• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.31-39
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• 2012

This study aims to suggest a methodology of localizing and calibrating parameters, such as acceleration, deceleration, and lane changing which are the basis of car following model in interrupted traffic flow to overcome the limitation of origin and destination based transportation simulation and to verify the application of activity-based model for use in Korean roadway condition in a large scale area or a city. Especially, we figured out that a proper cell size reflecting Korean traffic conditions is 1.0m rather than 7.5m which is default size and a methodology of tracking vehicle behavior characteristics through tracking vehicle ID is suggested on this study. In addition, vehicle running characteristics in real interrupted traffic flow is analyzed through subdividing vehicle types and updating vehicle type ratio. For verification of suggested model, some portion of Dalgubyul-ro in the Daegu city is tested, and the possibility of realization of interrupted traffic flow in simulation is studied.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.80-95
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• 2019

Recently, with the development of sensor and communication technology, researchers at home and abroad have actively conducted research on methodologies for determining maintenance through diagnosis and prediction techniques by collecting information on the status of equipment or systems. Based on the status of vehicle parts at this point in time, this study presented a system framework for making maintenance decisions by predicting the change in vehicle part status to a future date based on the current state of vehicle parts. In addition, condition diagnosis and predictive data adjustment was configured through tracking the status of vehicle parts before and after maintenance activities. We hope that the application of the results of this study will contribute a little to the safety of citizens using public buses and to the activation of the condition-based maintenance system of vehicles.

• Steel and Composite Structures
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• v.31 no.6
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• pp.629-640
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• 2019

The investigation of retaining wall structures behavior under dynamic loads is considered as one of important parts for designing such structures. Generally, the performance of these structures is under the influence of the environment conditions and their geometry. The aim of this research is to design retaining wall structures based on smart and optimal systems. The use of accuracy and speed to assess the structures under different conditions is one of the important parts sought by designers. Therefore, optimal and smart systems are able to have better addressing these problems. Using numerical and coding methods, this research investigates the retaining wall structure design under different dynamic conditions. More than 9500 models were constructed and considered for modelling design. These designs include height and thickness of the wall, soil density, rock density, soil friction angle, and peak ground acceleration (PGA) variables. Accordingly, a neural network system was developed to establish an appropriate relationship between data to obtain safety factor (SF) of retaining walls under different seismic conditions. Different parameters were analyzed and the effect of each parameter was assessed separately. According to these analyses, the structure optimization was performed to increase the SF values. The optimal and smart design showed that under different PGA conditions, the structure performance can be appropriately improved while utilization of the initial (or basic) parameters leads to the structure failure. Therefore, by increasing accuracy and speed, smart methods could improve the retaining structure performance in controlling the wall failure. The intelligent design process of this study can be applied to some other civil engineering applications such as slope stability.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.111-120
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• 2018

This study focuses on utilizing drones for performance evaluation of ITS detectors and analyzing economic feasibility when performance evaluation is conducted by the traffic management center's own personnel using drones. The study sites were selected from DSRC, video detector, and radar detector locations and drone filming was conducted to obtain travel speed, queue length, and delay time and compare with the detector data. It was shown that drones can be very effectively used to evaluate performance of major ITS detectors such as DSRC and video detectors. In addition, it was analyzed that a drone operated by the traffic management center's own personnel provides very economic solution for ITS detector performance evaluation when compared to consignment by external agencies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.153-172
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• 2018

Overloading of Commercial vehicles have been an important area of transportation as one of the main causes of pavement damage, bridge collapse, severe traffic accident, etc. In this study, we analyzed the effects of overweight prevention by analyzing overweight driving patterns and using weight sensors. First, we analyzed relevant literatures of overweight and surveyed the commercial weight sensors. Then we chose the typical type of overweight vehicles based of overweight enforcement data analysis. MEMs inclinometer weight sensor were installed to 10 test vehicles and data was collected by weight sensors and gps in real time. As a result of gross vehicle weight and axle weight analysis, it was found weight sensor could decrease overweight rate. However, since the number of samples of test vehicles is insufficient to represent the whole commercial vehicle, further studies are deemed possible through the extension test.

• Composites Research
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• v.32 no.4
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• pp.171-176
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• 2019

In this paper, the strength of the composite securement device was characterised by FE analysis. Preliminary frontal crash analysis for the vehicle, equipped with the conventional steel securement device, was carried out according to the ISO 10542 for special transportation to obtain loading data, which were applied to securement device during crash. The securement device consists of block, guide and rail and the weight fraction of rail was the highest among them, therefore, it is desirable to reduce weight of rail by applying carbon/epoxy composite. Also, it was found that 27% of lightweight effect was obtained by hybrid rail that bottom part was replaced by a composite compared to the conventional rail, i.e., made of SAPH 440, without sacrificing the structural strength.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.142-155
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• 2019

This study was conducted with the aim of developing a system to collect taxi GPS probe information to prevent link defects and to improve the accuracy of the standard link-based travel speed by determining when to go into and come out the link. For this purpose, a framework and algorithm consisting of a five-step process for standard link-based map matching and individual vehicle travel speed are presented and used it to calculate the average travel speed of the service link. Two on-site surveys of Teheran and Hakdong-ro were conducted to verify the results by the methods proposed in this paper. On the basis of the overall time of the field survey, the deviation in the travel speed was 0.2 km/h and 0.6 km/h, the accuracy was 99% and 96%, and the MAPE(Mean Absolute Percentage Error) was 1% and 4% in Teheran and Hakdong-ro, respectively. These results were more accurate thand those obtained using conventional methodologies without standard links.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.61-73
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• 2020

Real-time location-sensing technology using location information collected from IoT devices is being applied for safety management purposes in many industries, such as ports. On the other hand, positional error is always present owing to the characteristics of GPS. Therefore, accident-risk detection algorithms must consider positional error. This paper proposes an methodology of calibration for falling object accident-risk-zone approach detection algorithm considering GPS errors. A probability density function was estimated, with positional error data collected from IoT devices as a probability variable. As a result of the verification, the algorithm showed a detection accuracy of 93% and 77%. Overall, the analysis results derived according to the GPS error level will be an important criterion for upgrading algorithms and real-time risk managements in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.74-87
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• 2020

Overloaded trucks are the main source that threatens road safety and directly affects the reduction of pavement life. The On-board truck scale is the only equipment that could prevent overloading by measuring and adjusting the loading weight before driving. Legislation is needed to encourage its installation so that the driver can prevent overloading. In this study, an on-board truck scale system was installed on 30 dump trucks for transporting construction waste, such as soil and aggregates, which are major loads of 36.55% for overloading, and the trucks were monitored remotely. The overload prevention effect was analyzed by comparing driving data for 1 month before distribution of the weight display app that can recognize the weight to the driver and 1 month after distribution. After installation, overloading could be 6.1% reduced, and the transportation efficiency could be increased by checking the weight provided from the On-board truck scale system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.107-117
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• 2020

With the recent development of autonomous vehicles, various sensors installed in vehicles have become common, and big data generated from those sensors is increasingly being used in the transportation field. In this study, we proposed a grid index method to efficiently process real-time vehicle sensing big data and public data such as road weather. The applicability and effect of the proposed grid space division method and grid ID generation method were analyzed. We created virtual data based on DTG data and mapped to the road link based on coordinates. As a result of analyzing the data processing speed in grid index method, the data processing performance improved by more than 2,400 times compared to the existing link unit processing method. In addition, in order to analyze the efficiency of the proposed technology, the virtually generated data was mapped and visualized.