An incident detection system based on semantic hierarchy

2005 ◽  
Author(s):  
S. Kamijo ◽  
M. Harada ◽  
M. Sakauchi
Keyword(s):  
Detection System ◽  
Incident Detection ◽  
Semantic Hierarchy

Traffic incident detection system based on video image processing

2008 ◽  
Vol 28 (7) ◽  
pp. 1886-1889 ◽  
Author(s):  
Qin WANG ◽  
Shan HUANG ◽  
Hong-bin ZHANG ◽  
Quan YANG ◽  
Jian-jun ZHANG
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Video Image ◽  
Incident Detection ◽  
Traffic Incident ◽  
Video Image Processing

scholarly journals A support vector machine with the tabu search algorithm for freeway incident detection

2014 ◽  
Vol 24 (2) ◽  
pp. 397-404 ◽  
Author(s):  
Baozhen Yao ◽  
Ping Hu ◽  
Mingheng Zhang ◽  
Maoqing Jin
Keyword(s):  
Support Vector Machine ◽  
Tabu Search ◽  
Traffic Management ◽  
Search Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Incident Detection ◽  
Tabu Search Algorithm ◽  
Proposed Model ◽  
Parameter Values

Abstract Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

Traffic Monitoring and Congestion Management in the SCOOT Urban Traffic Control System

1998 ◽  
Vol 1634 (1) ◽  
pp. 118-122 ◽  
Author(s):  
David Bretherton ◽  
Keith Wood ◽  
Neil Raha
Keyword(s):  
Control System ◽  
Traffic Control ◽  
Detection System ◽  
Congestion Management ◽  
Traffic Monitoring ◽  
Urban Traffic ◽  
Incident Detection ◽  
The European Union ◽  
Traffic Control System ◽  
Urban Traffic Control

The SCOOT Urban Traffic Control system is now operating in over 170 cities worldwide, including 7 systems in North America. Since the first system was installed, there has been a continuous program of research and development to provide new facilities to meet the requirement of the traffic manager. The latest version of SCOOT (Version 3.1) incorporates a traffic information database, ASTRID, and an incident-detection system, INGRID, and provides a number of facilities for congestion control. The traffic monitoring facilities of SCOOT, including a new facility to estimate emissions from vehicles, and the current program of work to enhance the incident-detection system and to provide additional facilities to manage incidents and congestion are reported in this paper. The work is being carried out as part of the European Union, DGXIII 4th Framework project, COSMOS, with additional funding from the UK Department of Transport. The enhanced system is to be installed in the Kingston Borough of London, where it will be tested in combination with congestion warning information provided by variable message signs.

Current Developments in SCOOT: Version 3

1996 ◽  
Vol 1554 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Dave B retherton
Keyword(s):  
Control System ◽  
Data Base ◽  
Traffic Control ◽  
Detection System ◽  
Urban Traffic ◽  
Incident Detection ◽  
Bus Priority ◽  
Current Information ◽  
Traffic Control System ◽  
Urban Traffic Control

The SCOOT urban traffic control system is now operating successfully in more than 130 towns and cities worldwide. The latest version of SCOOT has been extended to include support for bus priority, the automatic SCOOT traffic information data base (ASTRID) system, and the INGRID incident detection system and has been given added flexibility, particularly for use in incident conditions. Bus priority in SCOOT was developed within the European Union DRIVE 2 project PROMPT. This software has now been issued as part of the latest SCOOT version, following the field trials in London and Southampton, United Kingdom, which showed that significant benefits to buses could be obtained. The ASTRID data base has now been integrated with SCOOT and can run in the same machine as the urban traffic control system. As well as providing current and historical information to traffic engineers, ASTRID now can feed historic information back into SCOOT, providing a substitute cyclic flow profile that can be used for optimization when there are faulty detectors. The INGRID incident detection system contains two algorithms to provide an indication of an incident; taking current information directly from SCOOT, INGRID detects abnormal changes in flow and occupancy, and comparing current information with historic information stored in the ASTRID data base, INGRID detects abnormal patterns in these parameters. The SCOOT optimizers have been made more flexible and can now make larger changes to the signal timings if required. This facility can be switched on or off and would be particularly useful where an incident has been detected.

Exploiting Wired-Pipe Technology in an Adaptive Observer for Drilling Incident Detection and Estimation

SPE Journal ◽  
2021 ◽  
pp. 1-20
Author(s):  
Haavard Holta ◽  
Ole Morten Aamo
Keyword(s):  
Case Studies ◽  
Detection System ◽  
Pressure Sensors ◽  
Adaptive Observer ◽  
Incident Detection ◽  
Detection And Estimation ◽  
Fundamental Properties ◽  
Annulus Formation ◽  
Lumped Models ◽  
Pde Systems

Summary We deploy an adaptive observer recently developed for general hyperbolic partial differential equation (PDE) systems to detect and diagnose various drilling incidents. The well is modeled by a distributed PDE which, contrary to lumped models, preserves fundamental properties of well-flow dynamics enabling faster and more accurate incident detection and estimation. Wired drillpipe technology with pressure sensors is needed to locate and isolate incidents. Four realistic simulation case studies demonstrating various properties of the observer are presented. Drilling incidents treated in the simulation case studies include packoff in the annulus, formation in-flows, loss of circulation, and various combinations of these. Although simulation results show that the developed observers successfully estimate properties of the incidents that they are tailored for, they do not constitute an incident-detection system for drilling. However, they provide a part of the data on which an overall incident-detection system can rely.

scholarly journals Using Principal Component Analysis to Solve a Class Imbalance Problem in Traffic Incident Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Changjiang Zheng ◽  
Shuyan Chen ◽  
Wei Wang ◽  
Jian Lu
Keyword(s):  
Principal Component Analysis ◽  
Detection System ◽  
Class Imbalance ◽  
Principal Component ◽  
Component Analysis ◽  
Incident Detection ◽  
Least Squares Regression ◽  
Class Imbalance Problem ◽  
Significance Level ◽  
Traffic Incident

High imbalances occur in real-world situations when a detection system needs to identify the rare but important event of a traffic incident. Traffic incident detection can be treated as a task of learning classifiers from imbalanced or skewed datasets. Using principal component analysis (PCA), a one-class classifier for incident detection is constructed from the major and minor principal components of normal instances. Experiments are conducted with a real traffic dataset collected from the A12 highway in The Netherlands. The parameters setting, including the significance level, the percentage of the total variation explained, and the upper bound of the eigenvalues for the minor components, is discussed. The test results demonstrate that this method achieves better performance than partial least squares regression. The method is shown to be promising for traffic incident detection.

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