• Title/Summary/Keyword: Neural network incident detection algorithm

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Development of Automatic Incident Detection Algorithm Using Image Based Detectors (영상기반의 자동 유고검지 모형 개발)

  • 백용현;오영태
    • Journal of Korean Society of Transportation
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    • v.19 no.6
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    • pp.7-17
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    • 2001
  • The purpose of this paper is to develop automatic incident detection algorithm using image based detector in freeway management system. This algorithm was developed by using neutral network for high speed roadway and by using speed and occupancy variable for low speed roadway. The image detector system with the developed automatic incident detection algorithm can detect multi-lane as well as several detect areas for each lane. To evaluate this system, field tests to measure the detecting rate of incidents were performed with other systems which have APID and DES algorithm at high speed roadway(freeway) and low speed roadway(national arterial). As the results of field test, it found that the detect rate of this system was highest rate comparing to other two systems.

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A Travel Speed Prediction Model for Incident Detection based on Traffic CCTV (돌발상황 검지를 위한 교통 CCTV 기반 통행속도 추정 모델)

  • Ki, Yong-Kul;Kim, Yong-Ho
    • Journal of Industrial Convergence
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    • v.18 no.3
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    • pp.53-61
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    • 2020
  • Travel speed is an important parameter for measuring road traffic and incident detection system. In this paper I suggests a model developed for estimating reliable and accurate average roadway link travel speeds using image processing sensor. This method extracts the vehicles from the video image from CCTV, tracks the moving vehicles using deep neural network, and extracts traffic information such as link travel speeds and volume. The algorithm estimates link travel speeds using a robust data-fusion procedure to provide accurate link travel speeds and traffic information to the public. In the field tests, the new model performed better than existing methods.

An Automatic Pattern Recognition Algorithm for Identifying the Spatio-temporal Congestion Evolution Patterns in Freeway Historic Data (고속도로 이력데이터에 포함된 정체 시공간 전개 패턴 자동인식 알고리즘 개발)

  • Park, Eun Mi;Oh, Hyun Sun
    • Journal of Korean Society of Transportation
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    • v.32 no.5
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    • pp.522-530
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    • 2014
  • Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

A preliminary study for development of an automatic incident detection system on CCTV in tunnels based on a machine learning algorithm (기계학습(machine learning) 기반 터널 영상유고 자동 감지 시스템 개발을 위한 사전검토 연구)

  • Shin, Hyu-Soung;Kim, Dong-Gyou;Yim, Min-Jin;Lee, Kyu-Beom;Oh, Young-Sup
    • 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.