• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.111-121
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• 1999

From the traffic analyses, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results obtained: ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy.ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period shown 10mph decrease when compared with the 24hours' average speed, but the speed did not show a big difference in the afternoon peak period.ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge sectionⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.463-476
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• 2022

After industrialization in the past, the automobile industry has continued to grow centered on internal combustion engines, but is facing a major change with the recent 4th industrial revolution. Most companies are preparing for the transition to electric vehicles and autonomous driving. Therefore, in this study, topic modeling was performed based on LDA algorithm by collecting 4,002 domestic papers and 68,372 overseas papers that contain keywords related to CASE (Connectivity, Autonomous, Sharing, Electrification), which represent future automobile trends. As a result of the analysis, it was found that domestic research mainly focuses on macroscopic aspects such as traffic infrastructure, urban traffic efficiency, and traffic policy. Through this, the government's technical support for MaaS (Mobility-as-a-Service) is required in the domestic shared car sector, and the need for data opening by means of transportation was presented. It is judged that these analysis results can be used as basic data for the future automobile industry.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.14-28
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• 2015

Numerous research has been conducted using internet search data since the mid-2000s. For example, Google Inc. developed a service predicting influenza patterns using the internet search data. The main objective of this study is to prove the hypothesis that highway traffic indices are similar to the internet search patterns. In order to achieve this objective, a model to predict the number of vehicles entering the expressway and space-mean speed was developed and the goodness-of-fit of the model was assessed. The results revealed several findings. First, it was shown that the Google search traffic was a good predictor for the TCS entering traffic volume model at sites with frequent commute trips, and it had a negative correlation with the TCS entering traffic volume. Second, the Naver search traffic was utilized for the TCS entering traffic volume model at sites with numerous recreational trips, and it was positively correlated with the TCS entering traffic volume. Third, it was uncovered that the VDS speed had a negative relationship with the search traffic on the time series diagram. Lastly, it was concluded that the transfer function noise time series model showed the better goodness-of-fit compared to the other time series model. It is expected that "Big Data" from the internet search data can be extensively applied in the transportation field if the sources of search traffic, time difference and aggregation units are explored in the follow-up studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.177-182
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• 2023

Korea is currently an aging society with a population of about 15 percent over the age of 65. Accordingly, the government is currently working on a number of measures. However, the problem that is rapidly increasing rather than decreasing is the traffic accident of the elderly. It has increased so much that we can check it out in multiple media right away. An average of 110 elderly people die or are injured in traffic accidents a day, or about 40,000 a year. The National Police Agency reported a 25 percent increase in elderly traffic accidents from five years ago. This paper analyzes traffic accidents of senior citizens through the Big Data analysis and R programming language to present the main causes of traffic accidents of senior citizens and areas where measures are needed to prevent them.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.586-589
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• 2018

As the convergence of traffic system and Big-data technology, new convenient services which is helpful for drivers and pedestrian are appeared. Recently, the various researches about the traffic system, such as prediction of traffic jam and finding the shortest path, are studied. In this paper, we collect the data of taxi trips in Daegu City, and visualize them on the map of Daegu City. And then, we select specific sections of roads in the city, and by using the data of location and speed about taxis and the information of the road sections, calculate the traffic of that section and the average speed of cars on that section. As a result of this, we give help solving the problem of the specific road sections.

• Electronics and Telecommunications Trends
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• v.36 no.6
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• pp.55-66
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• 2021

The air traffic industry, one of Korea's major industries, has recently experienced increased demand from overseas air passengers, launched a low-cost airline, and increased special freight transportation capacity. These initiatives have had a positive impact on air traffic (for example, profitability); however, air traffic management has become more complex, which has increased the incidence of aviation accidents and created safety hazards. There is an increasing need to collect and analyze aviation data that can proactively respond to aviation accidents. Concatenation of collected aviation data as big data and the development of artificial intelligence technology are gradually expanding aviation safety event analysis from conventional statistical analysis to machine learning-based analysis. This paper surveys the trends of flight safety event analysis to derive aviation safety risk factors by looking at the types and characteristics of aviation data that can be used to predict accidents related to safety in aviation operations.

• International Journal of Highway Engineering
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• v.18 no.1
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• pp.99-107
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• 2016

PURPOSES : This study was initiated to estimate expressway traffic congestion costs by using Vehicle Detection System (VDS) data. METHODS : The overall methodology for estimating expressway traffic congestion costs is based on the methodology used in a study conducted by a study team from the Korea Transport Institute (KOTI). However, this study uses VDS data, including conzone speeds and volumes, instead of the volume delay function for estimating travel times. RESULTS : The expressway traffic congestion costs estimated in this study are generally lower than those observed in KOTI's method. The expressway lines that ranked highest for traffic congestion costs are the Seoul Ring Expressway, Gyeongbu Expressway, and the Youngdong Expressway. Those lines account for 64.54% of the entire expressway traffic congestion costs. In addition, this study estimates the daily traffic congestion costs. The traffic congestion cost on Saturdays is the highest. CONCLUSIONS : This study can be thought of as a new trial to estimate expressway traffic congestion costs by using actual traffic data collected from an entire expressway system in order to overcome the limitations of associated studies. In the future, the methodology for estimating traffic congestion cost is expected to be improved by utilizing associated big-data gathered from other ITS facilities and car navigation systems.

• The Journal of Bigdata
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• v.2 no.2
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• pp.129-140
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• 2017

Weather acts through low visibility, precipitation, high winds, and temperature extremes to affect driver capabilities, vehicle performance (i.e., traction, stability and maneuverability), pavement friction, roadway infrastructure, crash risk, traffic flow, and agency productivity. Recently a variety of road weather big data sources such as CCTV, road sensor/systems, car sensor have been developed to solve the weather-related problems, This study identifies and defines the types and characteristics of these sources to suggest how to utilize them for car safety and efficiency as well as road management through analyzing domestic and oversea cases of road weather big data applications.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.27-39
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• 2004

The previous studies on analyzing the effects of traffic safety policies are very limited. Implementing traffic safety policies in view of their own urban traffic characteristics would be fairly desirable to handle properly the traffic safety problems. The relationships between traffic accidents and traffic safety policies have been researched by classifying the eighty one cities in Korea into four groups in terms of the size of the city population. Statistical analysis have been conducted for traffic accidents data and traffic safety policies, respectively. In order to mearsure the effectiveness of the traffic policies in the real world, regression models have been developed by handling the accident data and policy data. As a result of analysing the data, the traffic policies have showed different effects according to the size of the cities. While budget investment policies had provided enormous influences to reduce traffic accidents in the big cities more than a half million polulation, traffic enforcement and traffic education have been so efficient to control traffic accident problems in the smaller cities less than a half million poluation.

• Journal of Information Technology and Architecture
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• v.11 no.4
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• pp.449-462
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• 2014

In big data era, there are a number of considerable parts in processing systems for capturing, storing, and analyzing stored or streaming data. Unlike traditional data handling systems, a big data processing system needs to concern the characteristics (format, velocity, and volume) of being handled data in the system. In this situation, virtualized computing platform is an emerging platform for handling big data effectively, since virtualization technology enables to manage computing resources dynamically and elastically with minimum efforts. In this paper, we analyze resource utilization of virtualized computing resources to discover suitable deployment models in Apache Hadoop and HBase-based big data processing environment. Consequently, Task Tracker service shows high CPU utilization and high Disk I/O overhead during MapReduce phases. Moreover, HRegion service indicates high network resource consumption for transfer the traffic data from DataNode to Task Tracker. DataNode shows high memory resource utilization and Disk I/O overhead for reading stored data.