• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.53-65
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• 2004

A series of accidents, which are non-recurrent and non-anticipated, are called incidents. These incidents make standard traffic flows interrupt, which result in the decrease of road capacity and a number of social and economic costs, such as the traffic congestion and air pollution. In order to prevent the hazard of incidents, domestic and foreign traffic management center are likely to opt auto-sense system with algorithms of auto-incident sense. However, it is evaluated that the algorithms have a low function with frequent wrong alarms, even if they accurately ry to speculate the incidents. In the case of bottleneck which has lack of road capacity, compared with other roads, due to inefficient road structured over-capacity of the demand of on-off ramp, the incidents regularly take place. Nonetheless, it can be more difficult to speculate the auto-incidents sense owing to similar incidents, such as the queue of in-out flows of cars and the change of road line. Throughout this research, the function of the model has improved excluding near road line in the module of the incidents which is based on the auto-incidents algorithms during the sense of the congestion of ramp areas.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.2
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• pp.13-23
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• 2016

Many traffic centers are highly hesitant in employing existing Automatic Incident Detection Algorithms due to high false alarm rate, low detection rate, and enormous effort taken in maintaining algorithm parameters, together with complex algorithm structure and filtering/smoothing process. Concerns grow over the situation particularly in Freeway Incident Management Area This study proposes a new algorithm and introduces a novel concept, the Displaced Flow Index (DiFI) which is similar to a product of relative speed and relative occupancy for every execution period. The algorithm structure is very simple, also easy to understand with minimum parameters, and could use raw data without any additional pre-processing. To evaluate the performance of the DiFI algorithm, validation test on the algorithm has been conducted using detector data taken from Naebu Expressway in Seoul and following transferability tests with Gyeongbu Expressway detector data. Performance test has utilized many indices such as DR, FAR, MTTD (Mean Time To Detect), CR (Classification Rate), CI (Composite Index) and PI (Performance Index). It was found that the DR is up to 100%, the MTTD is a little over 1.0 minutes, and the FAR is as low as 2.99%. This newly designed algorithm seems promising and outperformed SAO and most popular AIDAs such as APID and DELOS, and showed the best performance in every category.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.2
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• pp.29-38
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• 2012

The data quality and measurements along consecutive detector stations can vary much even in the same traffic conditions due to variety in detector types, calibration and maintenance effort, field operation periods, minor geometric changes of roads and so on. These faulty situations often create 10% or more of inherent difference in important traffic measurements between two stations even under stable low flow condition. Low detection rates(DR) and high false alarm rates(FAR) therefore sets in among many popular Automatic Incident Detection Algorithms(AIDA). This research is two-folded and aims mainly to develop a new AIDA for uninterrupted flow. For this purpose, a technique which utilizes a Simple Arithmetic Operation(SAO) of traffic variables is introduced. This SAO technique is designed to address the inherent discrepancy of detector data observed successive stations, and to overcome the degradation of AIDA performance. It was found that this new algorithm improves DR as much as 95 percent and above. And mean time to detection(MTTD) is found to be 1 minutes or less. When it comes to FAR, this new approach compared to existing AIDAs reduces FAR up to 31.0 percent. And capability in persistency check of on-going incidents was found excellent as well.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.1006-1009
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• 2009

매년 운전자의 운전미숙과 돌발 상황으로 인한 자동차사고로 인해 수만 명의 사망자와 부상자가 나오고 있으며 이로 인한 경제적 손실도 막대하다. 현재 충돌사고를 방지하기 위해 레이더나 초음파센서와 같은 통신기기를 활용한 충돌회피시스템이 개발되고 있는 상황으로 충돌회피를 위해서는 센서부도 중요한 역할을 하지만 충돌회피 알고리즘 개발이 무엇보다 중요하다. 본 논문에서는 두 개의 초음파 센서를 이용하여 전방에 있는 장애물을 회피할 수 있는 시스템을 구축하였다. 자동차가 자율적인 판단에 따라 충돌 없이 이동할 수 있는 능력을 갖게 하기 위하여 초음파센서와 광센서를 이용하였으며, 라인트레이서와 같이 라인을 따라 자동차가 이동하는 도중 장애물을 감지하였을 경우 장애물을 회피할 수 있는 알고리즘을 이용하여 개발하였다. 그리고 주행 시험을 통해 장애물 회피에 관한 테스트를 수행하였다.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.703-706
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• 2009

매년 운전자의 운전미숙과 돌발 상황으로 인한 자동차사고로 인해 수만 명의 사망자와 부상자가 나오고 있으며 이로 인한 경제적 손실도 막대하다. 현재 충돌사고를 방지하기 위해 레이더나 초음파센서와 같은 통신기기를 활용한 충돌회피시스템이 개발되고 있는 상황으로 충돌회피를 위해서는 센서부도 중요한 역할을 하지만 충돌회피 알고리즘 개발이 무엇보다 중요하다. 본 논문에서는 두 개의 초음파 센서를 이용하여 전방에 있는 장애물을 회피할 수 있는 시스템을 구축하였다. 자동차가 자율적인 판단에 따라 충돌 없이 이동할 수 있는 능력을 갖게 하기 위하여 초음파센서와 광센서를 이용하였으며, 라인트레이서와 같이 라인을 따라 자동차가 이동하는 도중 장애물을 감지하였을 경우 장애물을 회피할 수 있는 알고리즘을 이용하여 개발하였다. 그리고 주행 시험을 통해 장애물 회피에 관한 테스트를 수행하였다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.