• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.34-45
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• 2019

Based on the analysis of congestion conditions in small cities with a population of less than 500,000 in Chungcheong Province, this study conducted a feasibility study on the scope of congestion improvement projects. Further, traffic congestion management standards were further established by analyzing LOS as well as the speed of traffic presented by congestion criteria in the existing Project. According to the analysis according to the congestion management standard LOS suggested in this study, Asan city has the most major number of highways where LOS E occurs for more than 3 hours, followed by Sejong City and Gongju City. In addition, when comparing the ratio of the busy main roads according to the frequency of LOS E by city size, it was analyzed that the ratio and extension of the busy highways relative to the overall extension of the large Chungcheong area are similar to the ratio in small cities in the Chungcheong region. Therefore, traffic congestion occurred in small cities and the feasibility of the improvement project was revealed.

• MALSORI
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• no.51
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• pp.137-149
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• 2004

A weather forecast service with speech recognition is described. This service allows users to get the weather information of all the cities by saying the city names with just one phone call, which was not provided in the previous weather forecast service. Speech recognition is implemented in the intelligent peripheral (IP) of the advanced intelligent network (AIN). The AIN is a telephone network architecture that separates service logic from switching equipment, allowing new services to be added without having to redesign switches to support new services. Experiments in speech recognition show that the recognition accuracy is 90.06% for the general users' speech database. For the laboratory members' speech database, the accuracies are 95.04% and 93.81%, respectively in simulation and in the test on the developed system.

• Journal of the Korean Professional Engineers Association
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• v.42 no.6
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• pp.28-30
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• 2009

There is a proverb in Korea "Don't chase after another." It can be a right proverb in sometimes. However, usually it doesn't fit in these various society, information knowledge society. Modern society requires convergence technology. IBS (Intelligent Building System) requires knowledge of architecture field, electric field, communication field, and computation field. ITS (Intelligent Transport Systems) which is constructing in many cities requires various knowledge as engineering works, electricity, computation, communication and transportation. In the case of u-City, it requires technology of many fields as architecture, electricity, communication, engineering works, transportation, and computation. Anyone who wants to participate in u-City should study and acquire knowledge in various field. Otherwise, it must be failed because of lack of communication like as the Tower of Babel. U-City is not a portion of one field. Therefore, engineers in many fields should cooperate with each other to make u-city as the best product in the world.

• ETRI Journal
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• v.44 no.2
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• pp.194-207
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• 2022

In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of traffic congestion and its adverse effect on smart cities. Edge computing is one such advanced progress facilitating real-time data transmission among vehicles and roadside units to mitigate congestion. An edge computing-based deep reinforcement learning system is demonstrated in this study that appropriately designs a multiobjective reward function for optimizing different objectives. The system seeks to overcome the challenge of evaluating actions with a simple numerical reward. The selection of reward functions has a significant impact on agents' ability to acquire the ideal behavior for managing multiple traffic signals in a large-scale road network. To ascertain effective reward functions, the agent is trained withusing the proximal policy optimization method in several deep neural network models, including the state-of-the-art transformer network. The system is verified using both hypothetical scenarios and real-world traffic maps. The comprehensive simulation outcomes demonstrate the potency of the suggested reward functions.

• Journal of the Semiconductor & Display Technology
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• v.14 no.2
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• pp.29-34
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• 2015

Traffic lights are common in cities and are important cues for the path planning of intelligent vehicles. In this paper, we propose a robust and efficient algorithm for recognizing traffic lights from video sequences captured by a low cost off-the-shelf camera. Instead of using color information for recognizing traffic lights, a shape based approach is adopted. In learning and detection phase, Histogram of Oriented Gradients (HOG) feature is used and a cascade classifier based on Adaboost algorithm is adopted as the main classifier for locating traffic lights. To decide the color of the traffic light, a technique based on histogram analysis in HSV color space is utilized. Experimental results on several video sequences from typical urban environment prove the effectiveness of the proposed algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.5
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• pp.138-150
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• 2009

10 years ago, Korea proclaimed the bike traffic activation law. Many cities have constructed bike paths and parking lots. However, because of increased bike users, lack of bike paths, and parking lots, it is predisposed to avoidance to ride. This study suggests reasonable bike parking lots scale and estimated bike parking lots using the bike traffic assignments. As results, reasonable bike parking lots scale is approximately 5% of car parking lots, which is minimum value. Then, each city can estimate reasonable bike parking lots scale by considering the characteristics and size of cities.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.169-181
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• 2017

As vehicle supply rate increases, traffic jam-related problems emerge and sharing transportation including carpool, centered on the advanced countries, becomes a major interest. This study aims to analyze benefit generated by carpool during the rush hours of medium and long distance travel, focused on the workers of public Agencies relocated to innovation cities. In order to compute benefit, carpool demand of relocated public Agencies was estimated and travel speed was estimated according to reduced traffic volume through carpool adoption using a traffic flow model. The benefit were computed dividing them into direct benefit and indirect benefit. As a result, 23billion KRW and 56.5billion KRW were annually revealed to be generated in terms of direct benefit and indirect benefit. The study result is expected to be used as part of basic research to adopt carpool for future traffic demand management.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.45-58
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• 2021

It is generally known that a two-lane roundabout has some problems in safety such as increasing conflicts, typically merging and diverging conflicts and conflicts between entering traffic and exiting as well as turning traffic. To solve these problems, a turbo-roundabout had been developed and has successfully brought safer and more efficient operation in other countries. In this study, micro simulations using VISSIM were conducted to investigate the maximum value of service traffic volume. It was found that operation of turbo-roundabouts was influenced by traffic volume for each turning traffic, and the maximum values of traffic volume were values between 2,400 and 2,800 vehicles per hour as rates of traffic volume for each turning traffic. Typically, turbo-roundabouts have limited to operate in conditions with more than 30% for left-turning traffic volume.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.42-52
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• 2022

Bus Information System (BIS) services are expanding nationwide to small and medium-sized cities, including large cities, and user satisfaction is continuously improving. In addition, technology development related to improving reliability of bus arrival time and improvement research to minimize errors continue, and above all, the importance of information accuracy is emerging. In this study, accuracy performance was evaluated using LSTM, a machine learning method, and compared with existing methodologies such as Kalman filter and neural network. As a result of analyzing the standard error for the actual travel time and predicted values, it was analyzed that the LSTM machine learning method has about 1% higher accuracy and the standard error is about 10 seconds lower than the existing algorithm. On the other hand, 109 out of 162 sections (67.3%) were analyzed to be excellent, indicating that the LSTM method was not entirely excellent. It is judged that further improved accuracy prediction will be possible when algorithms are fused through section characteristic analysis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.123-140
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• 2022

Even though driving behaviors in underground roads can be significantly different from ground roads, existing underground roads follow the design guidelines of ground roads. In this context, this study investigates the driving behaviors of the exit-ramp terminal of urban underground roads using a driving simulator. Virtual driving experiments were performed by analyzing scenarios between the underground and ground road environments. The experiments' driving behavior data for each geometry section are compared and validated through a statistical significance test. This test showed that the speed in the underground road environment is relatively low, and the LPM tends to move away from the adjacent tunnel wall. Based on these findings, this study suggests implications and feasible solutions for improving driver's safety in the exit-ramp terminal of the underground roads.