• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.36-54
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• 2017

The purpose of this paper is to develop a multi-sensor fusion-based traffic information acquisition system with robust to environmental changes. it combines the characteristics of each sensor and is more robust to the environmental changes than the video detector. Moreover, it is not affected by the time of day and night, and has less maintenance cost than the inductive-loop traffic detector. This is accomplished by synthesizing object tracking informations based on a radar, vehicle classification informations based on a video detector and reliable object detections of a infrared range finder. To prove the effectiveness of the proposed system, I conducted experiments for 6 hours over 5 days of the daytime and early evening on the pedestrian - accessible road. According to the experimental results, it has 88.7% classification accuracy and 95.5% vehicle detection rate. If the parameters of this system is optimized to adapt to the experimental environment changes, it is expected that it will contribute to the advancement of ITS.

• 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.

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.369-380
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• 1996

Inductive Loop Detector(ILD) has been commonly used in collecting traffic data such as occupancy time and non-occupancy time. From the data, the traffic volume and type of passing vehicle is calculated. To provide reliable data for traffic control and plan, accuracy is required in type recognition which can be utilized to determine split of traffic signal and to provide forecasting data of queue-length for over-saturation control. In this research, a new recognition model issuggested for recognizing typeof vehicle from thecollected data obtained through ILD systems. Two clustering methods, based on statistical algorithms, and one neural network clustering method were employed to test the reliability and occuracy for the methods. In a series of experiments, it was found that the new model can greatly enhance the reliability and accuracy of type recongition rate, much higher than conventional approa-ches. The model modifies the neural network clustering method and enhances the recongition accuracy by iteratively applying the algorithm until no more unclustered data remains.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1070-1081
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• 1996

In this research, a model was developed for estimating the queue length of vehicles, based on occupancy time of each vehicle collected by loop detectors which were setup at the upstream of urban street. The estimation model suggestes a method which minimizes architectural effects of the street, such as existence of pedestrian crossing, for future applications to the field. The estimation model suggested in this research was established based on real traffic data collected at up-stream detectors in Kangnam Subway station, Seoul, and the formula of the model is based on Multi-Polynomial equations. Consequence of the experiments showed that the model can adequately and in real-time mode measure length of the queue which were constructed at the 80 to 90 meters away from the upstream detectors. The estimation accuracy of the model was verified in statistical analysis conducted by regressing analysis and test results in real traffic situation.

• International Journal of Highway Engineering
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• v.14 no.2
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• pp.165-174
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• 2012

In this study, a method for evaluating the performance of speed measurements was developed to assess the qualities of a vehicle detector. The evaluation method considers measurement errors that are reflected in a reference speed. For this, the concept of uncertainty in measurement was applied to the development method. Other factors such as precedent study, statistical processing techniques, and speed measurement performance method of traffic enforcement equipment and vehicle detection systems were also reviewed. Through this process, the problems of the existing evaluation methods were derived and developed for the new performance evaluation method. Vehicle detectors that are installed in the field were evaluated using the traditional assessment methods and the developed method. As a result, for traditional assessment methods, it was found that evaluation criteria are acceptable, while developed method's criteria are not acceptable. This means that traditional assessment methods do not sufficiently consider errors in measurement, so it has potential to over-estimate for performance of evaluation equipment. On the other hand, it was represented that the developed method should include variable factor such as errors in measurement and more precise compared to traditional assessment methods.

• 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.

• 한국ITS학회:학술대회논문집
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• v.2006 no.10
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• pp.37-44
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• 2006

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.41-51
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• 2002

As Advanced Traveler Information System(ATIS) is the kernel of the Intelligent Transportation System, it is very important how to manage data from traffic information collectors on a road and have at borough grip of the travel time's change quickly and exactly for doing its part. Link travel time can be obtained by two method. One is measured by area detection systems and the other is estimated by point detection systems. Measured travel time by area detection systems has the limitation for real time information because it Is calculated by the probe which has already passed through the link. Estimated travel time by point detection systems is calculated by the data on the same time of each. section, this is, it use the characteristic of the various cars of each section to estimate travel time. For this reason, it has the difference with real travel time. In this study, Artificial Neural Networks is used for estimating link travel time concerned about the relationship with vehicle detector data and link travel time. The method of estimating link travel time are classified according to the kind of input data and the Absolute value of error between the estimated and the real are distributed within 5$\~$15minute over 90 percent with the result of testing the method using the vehicle detector data and AVI data of Namsan Tunnel $\#$1. It also reduces Time lag of the information offered time and draws late delay generation and dissolution.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.4
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• pp.564-581
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• 1999

This paper introduces a new, small, low cost, robust and quick replaceable pavement-based vehicle detector using CW radar, magnetometer, and UHF small antennal techniques. The detector has been developed for a replacement of loop detectors having wide surface areas, for a more accurate operation under all weather conditions, and for no algorithmic change of the existing traffic information system. The detected vehicle information is sent by a small helical antenna embedded in a plastic material and received by a 5/8 $\lambda$ long GP antenna for signal processing. In a relatively good weather condition, the detector operates at 24 GHz. But in a heavy rain condition, magnetometer is activated by automatic switching.

• Journal of Advanced Navigation Technology
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• v.7 no.2
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• pp.108-117
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• 2003

In this paper, two types of radar vehicle detectors compatible with currently being used ILD(Inductive Loop Detector) without any modification has been developed. With these vehicle detectors based on FMCW altimeter and Doppler speedometer techniques at 24 GHz, the length and speed of a vehicle can be detected. For signal processing part, we have used DAQ board and programmed with LabView. For compatibility with traffic information network connected with existing ILD's, traffic information has been sent to VDS by using RS-232C standard interface. This development has improved approximately 10% in accuracy in terms of the speed and length information compared with that of the installed ILD in the test field.