• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.3
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• pp.317-325
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• 2017

The EDR(Event Data Recorder) is a part of the ACU(Airbag Control Unit) functions mounted on a vehicle. EDR data have pre-crash data and post-crash data. Pre-crash data are recorded within 5 sec from time zero(AE) with 0.5 sec resolution, and reveal vehicle speed, engine rotation speed, throttle opening, brake pedal operation, acceleration pedal position and steering angle, etc. Using this EDR data, the investigation of a traffic accident can become more objective and scientific. Crash tests of three vehicles equipped with EDR function had been performed successfully. Evaluation of EDR data reliability had also been performed using Vbox and PC-Crash's sequence table function. Based on the results, we could confirm EDR data's reliability and availability for Traffic Accident Analysis by the series of this process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.841-854
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• 2020

The paper explains the method to process, analyze and predict traffic patterns in Los Angeles county using Big Data and Machine Learning. The dataset is used from a popular navigating platform in the USA, which tracks information on the road using connected users' devices and also collects reports shared by the users through the app. The dataset mainly consists of information about traffic jams and traffic incidents reported by users, such as road closure, hazards, accidents. The major contribution of this paper is to give a clear view of how the large-scale road traffic data can be stored and processed using the Big Data system - Hadoop and its ecosystem (Hive). In addition, analysis is explained with the help of visuals using Business Intelligence and prediction with classification machine learning model on the sampled traffic data is presented using Azure ML. The process of modeling, as well as results, are interpreted using metrics: accuracy, precision and recall.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.5
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• pp.253-263
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• 2021

Traffic data collected using advanced equipment are highly valuable for traffic planning and efficient road operation. However, there is a problem regarding the reliability of the analysis results due to equipment defects, errors in the data aggregation process, and missing data. Unlike other detectors installed for each vehicle lane, radar detectors can yield different error types because they detect all traffic volume in multilane two-way roads via a single installation external to the roadway. For the traffic data of a radar detector to be representative of reliable data, the error factors of the radar detector must be analyzed. This study presents a field survey of variables that may cause errors in traffic volume collection by targeting the points where radar detectors are installed. Video traffic data are used to determine the errors in traffic measured by a radar detector. This study establishes three types of radar detector traffic errors, i.e., artificial, mechanical, and complex errors. Among these types, it is difficult to determine the cause of the errors due to several complex factors. To solve this problem, this study developed a radar detector traffic volume error analysis model using a multiple linear regression model. The results indicate that the characteristics of the detector, road facilities, geometry, and other traffic environment factors affect errors in traffic volume detection.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.187-196
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• 2017

It is urgent to prepare countermeasures for traffic congestion problems of Korea's metropolitan area where central functions such as economic, social, cultural, and education are excessively concentrated. Most users of public transportation in metropolitan areas including Seoul use the traffic cards. If various information is extracted from traffic big data produced by the traffic cards, they can provide basic data for transport policies, land usages, or facility plans. Therefore, in this study, we extract valuable information such as the subway passengers' frequent travel patterns from the big traffic data provided by the Seoul Metropolitan Government Big Data Campus. For this, we use a Hadoop (High-Availability Distributed Object-Oriented Platform) to preprocess the big data and store it into a Mongo database in order to analyze it by a sequential pattern data mining technique. Since we analysis the actual big data, that is, the traffic cards' data provided by the Seoul Metropolitan Government Big Data Campus, the analyzed results can be used as an important referenced data when the Seoul government makes a plan about the metropolitan traffic policies.

• Journal of Korean Society of Transportation
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• v.12 no.1
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• pp.97-116
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• 1994

Today advanced traffic management systems are required because of a high increase in traffic demand. Accordingly, the objective of this study is to take advantage of image processing systems and present image processing methods available for collection of the data on traffic characteristics, and then to investigate the possibility of traffic flow analysis by means of comparison and analysis of measured traffic flow. Data were collected at two places of Daegu city and Kyongbu expressway by using VTR. Rear view (down stream) and frontal view (up stream) methods were employed to compare and analyze traffic characteristics including traffic volume, speed, time-headway, time-occupancy, and vehicle-length, by analysis of measured traffic flow and image processing respectively. Judging from the results obtained by this study, image processing techniques are sufficient for the analysis of traffic volume, but a frame grabber equipped with high speed processor is necessary as well, with low level system judged to be sufficient for traffic volume analysis.

• The Journal of the Korea Contents Association
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• v.19 no.2
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• pp.460-468
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• 2019

Recently, analysis techniques to extract new meanings using big data analysis and various services using these analysis techniques have been developed. Among them, the transport is one of the most important areas that can be utilized about big data. However, the existing traffic route guidance system can not recommend the optimal traffic route because they use only the traffic information when the user search the route. In this paper, we propose a realtime optimal traffic route guidance system using big data analysis. The proposed system considers the realtime traffic information and results of big data analysis using historical traffic data. And, the proposed system show the warning message to the user when the user need to change the traffic route.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.992-1001
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• 2022

The maritime traffic analysis has been conducted in various ways to solve problems arising from the complex marine environment. However, recent trends in the maritime industry, such as the development of the maritime autonomous surface ships (MASS), suggest that maritime traf ic analysis needs change. Accordingly, based on the studies conducted over the past decade for improvements, automatic identification system (AIS) data is mainly used for maritime traffic analysis. Moreover, the use of geographic information that directly af ects ship operation is relatively insufficient. Therefore, this study presented a method of using a combination of shape format data and AIS data to enhance maritime traffic analysis in preparation for the commercialization of autonomous ships. Consequently, extractable marine traffic characteristics were presented when shape format data were used for marine traffic analysis. This is expected to be used for marine traffic analysis for the introduction of autonomous ships in the future.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.1035-1036
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• 2006

We analyzed the performance of hierarchical cellular system with multi traffic(voice traffic, data traffic). We executed the computer simulation by the various ratio of traffic generation(voice traffic, data traffic). We generated data traffic at microcell. The more voice traffic generated, the higher the block probability of data traffic became at macrocell.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.262-265
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• 2008

We design level 1 traffic thematic map for common data structure. Level 1 means the road that can passing cars. If public office and private company use this form, they can save amount of money from overlapping update. And widely use of traffic analysis, navigation and traffic information system. For design common data structure we compared several data structure(traffic thematic map, ITS standard node/link, Car navigation map), and generalization these characteristic data. After generalization we considered about application parts. It can use of public part(traffic analysis, road management, accident management) and private part(car navigation, map product, marketing by variable analysis) etc.

• Journal of Industrial Convergence
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• v.17 no.4
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• pp.53-58
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• 2019

It is important to collect and analyze the data from the traffic accident analysis system and the National Statistical Office to reduce the traffic accident rate of the elderly, who are the weakest. In particular, it is more important to analyze the data in areas where the elderly population is large and where accidents occur frequently. This paper visualizes and analyzes the data of elderly traffic accidents that occurred in recent 5 years in the area where many elderly people live in Buyeo-gun. The elderly traffic accident type, accident area, and location data of the elderly can be useful for the improvement measures and related decision making to reduce the elderly traffic accidents.