• Information Systems Review
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• v.25 no.4
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• pp.113-130
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• 2023

Due to economic development and increasing gross national income, the number of automobiles continues to rise, leading to a serious issue of illegal parking due to limited road conditions and insufficient parking facilities. Illegal parking causes significant inconvenience and displeasure to people and can even result in accidents and loss of lives. The severity of accidents and their consequences, related to the growing number of vehicles and illegal parking, is escalating, particularly in the metropolitan areas. Consequently, efforts are being made to address this problem as a cause of social issues and come up with measures to reduce illegal parking. In particular, half of the public complaints in the metropolitan area are related to illegal parking, and the highest physical and human damage occurs in Gyeonggi. Thus, this study aims to use machine learning techniques based on data related to illegal parking in Suwon city, Gyeonggi, to categorize regional characteristics and propose effective measures to crack down on illegal parking. Additionally, practical, social, policy, and legal measures to decrease illegal parking in the metropolitan area are suggested. This study has academic significance in that it solved the problem of illegal parking, which is mentioned as one of the social problems that cause traffic congestion, by classifying regional characteristics using K-prototype, a machine learning algorithm. Furthermore, the results of this study contribute to practical and social aspects by providing measures to decrease illegal parking in the metropolitan area.

• Journal of Urban Science
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• v.6 no.2
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• pp.29-34
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• 2017

Although the supply of public parking lots in major cities is steadily increasing, there is still a shortage of parking spaces that take into account the characteristics of actual available parking spaces. These parking problems are caused by conflicts between users, illegal occupation and privatization of roads, interference with traffic roads, and business stagnation in commercial areas and illegal parking problems. In addition, despite various parking demand management policies and continuous supply of public parking facilities, the solution to the parking problem is uneasy due to increased construction costs and changes in social conditions. In order to solve this problem, it was judged that it would be necessary to utilize the existing public parking lots efficiently. Therefore, we collect the variables expected to affect the turnover, and use multiple regression models. The results are summarized as follows. Firstly, public parking lots can be classified into four types using utilization rate and turnover rate. Secondly, influencing factors are found including index of public transportation usage convenience, index of illegal parking, and land use characteristics in central commercial district. Thirdly, it was shown that there was little impact by the size of public parking lots, separation distance to subway distance, separation distance to bus stops, transfer zone, residential zone, and second-rated lots by parking costs. Finally, it is found that public parking lots can be improved by proving accessibility of public transit, enforcement of illegal parking, active approaches supporting public parking lots. It is also recommended that public parking cost rating system based mainly on land use characteristics should be remedied and rearranged.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.13-20
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• 2016

In this study, we propose a method to regulate parking violations using computer vision technology. A still color image of the parked vehicle under question is obtained by a camera mounted on enforcement vehicles. The acquired image is preprocessed through a morphological algorithm and binarized. The vehicle's shadows are detected from the binarized image, and lanes are identified using the information from the yellow parking lines that are drawn on the load. Whether parking is illegal is determined by the conformity of the lanes and the vehicle's shadow.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.59-64
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• 2024

Recently, research on the convergence of drones and artificial intelligence technologies have been conducted in various industrial fields. In this paper, we propose an illegal parking vehicle recognition model using deep learning-based object recognition and classification algorithms. The model of object recognition and classification consist of YOLOv8 and ResNet18, respectively. The proposed model was trained using image data collected in general road environment, and the trained model showed high accuracy in determining illegal parking. From simulation results, it was confirmed that the proposed model has generalization performance to identify illegal parking vehicles from various images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.396-402
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• 2021

As the world is trying to establish smart city, unmanned vehicle control systems are being widely used. This paper writes about an unmanned parking control system that uses an adaptive background image modeling method, suggesting the method of updating the background image, modeled with an adaptive Gaussian mixture model, in both global and local way according to the moving object. Specifically, this paper focuses on suggesting two methods; a method of minimizing the influence of a moving object on a background image and a method of accurately updating the background image by quickly removing afterimages of moving objects within the area of interest to be monitored. In this paper, through the implementation of the unmanned vehicle control system, we proved that the proposed system can quickly and accurately distinguish both moving and static objects such as vehicles from the background image.

• Journal of Korean Society of Transportation
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• v.18 no.6
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• pp.7-17
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• 2000

The Purpose of this study is to test the effectiveness of Special Transportation Management Zone system (TZ) when it is applied to Dongdaemoon commercial zone located in downtown Seoul. The study consists of two Parts ; the legal components of TZ and the case study on Dongdaemoon district. The case study includes the analysis of current traffic situation on the study zone, the design of implemention alternative for each component of TZ, the framework of impact analysis system, and the analysis results. The study finds, if 2,000 won mandatory parking fee on drivers parking in the buildings only with over 10 parking spaces and over 3.000$m^2$ in floor area, strong illegal parking enforcement, and Pro-hibition of parking passenger cars whose last digit plate number matches with the last digit of date, are implemented in the zone simultaneously, the traffic speed increases to 20.56km/h which is above the target set in the zone. In conclusion, we find that TZ2 can be an effective tool relieving traffic congestion in Seoul, and that it is necessary for introducing TZ in Seoul that relevant laws should be revised and expecting problems should be overcome.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.1-8
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• 2011

Automatic vehicle license plate recognition technology is widely used in gate control and parking control of vehicles, and police enforcement of illegal vehicles. However inherent geometric information of the license plate can be transformed in the vehicle images due to the slant and the sunlight or lighting environment. In this paper, a distortion invariant vehicle license plate extraction and recognition algorithm is proposed. First, a binary image reserving clean character strokes can be achieved by using a DoG filter. A plate area can be extracted by using the location of consecutive digit numbers that reserves distortion invariant characteristic. License plate is recognized by using neural networks after geometric distortion correction and image enhancement. The simulation results of the proposed algorithm show that the accuracy is 98.4% and the average speed is 0.05 seconds in the recognition of 6,200 vehicle images that are obtained by using commercial LPR system.