• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2D
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• pp.217-225
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• 2011

Currently our country has very serious problems of traffic congestion and urban environment due to increasing automobile ownership. Recently, our concern about environmentally sustainable transportation and green transportation is increasing, so the government is pushing ahead the policy of bicycle using activation. So it is needed to develop a model to analyze the service level of bicycle roads more realistically. In this study, a neuro-fuzzy inference model to analyze the service level of bicycle roads was built selecting the width of bicycle roads, the number of conflicts during cycling and pedestrian volume, which have fuzzy characteristics, as input variables. The predictability of the model was evaluated comparing the surveyed and the estimated. The values of the statistics, $R^2$, MAE and MSE were 0.987, 0.142, 0.032. Therefore, It may be judged that the explainability of the model is very high. The service levels of bicyle roads estimated by the model are 1~3 steps lower than KHCM assessments. The reason may be explained that the model estimates the service level considering the width of bicycle roads and the number of conflicts simultaneously besides pedestrian volume.

• 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.21 no.3
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• pp.47-56
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• 2003

Considering the high density developments along the major traffic corridors in Seoul, transit-oriented street designs will be a very effective to control traffic congestion along the corridors. For testing the effectiveness, we selected. for our case study, Kangnam Street, which is one of the most highly developed corridors in Seoul The traffic study on Kangnam street in 2000 shows that the daily average bus speed is 11.73km/h, which is 5km/h lower than the auto speed. The Central Bus Lane system was applied on the Kangnam street to test impact on bus speed as well as auto speed. Simulation results show that with Central Bus Lane have been improved the travel speeds of bus as well as auto on Kangnam street from 14.4km/hr to 35.0km/hr and from 25.1km/hr to 26.1km/hr, respectively. The bus market share increases about 6-8 percentages. Especially, 13.4% of bus users are increased for long-distance trips.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.221-230
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• 2021

The study attempts to suggest potential problem and solutions expected in the process of implementing KASS, which is currently under development to improve the domestic navigation safety system, and to summarize improvement effects of domestic navigation safety system anticipated by the implementation of KASS. Challenges expected in the process of implementing KASS exists in four aspects: emotional, technical, cost, safety aspects. When KASS is implemented and operates, various benefits can be realized. Benefits include cost savings by not using navigation safety systems during takeoff and landing; reduction of flight delays and cancellations by removing airway congestion; increase of aircraft accommodation capacity; reduction of carbon emissions; preparation for future aviation demands and improvement of air transportation safety; and reduction of flight accidents. In conclusion, it is expected to enter into an era of more intense competition due to increased aviation demands. In order to survive in this competitive environment, early introduction of KASS is indispensable. Analysis results of this study are expected to provide reference information for academic research in this area. A possible future research topic include a study predicting the changes in the navigation safety systems introduced by KASS and proposing practical and useful ways to respond the changes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.125-132
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• 2017

The development project of Busan New Port aims to be Logistics Hub Port but there are too many things to deal with ; enlargement of harbour, interport competition, modernization of harbour loading equipment and so on. At present, 23 berths of North and South container quay are in operation and 22 berths will be constructed on west and south-side by 2020. Namely, Busan New Port will operate 45 berths in 2020. When it comes to port distripark, a large-scale of Port distripark project is underway, such as Ung-Dong district 1,2 phase, West container 1,2phase, North distripark and so on. This study is to deduce traffic system problem of Busan New Port which is caused by the development project through predicting traffic need considering the development project. According to study, there are three main problems of traffic system : 1. traffic congestion caused on main crossroad, connecting second harbour back road. 2. It has been predicted that South-North road and traffic capacity of New Port road would lack compared to traffic volume-to-be-increased. Moreover, the detour volume of traffic is caused because New Port's 1st avenue and route 2 were not connected directly. Thus, this study suggests three kinds of improvement plan for smoother traffic flow. 1st. Operate roundabout on major intersection, for example, second harbour back road, west container wharf's subway corridors(South to North), and permit only right turn on sub-intersection. 2nd. Extend New Port road(North container's port road) by utilizing side walk and median. 3rd. Install exit ramp which utilizes Route 2 connecting New Port's 1st avenue and local road 1042. The method we used to analyze the effect of improvement is Vissim of Mircro Simulation Package.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.