An application of the CUSUM algorithm to freeway incident detection based on two contiguous detectors

Qi, Yi; Martinelli, Dante Pinheiro; Teng, Hualiang; Jiang, Pengguo

doi:10.1002/atr.5670390206

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…It is worth mentioning that in the CUSUM algorithm only a single variable is considered (Y. G. Qi, Martinelli, Teng, & Jiang, 2010). If more than one parameter is required to monitor, it is recommended to integrate the variables.…”

Section: Methods For Waypoints Generationmentioning

confidence: 99%

Data analysis in the maritime domain

Stróżyna¹,

Abramowicz²,

Węcel³

et al. 2022

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The book delivers a rich set of foundations, state-of-the-art knowledge, new approaches and methods for the purpose of anomalies detection, maritime traffic analysis as well as risk and reliability assessment. It addresses relevant research problems at the intersection of maritime transport in global economies, reliability and risk assessment, and information systems and data processing. The book provides a theoretical overview of available maritime data sources and approaches for maritime data analysis, as well as a set of novel tools and methods for maritime data retrieval, fusion, and analysis. The proposed methods are evaluated on real-life AIS data, covering the entire world and more than 200 thousands of vessels, illustrating how they may be used for anomaly detection and risk assessment. The primary audience of the book are researchers from the fields of computer science and maritime transport as well as logistics service providers, shipping companies and port authorities companies that need support in managing security, safety, and risk of maritime transport services in global economies by making use of large-scale data processing.

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Section: Methods For Waypoints Generationmentioning

confidence: 99%

Data analysis in the maritime domain

Stróżyna¹,

Abramowicz²,

Węcel³

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Qi et al . adopt the cumulative sum of log‐likelihood ratio (CUSUM) algorithm to detect freeway incidents by integrating traffic measurements from two contiguous loop detectors and the non‐stationarity of traffic flows . In , Neural networks are proposed to detect traffic incidents.…”

Section: Introductionmentioning

confidence: 99%

More robust and better: a multiple kernel support vector machine ensemble approach for traffic incident detection

Xiao

Gao

Kong

et al. 2013

J of Advced Transportation

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This paper presents a multiple kernel support vector machine (MKL-SVM) ensemble algorithm to detect traffic incidents. It uses resampling technology to generate training set, test set, and training subset firstly; then uses different training subsets to train individual MKL-SVM classifiers; and finally introduces ensemble methods to construct MKL-SVM ensemble to detect traffic incidents. Extensive experiments have been performed to evaluate the performances of the four algorithms: standard SVM, SVM ensemble, MKL-SVM, and the proposed algorithm (MKL-SVM ensemble). The experimental results show that the proposed algorithm has the best comprehensive performances in traffic incidents detection. To achieve better performances, the proposed algorithm needs less individual classifiers to construct the ensemble than SVM ensemble algorithm. Thus, compared with SVM ensemble algorithm, the complexity of the ensemble classifier of the proposed algorithm is reduced greatly. Conveniently, the proposed algorithm also avoids the burden of selecting the appropriate kernel function and parameters.(DR), false alarm rate (FAR), and detection time. Some techniques based on video processing have been adopted to detect traffic incidents. Trivedi et al. described a novel architecture for developing distributed video networks for incident detection and management [29]. Mak and Fan made a study on the detection of lane-blocking incidents on the basis of the traffic data provided by video-based detectors installed along Singapore's Central Expressway [15]. It is a meaningful attempt to use the video processing techniques to detect traffic incidents, but it is sensitive to the outdoor environmental factors, namely static shadows, snow, rain, and glare. Shehata et al. present a literature review of outdoor environment detection. Once the environmental conditions are detected, they can be compensated for, and hence, the accuracy of alarms detected by video-based AID systems will be enhanced [27]. But this compensation is not able to completely eliminate the impact produced by the outdoor environment.The inductive loop detector (ILD) is the most commonly used sensor in traffic surveillance and management applications, which can collect the traffic data stably and does not suffer the outdoor environment impact. There are a lot of advanced algorithms that focus on detecting traffic incidents using ILD data and have achieved good results. These advanced algorithms divide the traffic patterns into two groups-incident traffic pattern and incident-free traffic pattern-then, the traffic incident detection problem will be transformed into a binary classification problem based on ILD data. Qi et al. adopt the cumulative sum of log-likelihood ratio (CUSUM) algorithm to detect freeway incidents by integrating traffic measurements from two contiguous loop detectors and the non-stationarity of traffic flows [23]. In [8,1,25,26], Neural networks are proposed to detect traffic incidents. The conclusion of their papers is that artificial neural networks have ...

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New Automatic Incident Detection Algorithm Based on Traffic Data Collected for Journey Time Estimation

Lam

Tam

2013

J. Transp. Eng.

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An application of the CUSUM algorithm to freeway incident detection based on two contiguous detectors

Cited by 4 publications

References 9 publications

Data analysis in the maritime domain

Data analysis in the maritime domain

More robust and better: a multiple kernel support vector machine ensemble approach for traffic incident detection

New Automatic Incident Detection Algorithm Based on Traffic Data Collected for Journey Time Estimation

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