Application of a Dynamic Model for Arterial Street Incident Detection

Lee, Jung-Taek; Taylor, William C.

doi:10.1080/10248079908903757

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Lee and Taylor (Lee, 1999) used a modified discrete linear Kalman filtering algorithm to filter and update aggregate traffic flow and speed to estimate true values and compare them with measured traffic parameters to detect an incident when a distinct difference is identified.…”

Section: Arterial Incident Detection Methods and Algorithms On Arterimentioning

confidence: 99%

Automatic Incident Detection at Intersections with Use of Telematics

Oskarbski

Zawisza

Żarski

2016

Transportation Research Procedia

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While there are many examples of Intelligent Transport System deployments in Poland, more attention should be paid to traffic incident management and detection on dual-carriageways and urban street networks. One of the aims of CIVITAS DYN@MO, a European Union funded project, is to use TRISTAR (an Urban Transport Management System) detection modules to detect incidents at junctions equipped with traffic signals. First part of paper provides an overview of urban incident detection methods and algorithms. Second part of paper describes how the TRISTAR system infrastructure and software are currently used for detecting incidents on urban artery sections (with higher speed limit). Because the need to detect incidents on other arteries was identified, research were undertaken that will lead to the development of algorithms for the detection of incidents on the streets equipped with traffic signals. The initial results of simulation studies (using VISSIM software) are presented in the third part of the paper. Their objective was to initially select parameters for detecting incidents at junctions equipped with traffic signals. Further research will look at a fusion of variables and possible other variables that may develop the algorithms.

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Section: Arterial Incident Detection Methods and Algorithms On Arterimentioning

confidence: 99%

Automatic Incident Detection at Intersections with Use of Telematics

Oskarbski

Zawisza

Żarski

2016

Transportation Research Procedia

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“…The algorithm along with some other early AIDAs (Stephanedes and Vassilakis 1994;Culip & Hall, 1997) used raw traffic data to detect incidents on urban arterials. Time-series techniques (Bretherton and Bowen, 1991), image processing technologies (Hoose et al 1992), discriminant techniques (Sethi et al, 1995), discriminant techniques combined with Kalman filtering (Chen and Chang, 1993) and Kalman filtering (Lee & Taylor, 1999) were developed to tackle the problem of incident detection on arterials. Data fusion techniques (Ivan et al 1998 andThomas 2011) used a fusion of data from separate sources: inductive loop detectors and travel time data collected by probe vehicles to detect incidents on urban arterials.…”

Section: Introductionmentioning

confidence: 99%

Customization of Automatic Incident Detection Algorithms for Signalized Urban Arterials

Ghosh

Smith

2014

Journal of Intelligent Transportation Systems

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Non-recurrent congestion or incidents are detrimental to the operability and efficiency of busy urban transport networks. There exists multiple Automatic Incident Detection Algorithms (AIDA) to remotely detect the occurrence of an incident in highway or freeway scenarios, however very little research has been performed to automatically detect incidents in signalised urban arterials. This limited research attention has mostly been focussed on developing new urban arterial specific algorithms rather than identifying alternative methods to synthesize existing freeway based algorithms to urban conditions. The main hindrance to such synthesis is that the traffic patterns on the signalised urban arterials are significantly different from the same on highways/freeways due to the presence of traffic intersections. This paper introduces a new strategy of customising the existing AIDAs (freeway based or otherwise) to significantly improve their adaptability to signalised urban arterial transport networks. The new strategy focuses on preprocessing the traffic information before being used as input to a freeway/highway based AIDA to lessen the effect of traffic signals and to imitate the input patterns in highway/freeway based incident conditions. The effectiveness of this new strategy has been established with the help of four existing AIDAs. The proposed strategy is a simple solution to implement existing algorithms to signalised urban networks without any further instrumentation or operational cost.

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Comparative evaluation of freeway incident detection models using field data

Thomas

Dia

2006

IEE Proc. Intell. Trans. Syst.

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Application of a Dynamic Model for Arterial Street Incident Detection

Cited by 4 publications

References 3 publications

Automatic Incident Detection at Intersections with Use of Telematics

Automatic Incident Detection at Intersections with Use of Telematics

Customization of Automatic Incident Detection Algorithms for Signalized Urban Arterials

Comparative evaluation of freeway incident detection models using field data

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