A wavelet-based freeway incident detection algorithm with adapting threshold parameters

Jeong, Young-Seon; Castro-Neto, Manoel Mendonça de; Jeong, Myong K.; Lee, Han

doi:10.1016/j.trc.2009.10.005

Cited by 41 publications

(21 citation statements)

References 34 publications

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“…It is worth mentioning though that the first four techniques [9], [17], [18], [24], which claim to obtain DR = 100%, seem to calculate the value of DR in a different way. In particular, once an incident has been correctly detected, any potential detection failure that might follow in the next time intervals is not taken into account in the calculation of DR, for as long as the incident lasts.…”

Section: Discussionmentioning

confidence: 99%

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Mining traffic data for road incidents detection

Gakis

Kehagias

Tzovaras

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Tackling urban road congestion by means of ITS technologies, involves a number of key challenges. One such challenge is related to the accurate detection of traffic incidents in urban networks for more efficient traffic management. This paper introduces a classification approach that achieves accurate detection of road traffic incidents, based on data retrieved from inductive-loop detectors. In the core of the proposed approach lies a more efficient feature extraction technique, based on the dynamic characteristics of data corresponding to those vehicles that are involved in incidents. Our work observes how dynamic aspects of measured data can be exploited for extracting features that result in measurable improvement of the incident detection rate by the application of a Support Vector Machines classification approach. The latter is known to be one of the most precise solutions that have been widely applied up to now for dealing with relevant incident detection problems. In this paper we conduct appropriate experimental evaluation, including comparison to a set of well known techniques, for assessing the impact of the proposed feature selection technique on the accuracy of the detection rate. The evaluation results show that our approach manages to realize a more accurate and faster incident detection mechanism, although it has a low impact on the improvement of the false alarm rate. We also describe how the proposed approach can be applied to a generic context of ITS for efficient urban traffic management.

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Section: Discussionmentioning

confidence: 99%

“…Jeong et al [18] suggested a wavelet-based method with varying threshold parameters according to the traffic flow. Zhou et al [19] applied SVM with linear kernel.…”

Section: B Related Workmentioning

confidence: 99%

Mining traffic data for road incidents detection

Gakis

Kehagias

Tzovaras

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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“…A variety of approaches have been developed for traffic incident detection [9], such as the California algorithms (CA) [10], [11], probe-based methods [12], [13], the artificial intelligence based method [14], the neural network approach [15], and the wavelet-based method [16]. Among these approaches, the most well known ones are variants of the California algorithm [10], [11].…”

Section: B Related Workmentioning

confidence: 99%

Multiple Model Particle Filter for Traffic Estimation and Incident Detection

Wang

Work

Sowers

2016

IEEE Trans. Intell. Transport. Syst.

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This paper poses the joint traffic state estimation and incident detection problem as a hybrid state estimation problem, in which a continuous variable denotes the traffic state and a discrete model variable identifies the location and severity of an incident. A multiple model particle smoother is proposed to solve the hybrid estimation problem, in which the multiple model particle filter is used to accommodate the nonlinearity and switching dynamics of the traffic incident model, and the smoothing algorithm is applied to improve the accuracy of the estimate when data are limited. The proposed algorithms are evaluated through numerical experiments using CORSIM as the true model. The proposed algorithm is also compared with a standard macroscopic traffic estimator via particle filtering and the California incident detection algorithm. The results show that jointly estimating the state and incidents in one algorithm is better than two dedicated algorithms working independently.

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“…As it turns out, abnormal data detection is a relatively easy problem because the difference between two traffic time series is dominated by the difference between their daily trends [42], [43], and the time series recorded from normal and abnormal sensors often have quite different trends.…”

Section: Abnormal Data Detection and Trend Modelingmentioning

confidence: 99%

Trend Modeling for Traffic Time Series Analysis: An Integrated Study

Li¹,

Zhang³

et al. 2015

IEEE Trans. Intell. Transport. Syst.

135

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This paper discusses the trend modeling for traffic time series. First, we recount two types of definitions for a long-term trend that appeared in previous studies and illustrate their intrinsic differences. We show that, by assuming an implicit temporal connection among the time series observed at different days/locations, the PCA trend brings several advantages to traffic time series analysis. We also describe and define the so-called short-term trend that cannot be characterized by existing definitions. Second, we sequentially review the role that trend modeling plays in four major problems in traffic time series analysis: abnormal data detection, data compression, missing data imputation, and traffic prediction. The relations between these problems are revealed, and the benefit of detrending is explained. For the first three problems, we summarize our findings in the last ten years and try to provide an integrated framework for future study. For traffic prediction problem, we present a new explanation on why prediction accuracy can be improved at data points representing the short-term trends if the traffic information from multiple sensors can be appropriately used. This finding indicates that the trend modeling is not only a technique to specify the temporal pattern but is also related to the spatial relation of traffic time series.

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A wavelet-based freeway incident detection algorithm with adapting threshold parameters

Cited by 41 publications

References 34 publications

Mining traffic data for road incidents detection

Mining traffic data for road incidents detection

Multiple Model Particle Filter for Traffic Estimation and Incident Detection

Trend Modeling for Traffic Time Series Analysis: An Integrated Study

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