• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.16 no.5
• /
• pp.38-48
• /
• 2017

Link attributes, such as speed, occupancy, and flow, are essential factors for transportation planning and operation. It is, therefore, one of the most important decision-making problems in intelligent transport system (ITS) to determine the optimal location of a sensor for collecting the information on link attributes. This paper aims to develop a model to determine the optimal location of a sensor to minimize the variability of traffic information on whole networks. To achieve this, a network sensor location model (NSLM) is developed to reflect discrete characteristics of data collection. The variability indices of traffic information are calculated based on the summation of diagonal elements of the variance-covariance matrix. To assess the applicability of the developed model, speed data collected from the dedicated short range communication (DSRC) systems were used in Daegu metropolitan area. The developed model in this study contributes to the enhancement of investment efficiency and the improvement of information accuracy in intelligent transport system (ITS).

• Journal of Korea Society of Industrial Information Systems
• /
• v.20 no.3
• /
• pp.87-93
• /
• 2015

The accidents caused by drivers while driving are considered as the major causes along with other causes such as conditions of roads, weather and cars. In this study, we investigated the driver's workloads under three different driving conditions (Weather, Driving time zone, and Traffic density) through analyzing biological signals obtained from a car driving simulator system. The proposed method is able to detect R waves and R-R interval calculation in the ECG. Heart rate variability (HRV) was investigated for the time domain to determine the changes in driver's conditions.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.23 no.8
• /
• pp.2042-2051
• /
• 1998

VBR compressed, pre-recorded video is known to exhibit significant, multiple time-scale bit rate variability. To reduce the variability and burstiness of the aggregated transmission rate as low as possible, in this paper, we present an adaptive bandwidth smoothing algorithm, that can be effectively applicalble for VoD services over a shared-medium channel. For these environments where many clients are connected to a single server, by introducing the conventional MVBA(minimum variability bandwidth allocation) algorithm and controlling adaptively the conventional MVBA(minimum variability bandwidth asllocation) algorithm and controlling adaptively the aggregated transmission rate whenever a new clients request is arrived at the server side, the proposed algorithm effectively reduces the burstiness and variability of the aggregated transmission rate. Through computer experiments, it is shown that the proposed method perporms better than the convertional non-aggregated bandwidth smoothing schemes in terms of the peak rate, standard deviation, number of rate changes for the aggregated traffic.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.2
• /
• pp.45-56
• /
• 2021

In platoon driving, several autonomous vehicles communicate to exchange information with each other and drive in a single cluster. The platooning technology has various advantages such as increasing road traffic, reducing energy consumption and pollutant emission by driving in short distance between vehicles. However, the short distance makes it more difficult to cope with an emergency accident, and accordingly, it is difficult to ensure the safety of platoon driving, which must be secured. In particular, the unexpected situation, i.e., variability that may appear during driving can adversely affect the safety of platoon driving. Because such variability is difficult to predict and reproduce, preparing safety guards to prevent risks arising from variability is a challenging work. In this paper, we studied a simulation method to avoid the risk due to the variability that may occur while platoon driving. In order to simulate safe platoon driving, we develop diverse scenarios considering the variability, design and apply safety guards to handle the variability, and extends the detail functions of VENTOS, an open source platooning simulator. Based on the simulation results, we have confirmed that the risks caused form the variability can be removed, and safe platoon driving is possible. We believe that our simulation approach will contribute to research and development to ensure safety in platoon driving.

• Journal of Cadastre & Land InformatiX
• /
• v.52 no.2
• /
• pp.153-169
• /
• 2022

With the extension of the average life span and the rapid aging of the population, defining elderly population as a single group is difficult as the physical, economic and social conditions of individual have become different. Therefore, policies that take into account the characteristics of each group are required. The purpose of this study is to classify individual travel types and to analyze the characteristics of each travel type, based on individual public transportation trajectory data as known as smart card data. Among the four classified types, the long-distance low-frequency stay type and the short-range medium-frequency mobile type show external activity traffic characteristics for retirement leisure, while the long-distance high-frequency stay type and the long-distance high-frequency mobile group include regular commuting. Traffic variability and residence areas of stay were identified in terms of each classified travel type. The results of this study provide the important suggestions for establishing a transportation policy that takes into account the characteristics of each type of elderly population in Seoul.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.18 no.1
• /
• pp.56-73
• /
• 2019

Probe-based systems have been gaining popularity in advanced traveler information systems. However, the high possibility of providing inaccurate travel-time information due to the inherent time-lag phenomenon is still an important issue to be resolved. To mitigate the time-lag problem, different prediction techniques have been applied, but the techniques are generally regarded as less effective for travel times with high variability. For this reason, current 5-min aggregated data have been commonly used for real-time travel-time provision on highways with high travel-time fluctuation. However, the 5-min aggregation interval itself can further increase the time-lags in the real-time travel-time information equivalent to 5 minutes. In this study, a new scheme that uses both individual probe and 5-min aggregated travel times is suggested to provide reliable real-time travel-time information. The scheme utilizes individual probe data under congested conditions and 5-min aggregated data under uncongested conditions, respectively. As a result of an evaluation with field data, the proposed scheme showed the best performance, with a maximum reduction in travel-time error of 18%.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.1243-1244
• /
• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Journal of Korean Society of Transportation
• /
• v.22 no.3 s.74
• /
• pp.85-94
• /
• 2004

Loop detection systems have been used in real-time signal control system to collect traffic information for estimating queue lengths. The queue length algorithm uses speed as a key variable estimated from occupancy time and average vehicle length. The measurement of average vehicle length is affected from the lengths of feeder cable, but their effects have not yet been evaluated. In this study, the variability of average vehicle length due to the lengths of feeder cable is assessed through a field study, and a practical guidelines is proposed. By applying this result, the operational performance of real-time signal control system could be improved.