• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1325-1334
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• 2016

The video incident detection System is a probe for rapid detecting the walker, falling, stopped, backwards, smoke situation in tunnel. Recently, the importance is increases from the downtown double deck tunnel in great depth underground space[1], but the legal basis is weak and the vulnerable situation experimental data. So, In this paper, we introduce a long-term log data analysis information in the tunnenl video incident detection system installed and experimental results in order to verify the feasibility of apply to video incident detection system for the double deck tunnel. It is proposed a few things about derives the problem of existing video incident detection system, improvements and reflect attributes for double deck tunnel. The contents described in this paper will contribute to refine the prototype of video incident detection system will apply to future double deck multi-layer tunnels.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.320-323
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• 2014

Today is going with the advancement of intelligent transportation systems and traffic environment and helping to provide safe and convenient service through a mobile device work with the popularization of the vehicle black box. The traffic flow by a variety of causes is constantly changing, it is often unable to prepare the driver, depending on external factors can not be controlled by the power of the public, leading to a major accident. The system needs to pass the real-time weather data in the inter-operator to prevent this. The proposed detection algorithm weather information delivered real-time weather information for this paper. The weather condition is detected by using the contrast between the histogram of the motion of the wiper and the clear day algorithm. In general, the wiper is worked in extreme weather conditions that will have a value different contrast due to rain or snow. Situation was considered clear, snowy conditions, such as using it on a rainy situation. First, designated as ROI (Region Of Interest) of the minimum area that can be detected in order to reduce the amount of calculation for the wiper, the wiper, which was detected through the operation of the threshold Thresholding the brightness of the vehicle wiper. In addition, we distinguish the value of each meteorological situation by using contrast. Results was obtained to 80% for the snow conditions, a rainy situation.

• Journal of Korean Society of Transportation
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• v.25 no.5
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• pp.23-32
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• 2007

Variable message sign (VMS), which is used for providing real-time information on traffic conditions and incidents, is one of the important components of intelligent transportation systems. VMS messages need to meet the requirements with the consideration of human factors that messages should be readable and understandable while driving. This study developed a legibility distance model for VMS messages using in-vehicle differential global positioning data (DGPS). Traffic conditions, highway geometric conditions, and VMS message characteristics were investigated for establishing the legibility model based on multiple linear regression analysis. The height of VMS characters, speed, and the number of lanes were identified as dominant factors affecting the variation of legibility distances. It is expected that the proposed model would play a significant role in designing VMS messages for providing more effective real-time traffic information.

• Journal of Korean Society of Transportation
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• v.21 no.2
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• pp.63-70
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• 2003

Real-time information on vehicle queue at intersections is essential for optimal traffic signal control, which is substantial part of Intelligent Transport Systems (ITS). Computer vision is also potentially an important element in the foundation of integrated traffic surveillance and control systems. The objective of this research is to propose a method for detecting an exact queue lengths at signalized intersections using image processing techniques and a neural network model Fuzzy ARTMAP, which is a supervised and self-organizing system and claimed to be more powerful than many expert systems, genetic algorithms. and other neural network models like Backpropagation, is used for recognizing different patterns that come from complicated real scenes of a car park. The experiments have been done with the traffic scene images at intersections and the results show that the method proposed in the paper could be efficient for the noise, shadow, partial occlusion and perspective problems which are inevitable in the real world images.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.955-961
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• 2010

In this paper, we describe a study of a vehicle detection method based on a Boosting Classifier which uses Histogram of Oriented Gradient (HOG) features and Image Segmentation techniques. An input image is segmented by means of a split and merge algorithm. Then, the two largest segmented regions are removed in order to reduce the search region and speed up processing time. The HOG features are then calculated for each pixel in the search region. In order to detect the vehicle region we used the AdaBoost (adaptive boost) method, which is well known for classifying samples with two classes. To evaluate the performance of the proposed method, 537 training images were used to train and learn the classifier, followed by 500 non-training images to provide the recognition rate. From these experiments we were able to detect the proper image 98.34% of the time for the 500 non-training images. In conclusion, the proposed method can be used for detecting the location of a vehicle in an intelligent vehicle control system.

• KIISE Transactions on Computing Practices
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• v.22 no.9
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• pp.405-418
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• 2016

Recently, many researchers have studied the VSL decision method using traffic information in multiple detector zones. However, this method selects incorrect VSL starting points, leading to the selection of the wrong speed control zone and calculation of the wrong VSL, causing traffic congestion. Eventually, the Unstable VSL system causes more congestion on the freeway. This paper proposes an improved VSL algorithm stably operated in multiple detector zones on the Korea highway. The proposed algorithm selects a preliminary VSL start station (VSS) expected to end the congestion using the acceleration of stations. It also determines the VSS at each congestion area. Finally, it calculates the VSL relative to the determined VSS and controls the vehicles that enters the traffic congestion zone. The developed strategy is compared with Real-time Variable Speed Limits for Urban Freeway (RVSL) to test the stability and efficiency of the proposed algorithm. The results show that the proposed algorithm resolves the problems of the existing algorithm, demonstrated by the correct VSS decision and the reduction of total travel time by 1-2 minutes.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5135-5142
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• 2011

A flexible mechanism is proposed in this paper to improve the dynamic response performance of a traffic flow control system in an urban area. The roads, vehicles, and traffic control systems are all modeled as intelligent systems, wherein a wireless communication network is used as the medium of communication between the vehicles and the roads. The necessary sensor networks are installed in the roads and on the roadside upon which reinforcement learning is adopted as the core algorithm for this mechanism. A traffic policy can be planned online according to the updated situations on the roads, based on all the information from the vehicles and the roads. This improves the flexibility of traffic flow and offers a much more efficient use of the roads over a traditional traffic control system. The optimum intersection signals can be learned automatically online. An intersection control system is studied as an example of the mechanism using Q-learning based algorithm, and simulation results showed that the proposed mechanism can improve the traffic efficiency and the waiting time at the signal light by more than 30% in various conditions compare to the traditional signaling system.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.223-238
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• 2015

Ramp metering is the most effective and direct method to control a vehicle entering the freeway. This paper proposed the new density-based ramp metering method. Existing methods that use the flow data had low reliability data and can have various problems. Also, when the ramp metering was operated by freeway congestion, the additional congestion and over-capacity can occur in the ramp. To solve this problem with the existing method, the proposed method used the density and acceleration data of the freeway and considered the ramp status. The developed strategy was tested on Trunk Highway 62 west bound (TH-62 WB) in Minnesota Twin-City and compared with Stratified Zone Metering(SZM), which had been operating in the Twin-City freeway. To constitute the experiment environment, the VISSIM simulator was used. The Traffic Information and Condition Analysis System (TICAS) was developed to control the PTV VISSIM simulator. The experiment condition was set between 2:00 PM and 7:00 PM, Oct 5th, 2014 during severe traffic congestion. The simulation results showed that total travel time was reduced by 20% for SZM. Thus, we solved the problem of ramp congestion and over-capacity.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.962-974
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• 2010

Recently, the convergence of information technology with bio-technology, nano-technology or other technologies has been creating a new paradigm. In a field of transportation, the intelligent transport systems which is a convergence of intelligent technologies and transportation systems have been studied. The Variable Speed Limit(VSL), is one of ITS technologies, is thought to improve safety and efficiency of transportation while controlling speed limit based on road conditions. Legacy studies have considered only one station for VSL algorithm. However, it is not appropriate for an urban freeway installed with many stations. In this paper, new algorithm is proposed to not only enhance effectiveness of VSL based on cooperation of stations but also reflect road conditions within 30 seconds. The proposed algorithm consists of 4 steps: the first is a "searching bottleneck station" step, the second is a "calculating a size of congestion" step, the third is a "calculating the number of controlled stations" step, the final is a "calculating VSL" step. This algorithm guarantees improved safety and minimum additional travel time. The travel time should be considered because drivers would against the VSL algorithm when the proposed algorithm occurs additional travel time. In our experiments, microscopic traffic simulator VISSIM is selected to perform a modeling work. The results show that proposed algorithm provides the improved safety and minimum increase of travel time.