Application of Filtering Techniques for Incident Detection

Stephanedes, Yorgos J.; Chassiakos, Athanasios P.

doi:10.1061/(asce)0733-947x(1993)119:1(13)

Cited by 49 publications

(21 citation statements)

References 2 publications

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“…These are discussed below in details. Low pass filtering algorithm is developed by [12] to minimize the false alarm rate. This algorithm used average volume and occupancy at every 30 s interval.…”

Section: Available Practices To Detect Incident Impacts On the Freewaymentioning

confidence: 99%

A Literature Review on Freeway Traffic Incidents and Their Impact on Traffic Operations

Islam¹

2019

JTTs

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Congestion on the freeway is more frequent due to several traffic incidents, namely traffic accidents, debris on the road, vehicle breakdown, and collision with guardrails than any other incidents. These, in turn, affect the operational performance of the freeway by increasing queue length, volume, and density. Consequently, effective freeway management strategies can help to minimize these impacts. The study investigates and summarizes existing studies to identify the reasons for and effects of the traffic incidents. Attention is given to the available solutions of the freeway traffic incidents management. The ultimate goal of this study is to identify the gaps which are not yet addressed to improve the operational effectiveness of the freeway. This study was conducted through a comprehensive literature review of existing refereed publications, established standards, and formal guidelines. Literature was sought through the Transport Research International Documentation (TRID) database, IEEE Transactions database, and google scholar search engine. Research focusing on freeway traffic incidents is a growing concern in transportation operations, as transportation network performance depends on it. Due to the advancement of technology, emerging vehicle technologies like connected vehicles have the potential to address these problems affecting the US transportation system and revolutionize mobility in the future. The study can serve as a reference for the researchers that are involved in freeway traffic operations.

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Section: Available Practices To Detect Incident Impacts On the Freewaymentioning

confidence: 99%

A Literature Review on Freeway Traffic Incidents and Their Impact on Traffic Operations

Islam¹

2019

JTTs

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“…Traffic data usually exhibit sudden and large changes in magnitude that reduce the reliability of algorithms. Statistical techniques for preprocessing the raw data have been proposed in the past (Cook and Cleveland 1974;Dudek et al 1974;Ahmed and Cook 1982;Stephanedes and Chassiakos 1993). Dudek et al (1974) used the standard normal deviate of the data in their threshold-based algorithm, whereas Cook and Cleveland (1974) proposed the use of double exponential smoothing of traffic data in a similar algorithm logic.…”

Section: Incident Detection Algorithmsmentioning

confidence: 99%

“…Ahmed and Cook (1982) presented a short-time time-series moving average model of occupancy data to determine large deviations and predict incidents. The Minnesota algorithm (Stephanedes and Chassiakos 1993) uses a moving average smoothing approach to remove high frequency components in observed data. The smoothed data are then employed in the algorithm logic for incident detection.…”

Section: Incident Detection Algorithmsmentioning

confidence: 99%

Fuzzy-Wavelet RBFNN Model for Freeway Incident Detection

2000

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Traffic incidents are nonrecurrent and pseudorandom events that disrupt the normal flow of traffic and create a bottleneck in the road network. The probability of incidents is higher during peak flow rates when the systemwide effect of incidents is most severe. Model-based solutions to the incident detection problem have not produced practical, useful results primarily because the complexity of the problem does not lend itself to accurate mathematical and knowledge-based representations. A new multiparadigm intelligent system approach is presented for the solution of the problem, employing advanced signal processing, pattern recognition, and classification techniques. The methodology effectively integrates fuzzy, wavelet, and neural computing techniques to improve reliability and robustness. A wavelet-based denoising technique is employed to eliminate undesirable fluctuations in observed data from traffic sensors. Fuzzy c-mean clustering is used to extract significant information from the observed data and to reduce its dimensionality. A radial basis function neural network (RBFNN) is developed to classify the denoised and clustered observed data. The new model produced excellent incident detection rates with no false alarms when tested using both real and simulated data.

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“…This approach includes the Standard Normal Deviate (SND) method (Dudek er ol., 1974), the Bayesian approach (Levin and Kraus, 1978), the exponential smoothing method (Cook and Cleveland, 1974), the Box-Jenkins technique (Ahmed and Cook, 1980), and the filtering techniques (Stephanedes er al., 1992; Stephanedes and Chassiakos, 1993). These methods have theoretical limitations such as historical data dependency, fixed smoothing factors obtained by trial-and-error, non-adaptive parameters, and smoothing with equal weighting assignments.…”

Section: Introductionmentioning

confidence: 99%

Application of a Dynamic Model for Arterial Street Incident Detection

Lee

Taylor

1999

ITS Journal - Intelligent Transportation Systems Journal

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Existing freeway and signalized arterial street incident detection algorithms were investigaled lo determine their merit for use on urban arterial streets. Based on this investigalion, a Kalman filtering algorithm was modified to recursively filter and update aggregate traffic flow and speed data to estimate true values. A test using measured arterial street data at a signalized intersection shows good tracking ability on these trafiic variables over time. A test using data from an incident on an arterial street also confirmed that this algorithm has good potential for arterial street incident detection.

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Application of Filtering Techniques for Incident Detection

Cited by 49 publications

References 2 publications

A Literature Review on Freeway Traffic Incidents and Their Impact on Traffic Operations

A Literature Review on Freeway Traffic Incidents and Their Impact on Traffic Operations

Fuzzy-Wavelet RBFNN Model for Freeway Incident Detection

Application of a Dynamic Model for Arterial Street Incident Detection

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