• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.106-112
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• 2017

In the paper, we propose a levitation and a propulsion controller for the magnetic levitation logistic transportation system. The levitation controller is designed considering the mutual influence of the electromagnets to minimize roll and pitch movements. In order to solve the structural disadvantages of the magnetic levitation transportation system, we improve the problem of the existing controller by applying the exponential filter to the reference input. DSP-based control hardware is developed and the levitation control method is verified by levitation experiments to the air gap goal. The propulsion controller uses the space vector voltage modulation method. The propulsion controller is designed to follow the position and velocity profile by detecting the absolute position from the bar code information attached to the rail. The position control result shows satisfactory performance through the propulsion control reciprocating motion experiment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.167-189
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• 2022

In the case of SAE autonomous driving levels 2 and 3, since complete autonomous driving is impossible, the take-over process is essential, and take-over time(TOT) is the most important factor in determining the safety of the autonomous driving system. Accordingly, research on TOT is being actively conducted, but each research is independently conducted and general conclusions that integrate various research results are required. Therefore, in this study, the factors affecting TOT were analyzed using meta-analysis, which integrates the results of individual studies and presents an integrated opinion. As a result of meta-analysis, a total of 10 influencing factors were selected, and most of them were related to the non-driving related task(NDRT) type. In addition, implications for the future research direction of take-over and NDRT were presented.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.1-17
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• 2022

Accurate analysis of the causes of metro rail traffic congestion provides a means of addressing issues arising from metro rail traffic congestion in metropolitan areas. Currently, congestion analysis based on counting, weight detection, CCTVs, and mobile Wi-Fi is limited by poor accuracies or because studies have been restricted to single routes and trains. In this study, a train congestion analysis model was used that includes the transfer and multi-path behavior of metro passengers and train operation plans for metropolitan urban railroads. Analysis accuracy was improved by considering traffic patterns in which passengers must wait for next trains due to overcrowding. The model updates train crowding levels every 10 minutes, provides information to potential passengers, and thus, is expected to increase the social benefits provided by the Seoul metropolitan subway

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3B
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• pp.485-492
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• 2010

Intelligent Transport System(ITS) has been studied in various systems, such as road environment information offering, vehicle short-range wireless/wire communication, vehicle collision preventing and pedestrian safety offering systems. Related to this, the USN technology based on the sensing accuracy for motorists and pedestrians safety, the information reliability, the maintenance and convenience for Sensor Network is highlighted. This study uses various sensors to construct USN to the road, and connect it to the developed RSU so it collects the real-time road environment information and offers it to OBU and Traffic Control Surveillance Center with Road Weather Information System. RSU collects roadside information for driver's safety and analyzes it to offer IP and beacon service according to the service priority to OBU & upper layer terminal. In the upper layer terminal it is developed the IP based Settop Box application program to offer the urban traffic information & road environment, and environment sensor error, etc. Finally, RWIS develops the real-time collection of roadside information to complement the driver's safety to the intelligent traffic system, and presents various service modes with technology convergence.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.182-192
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• 2022

Motorcycle accidents are increasing at an average annual rate of 10.01%, and fatalities are also increasing by 2.64%. Enforcement cameras are installed to enforce safe driving of more-than or equal-to four-wheeled vehicles on the road. Even though the main purpose of this enforcement camera is to disencourage the speed violation of all types of vehicle, one cannot expect complete enforcement by these cameras. In particular, enforcement of the motorcycle should rely on on-site activities through the input of on-site personnel. Recently, to discourage the illegal acts of motorcycling, the National Police Agency introduced the 'National Police Agency SMART National Report'. However, it is necessary to prepare an appropriate practical plan to maximize the effect of enforcement requiring continuous manpower management. This study proposed four types of rider certification IDs for delivery motorcycles. These IDs are institutional devices to manage delivery motorcycle riders. In addition, a experiment on enforcement was conducted using those license ID systems for delivery motorcycles. This experiment confirmed through the image-processing program (D-MESO) if one of the systems was possible to implement for enforcement on the delivery motorcycle rider's license.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.14-25
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• 2018

In recent years, it has become technically easier to explain factors related with traffic accidents in the Big Data era. Therefore, it is necessary to apply the latest analysis techniques to analyze the traffic accident data and to seek for new findings. The purpose of this study is to compare the predictive performance of the negative binomial regression model and the deep learning method developed in this study to predict the frequency of traffic accidents in expressways. As a result, the MOEs of the deep learning model are somewhat superior to those of the negative binomial regression model in terms of prediction performance. However, using a deep learning model could increase the predictive reliability. However, it is easy to add other independent variables when using deep learning, and it can be expected to increase the predictive reliability even if the model structure is changed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.98-112
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• 2018

Intentional aggressive driving (IAD) is a very dangerous driving behavior that threatens to attack the adjacent vehicles. Most existing studies have focused on the independent driving characteristics of attack drivers. However, the identification of interactions between the offender and the victim is necessary for the traffic safety analysis. This study established multi-agent driving simulation environments to systematically analyze vehicle interactions in terms of traffic safety. Time-to-collision (TTC) was adopted to quantify vehicle interactions in terms of traffic safety. In addition, a exponential decay function was further applied to compare the overall pattern of change in crash potentials when IAD events occurred. The outcome of this study would be useful in developing policy-making activities to enhance traffic safety by reducing dangerous driving events including intentional aggressive driving.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.58-72
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• 2018

Ganghwa has met a new development period in land use and infrastructure based on the 4th National Development Planning, however the public transportation system is not systematically operated yet. This paper analyzes the bus trip pattern in Ganghwa using transportation card data during a week. The result indicates that average 7,100 people use buses a day and the most frequent use occurred in Friday. Clear peak-hours between 7 and 8 A.M. and between 4 and 5 P.M. were appeared due to commuting and school trips. According the result of regression analysis, population and the number of hospitals and schools area showed positive relationships with but trips reflecting regional characteristics. The research contributes to providing basic data for constructing an efficient public transportation system in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.1
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• pp.50-63
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• 2017

While transit is inconvenient, it is also inevitable for the efficient public transportation. Reducing the number of transfers as much as possible is most important in providing the convenience of public transportation and facilitating the public transportation. As for the public transportation card data, 61,986 items on weekdays and 69,100 items on weekends were collected. Pattern analysis and traffic influence factors were analyzed using traffic data card. Trip chain results revealed that people have more transit transfers for shopping and leasure than commuting purposes on weekends and that commuting distance and time increase by 10 km and 9.9 minutes, respectively. Besides, results of the structural equation model showed that factor 1(total travel time, total travel distance), factor 2(number of people getting on and off), factor 3(transit time), and factor 4(number of bus connections, number of operations) were found to have significant effects on the number of transfers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.11-30
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• 2019

Smartcards are used as the basic data for utilizing the various transportation policies and evaluations, etc. and provided the transportation basic statistics index. However, the main problem of the smartcard data is that the most of users do not take the alighting tag at the stop, so there is a limit to the scope of use for the total O-D trip data because incomplete O-D traffic data of transportation card users. In this study, a decision tree of learning model is estimated for the alighting stop of smartcard users. The model estimation accuracy in range less than 2 stops interval was 89.7% on average. By eliminating the incompleteness alighting stop of smartcard data through this model, it is expected to be used as the basic data for various transportation analyses and evaluations.