Heavy Flow-Based Incident Detection Algorithm Using Information From Two Adjacent Detector Stations

Mak, Chin Long; Fan, Henry

doi:10.1080/15472450500455229

Cited by 15 publications

(4 citation statements)

References 12 publications

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“…In their review paper, Liu et al [117] looked at the latest technologies and applications of video analytics and intelligent video systems. Video analytics has been successfully applied in traffic control systems to detect traffic volume for planning, highlighting incidents and enhancing safety by enforcing traffic rules [124]. Another set of applications is for intelligent vehicles to assist the driver.…”

Section: Transport: Traffic Control and Routing Pedestrian Detectionmentioning

confidence: 99%

Analytics for the Internet of Things

2018

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The Internet of Things (IoT) envisions a world-wide, interconnected network of smart physical entities. These physical entities generate a large amount of data in operation and as the IoT gains momentum in terms of deployment, the combined scale of those data seems destined to continue to grow. Increasingly, applications for the IoT involve analytics. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorisation of analytic approaches and propose a layered taxonomy from IoT data to analytics. This taxonomy provides us with insights on the appropriateness of analytical techniques, which in turn shapes a survey of enabling technology and infrastructure for IoT analytics. Finally, we look at some tradeoffs for analytics in the IoT that can shape future research.

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Section: Transport: Traffic Control and Routing Pedestrian Detectionmentioning

confidence: 99%

Analytics for the Internet of Things

2018

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“…The algorithms developed so far fall into three broad categories: volume monitoring in combination with forecasts from traffic models [18,19], statistical methods applied to short-period traffic data [20,21], and machine-learning techniques [22][23][24][25][26][27]. In addition, recently, there has been some success in using unconventional data like cellular phone tracking or social media posts to detect incidents [28][29][30], video-based detectors [31,32], and methods based on acoustic data [33].…”

Section: Introductionmentioning

confidence: 99%

Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan

Zaitouny

Fragkou

Stemler

et al. 2022

Sensors

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Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.

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“…Modern traffic detection technologies have provided new opportunities for incident detection. Some algorithms have been proposed taking the advantage of new data sources, such as automatic vehicle identification (AVI) (Parkany & Bernstein, 1995), global positioning system (GPS) probe vehicles (Sethi, Bhandahari, Koppelman, & Schofer, 1995), cellular probe vehicles (Balke, Dudek, & Mountain, 1996;Sermons & Koppelman, 1996), traffic image (Mak & Fan, 2006), and even traffic sound (Harlow & Wang, 2002). New data sources bring new opportunities for AID system development, but they still have much less coverage than loop detectors.…”

Section: Introductionmentioning

confidence: 99%

Automatic Incident Detection for Urban Expressways Based on Segment Traffic Flow Density

Cheng

Zhang

Yang

2015

Journal of Intelligent Transportation Systems

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Urban expressways play an important role in urban road networks. Although automatic incident detection (AID) methods have been studied for a long time, most of the existing AID algorithms are designed for freeways and do not explicitly consider the detection of incidents near ramps with frequent in and out weaving flows. Urban expressways usually have short spaced ramps, which makes the traffic flow characterizations quite different from the ones of freeways. In addition, expressways usually have heavy traffic flow, which makes it more difficult to distinguish incidents from congestion. This article presents an AID method for urban expressways, using loop detector data. Based on the geometric conditions and detector locations, the expressway is divided into short segments. The equivalent upstream and downstream traffic flow density difference is defined and calculated using the loop detector data. A detection logic based on the pattern of the density difference fluctuation is then proposed. The performance of the proposed algorithm was tested using real incident data from a corridor of the Shanghai expressway and compared with two classic AID algorithms. The results indicate that this method performs very well and is suitable for urban expressways.

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Heavy Flow-Based Incident Detection Algorithm Using Information From Two Adjacent Detector Stations

Cited by 15 publications

References 12 publications

Analytics for the Internet of Things

Analytics for the Internet of Things

Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan

Automatic Incident Detection for Urban Expressways Based on Segment Traffic Flow Density

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