• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.97-113
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• 2023

Swift and appropriate responses in emergency situations like objects falling on the road can bring convenience to road users and effectively reduces secondary traffic accidents. In Korea, current intelligent transportation system (ITS)-based detection systems for emergency road situations mainly rely on loop detectors and CCTV cameras, which only capture road data within detection range of the equipment. Therefore, a new detection method is needed to identify emergency situations in spatially shaded areas that existing ITS detection systems cannot reach. In this study, we propose a ResNet-based algorithm that detects and classifies emergency situations from vehicle camera footage. We collected front-view driving videos recorded on Korean highways, labeling each video by defining the type of emergency, and training the proposed algorithm with the data.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.105-118
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• 2001

Incident detection rate and false alarm rate are the key measures tot estimating the performance of automatic incident detection algorithms. It is, however inherently very difficult to improve the two measures simultaneously. The main purpose of this study is to present some methods for solving the problem. For this, an incident detection algorithm has been designed in this study. The algorithm is consisted of two functions, one for detecting incident and another for detecting congestion. A logic for distinguishing non-recurrent congestion from recurrent congestion was employed in the algorithm. The new algorithm basically requires speed, flow, and occupancy data for defining incident situation, but the algorithm is able to perform this task without one of the three parameters. The performance of the algorithm has been evaluated by using the field data collected from Interstate Highway 880 in Bay Area, California. The empirical analysis results are very promising and thus, the algorithm proposed may be very useful for the analysts. This paper presents some empirical test results for the performance of California incident detection algorithm, only for the reference purpose.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.229-239
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• 2008

Most algorithms for detecting incidents have been developed under the premise that congestion must happen whenever an incident occurs. For that reason, the performance of these algorithms could not be guaranteed in cases where congestion did not happen due to traffic operations with low flows despite the occurrence of an incident. The objective of this paper is to develop an automatic incident detection algorithm using a new diagram that can reliably detect the incident under various conditions of traffic operations including a low volume state. Compared with the McMaster Algorithm, the proposed algorithm in this paper was evaluated with three different cases in which the incidents occur in traffic operations with a low volume state, a relatively high volume state, and a recurrent congestion state. It is shown that the new algorithm has a capability to identify the flow characteristics of incidents for all the three cases and is much better than McMaster algorithm in terms of detection rate and false alarm rate.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.581-586
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• 2006

Incidents, which is unexpected unusual events such as traffic accidents, have increased on the most roads in Korea. The obstruction of a fluent traffic flow occurred by incidents causes the traffic congestion and decreases the capacity. The Wavelet technique was applied to detect the road section and the happening time of incidents on urban freeways in this study, and this technique has been widely used in many engineering fields such as an electrical engineering, etc. The availability and validity of the Wavelet technique to the detection of incidents was examined by the occupancy rate, the important element of traffic flows, which is extracted from the data of detectors installed on Seoul Urban freeways. Then, this result is compared to the California Algorithm and the Low-Pass Filtering Algorithm among basic present detection algorithms, which are based on the occupancy rate. As a result, the false alarm rate of this method was similar as that of the California algorithm and the Low-Pass Filtering algorithm, but the detection rate is higher.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.25-39
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• 2018

The purpose of this study is to develop an efficient incident detection algorithm by applying machine learning, which is being widely used in the transport sector. As a first step, network of the target site was constructed with micro-simulation model. Secondly, data has been collected under various incident scenarios produced with combination of variables that are expected to affect the incident situation. And, detection results from both McMaster algorithm, a well known incident detection algorithm, and the Naive Bayes algorithm, developed in this study, were compared. As a result of comparison, Naive Bayes algorithm showed less negative effect and better detect rate (DR) than the McMaster algorithm. However, as DR increases, so did false alarm rate (FAR). Also, while McMaster algorithm detected in four cycles, Naive Bayes algorithm determine the situation with just one cycle, which increases DR but also seems to have increased FAR. Consequently it has been identified that the Naive Bayes algorithm has a great potential in traffic incident detection.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.159-173
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• 2004

This study utilizes the fuzzy logic and genetic algorithm to improve the existing incident detection models by addressing the problems associated with "crisp" thresholds and model transferability (applicability). The model's major components were designed to be a set of the fuzzy inference engines, and for the self-adaptation capability the genetic algorithm was introduced in optimization(or training) of the fuzzy membership functions. This approach is often called "the hybrid of fuzzy-genetic algorithm" The model performance was tested and found to be compatible with that of the existing well-recognized models in terms of performance measures such as detection rate, false alarm rate, and detection time. This study was not an effort for simple improvement of the model performance, but an experimental attempt to incorporate new characteristics essential for the incident detection model to be universally applicable for various roadway and traffic conditions. The study results prove that the initial objective of the study was satisfied, and suggest a direction that the future research work in this area must follow.

• 한국ITS학회:학술대회논문집
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• 2004.11a
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• pp.175-180
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• 2004

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.119-129
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• 2004

This paper is designed to report the results of development and validation procedures in relation to the Freeway Incident Management System (FIMS) prototype development as part of Intelligent Transportation Systems Research and Development program. The central core of the FIMS is an integration of the component parts and the modular, but the integrated system for freeway management. The whole approach has been component-orientated, with a secondary emphasis being placed on the traffic characteristics at the sites. The first action taken during the development process was the selection of the required data for each components within the existing infrastructure of Korean freeway system. After through review and analysis of vehicle detection data, the pilot site led to the utilization of different technologies in relation to the specific needs and character of the implementation. This meant that the existing system was tested in a different configuration at different sections of freeway, thereby increasing the validity and scope of the overall findings. The incident detection module has been performed according to predefined system validation specifications. The system validation specifications have identified two component data collection and analysis patterns which were outlined in the validation specifications; the on-line and off-line testing procedural frameworks. The off-line testing was achieved using asynchronous analysis, commonly in conjunction with simulation of device input data to take full advantage of the opportunity to test and calibrate the incident detection algorithms focused on APID, DES, DELOS and McMaster. The simulation was done with the use of synchronous analysis, thereby providing a means for testing the incident detection module.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.175-196
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• 2004

This study focuses on improving the performance of freeway incident detection by introducing some new measures to reduce false alarms in developing a new incident detection model. The model consists of the 5 major components through which a series of decision makings in determining the given traffic flow condition are made. The decision making process was designed such that the causes of traffic congestions can be accurately classified into several types including incidents and bottlenecks according to their unique characteristics. The model performance was tested and found to be compatible with that of the existing well-recognized models in terms of the detection rate and detection time. It should noted that the model produced much less false alarms than most of the existing models. The study results prove that the initial objective of the study was satisfied as it was an experimental trial to improve the false alarm rate for the incident detection model to be more pactically usable for traffic management purposes.

• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.39-50
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• 2006

This study aims to introduce a basic principle to improve the incident detection algorithm using traffic flow diagrams that can classify traffic states with a high reliability on the basis of the analysis of traffic flow characteristics under the recurrent or incident congestions. It is tried to newly classify the traffic states with the speed-flow and speed-occupancy diagrams. This is because McMaster algorithm has a tendancy on not identifying the traffic states exactly using the flow-occupancy diagram. In this study it shows that the classification of traffic states is applicable to use speed-occupancy relationship Therefore, it is necessary to determine some parameters to correctly classify the areas representing the traffic states and it may be possible to develop a new algorithm to detect the incident with a high reliability.