --   |g 11.  |t Incident Detection /  |r Neil Hoose --   |g 11.1.  |t Introduction --   |g 11.2.  |t Incident detection in the context of the incident management process --   |g 11.3.  |t Key parameters for incident detection --   |g 11.4.  |t Incident detection using traffic-parameter-based technologies and techniques --   |g 11.4.1.  |t Flow in vehicles per hour per lane or per direction --   |g 11.4.2.  |t Average speed per time interval at a specific location --   |g 11.4.3.  |t Average speed over a distance, or journey time, per time interval --   |g 11.4.4.  |t Headway (time) in seconds average per lane per time interval --   |g 11.4.5.  |t Detector occupancy --   |g 11.5.  |t Sensor technologies --   |g 11.5.1.  |t Inductive loops --   |g 11.5.2.  |t Fixed-beam RADAR --   |g 11.5.3.  |t Computer vision --   |g 11.5.4.  |t Journey time measurement using licence plates --   |g 11.5.5.  |t Journey time measurement using Bluetooth and Wi-Fi --   |g 11.6.  |t Wide-area incident detection techniques --   |g 11.6.1.  |t Computer vision --   |g 11.6.2.  |t Scanning radar --   |g 11.6.3.  |t Use of linear radar --   |g 11.6.4.  |t Light detection and ranging --   |g 11.6.5.  |t Longitudinal optic fibre --   |g 11.6.6.  |t Mobile phone, probe vehicle and connected-autonomous-vehicle-based techniques --   |g 11.6.7.  |t Social media and crowd-sourcing techniques --   |g 11.7.  |t Comment on incident detection technology --   |t References --   |g 12.  |t Sensing Of Heavy Precipitation---Development Of Phased-Array Weather Radar /  |r Tomoo Ushio --   |g 12.1.  |t Introduction --   |g 12.2.  |t Background --   |g 12.3.  |t Problems --   |g 12.4.  |t Phased-array weather radar --   |g 12.5.  |t Observations --   |g 12.6.  |t Future --   |t References. 
533 |a Electronic reproduction.  |b Ann Arbor, MI  |n Available via World Wide Web. 
588 |a Description based on online resource; title from digital title page (viewed on May 27, 2021). 
650 0 |a Intelligent transportation systems. 
650 0 |a Traffic monitoring  |x Technological innovations. 
700 1 |a Ozaki, Nobuyuki  |e editor 
710 2 |a ProQuest (Firm) 
776 0 8 |i Print version:  |t Smart sensing for traffic monitoring  |d Stevenage : Institution of Engineering and Technology, 2020  |z 9781785617744 
830 0 |a IET transportation series ;  |v 17. 
856 4 0 |u https://ebookcentral.proquest.com/lib/santaclara/detail.action?docID=6467735  |z Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)  |t 0 
907 |a .b38249315  |b 211018  |c 211018 
998 |a uww  |b    |c m  |d z   |e l  |f eng  |g enk  |h 0 
917 |a GOBI ProQuest DDA 
919 |a .ulebk  |b 2020-07-09 
999 f f |i ab4d025e-afd9-5218-9327-7170a377271a  |s df0180cd-1937-54b3-9b5e-1e6dc6711926  |t 0 

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