국내 교통량 및 속도 자료 DB구축 현황, 문제점, 그리고 개선방안

연지윤,김찬성,김은미 한국교통연구원 2012 交通硏究 Vol.19 No.3

In the field of traffic-related research, policy making, and investment purposes, the most basic and vital information is traffic counting and speed data. Traffic counting data are used for the validity of the investment of facilities, estimation of the size of traffic impacts, and evaluation of the facilities for capacity analysis, while the speed data are used for providing traffic information. In addition, such traffic data could be applied for other various purposes as traffic-related indicators if the data were generated as statistical information, but the consistency of the data are not guaranteed. The reason is that the traffic counting data were collected differently by road management agencies and speed data were collected for proving traffic information and did not manage the history. This paper discusses the status of traffic counting and speed data collection, and problems and improvements regarding the system. The biggest issue is that the traffic counting data were collected and managed differently by the provincial government and it was difficult to obtain the collected data. This could be considered the replacement of survey methods, done by mechanical investigation and not human expression. The other thing for the speed data is that data history management should be required for proving various traffic information. Finally, in order to facilitate the traffic counting and speed data acquisition online, user application resources must be developed so that data can be downloaded which is based on the historical DB system. 교통관련 분야에서 연구를 위한 목적이든 정책 및 사업 평가를 위한 목적이든 가장 기초적이고 핵심적인 자료가 바로 교통량과 속도 자료이다. 교통량 자료는 교통시설 투자 타당성 평가 및 시설물 규모산정, 교통영향 평가, 시설물의 용량 및 서비스 수준 분석 등 크고 작은 교통관련 사업에 사용되고 있고, 속도자료는 도로의 소통 정보를 제공하기 위한 목적으로 주로 사용되고 있다. 이외에도 교통량과 속도 자료는 분석 목적에 따라 다양한 교통관련 지표 혹은 통계정보를 생성하여 연구 및 정책분석에 활용 가능하지만, 일관성 있는 자료의 획득이 쉽지 않은 것이 현실이다. 그 이유는 현재 우리나라의 교통량 자료는 도로 관리 주체별로 수집체계가 다르고, 속도 자료의 경우에는 소통정보 제공 후 버려져 이력관리가 되고 있지 않기 때문이다. 본 논문에서는 우리나라의 교통량과 속도 자료 수집 및 이용 현황을 살펴보고, 문제점 및 개선방안에 대해 정리해 보았다. 교통량 자료의 경우, 가장 큰 문제점은 지방정부에서 관리하는 도시부 도로의 교통량 자료 수집 체계가 제각각일 뿐만 아니라, 신뢰성 있는 자료 획득에 상당한 어려움이 따른다는 점이다. 이는 지방정부의 재정적인 문제와 연결될 수 있는 부분으로 연 1회 인력식 조사를 통해 수집된 교통량 조사 지점을 상시 조사가 가능한 기계식 조사로 대치하는 방안을 고려해 볼 수 있다. 또한, 인력식 및 비전문가에 의한 교통량 조사체계를 개선하기 위해서는 기존의 ITS 시스템을 활용하는 방안 등을 고려해 볼 수 있다. 속도 자료의 경우의 가장 큰 문제점은 소통 정보 제공을 목적으로 수집되기 때문에 별도의 이력자료 관리를 하지 않고 있는데, 이력자료 분석 등을 통하여 좀더 다양한 교통정보를 생성할 수 있어야 한다. 마지막으로 교통량 및 속도자료의 획득을 용이하게 하기 위해서는 이용자 어플리케이션 개발로 온라인 상에서 필요한 교통자료를 다운받을 수 있도록 해야 하고, 전문인력 배치를 통한 정보공개 요청에 대응할 수 있도록 하며, 이력자료 DB와의 연동 등의 개선이 필요할 것으로 보인다.

교통정보 이력자료 분석을 위한 통합 교통 데이터베이스의 설계 및 구축

이민수,최옥주,맹보현 한국정보과학회 2009 데이타베이스 연구 Vol.25 No.3

현재 한국도로공사에서 운영하는 고속도로 교통관리시스템(FTMS: Freeway Traffic Management System)과 우회도로 교통정보시스템(ARTIS: Alternative Route Traffic Information System)은 차량검지장치(VDS), 차량번호인식장치(AVI), 그리고 CCTV를 통해 실시간 교통자료를 수집하고, 도로전광표지(VMS) 등 다양한 매체로 교통정보를 제공한다. 이러한 운영계 시스템은 매일 도로에서 수집되는 엄청난 양의 교통자료를 가공하여 실시간으로 교통정보를 제공하는데에만 목적을 두고 있으므로 최소한의 교통자료만을 저장하고 있다. 또한 교통자료가 여러 운영계 시스템으로 분산되어 있어 연구자가 다양하고 대량의 과거 교통자료를 가공하여 비교 분석을 하는데 어려움이 있다. 본 논문에서는 여러 운영계 시스템으로부터 대용량의 교통자료를 하나의 통합 교통 데이터베이스로 구축하여 교통정보 이력자료를 연구할 수 있는 환경을 제안한다. 제안된 통합 교통 데이터베이스는 차량검지장치 자료, 차량번호인식장치 자료, 고속도로 통행료수납(TCS) 자료, 고속도로 전자통행료수납(HI-PASS) 자료, 돌발상황 자료, 도로전광표지 자료에 대한 통합 모델을 제시하고 실제 대용량 데이터베이스 구축으로 교통이력자료에 대한 분석이 가능토록 하였다. 본 시스템과 같은 대규모의 통합 교통 데이터베이스는 새로운 시도이며 실험을 통해 시스템의 자료처리나 질의 처리에서도 매우 좋은 성능을 보여준다. The current Freeway Traffic Management System (FTMS) and Alternative Route Traffic Information System (ARTIS) operated by Korea Expressway Corporation are the systems which gather the traffic data in real-time through Vehicle Detecting System (VDS), Auto Vehicle Identification (AVI) and CCTV, and provide information on traffic through various media such as Variable Message System, etc. Such operational systems aim to provide real-time information and only save the minimum required traffic data. The various traffic data are also dispersed into a number of operational systems. Hence, it is very difficult for a researcher to compare and analyze the various historical traffic data. In this study, we propose an integration model to build an integrated traffic database which integrates data from Vehicle Detecting Systems, Auto Vehicle Identification Systems, Toll Collection System (TCS), Highway Auto Toll Payment System (HI-PASS), Events, Variable Message System (VMS). This work is a pioneering work in such an area, and we actually implement the database and verify that it provides superior performance in terms of data and query processing capabilities.

통신사실확인자료 보관의 법적 근거와 성질

박희영 한국형사법학회 2023 형사법연구 Vol.35 No.1

Communication confirmation data (= traffic data, Verkehrsdaten) is sensitive personal information and information that belongs to communication secrets and private life. Therefore, the legal basis and legal nature of the retention of traffic data can be derived from a historical and systematic review of the relevant provisions of the Personal Information Protection Act(PIPA) and the Protection of Communications Secrets Act(PCSA). The legal basis for the traffic data retention comes from the combination of the preservation obligation in Article 21(3) of PIPA and Article 15(2) of PCSA and Article 41 of its Enforcement Decree. Failure to comply with the preservation obligation is punishable by a administrative fine under Article 75 (4) (1) of PIPA, so the legal nature is mandatory. The duty to preserve is further strengthened by the duty to take safety measures, and the provision of retention data to investigative agencies is also a legal obligation, as it is compelled by Article 18 (2) and its penalty provision of PIPA. The German Telecommunications Act(TKG) imposes an obligation on telecommunications providers to retain traffic data for public interest purposes such as criminal investigations. In this respect, the legal nature of traffic data retention in South Korea is the same as in Germany. On September 22, 2022, the Court of Justice of the European Union(CJEU) ruled that the traffic data retention provisions of TKG violate Articles 7, 8, 11, and 52(1) of the Charter of Fundamental Rights of the EU. This violation naturally also applies to the provisions of the Code of Criminal Procedure(StPO) requiring the provision of data. Applying the CJEU's ruling to our traffic data retention provisions would likely lead to the same conclusion, unless the interpretation of the relevant fundamental rights is different. Therefore, a precise analysis of the judgement of CJEU would be required to determine the constitutionality of our traffic data retention provisions. If there is a possibility of unconstitutionality, a new legislative theory should be discussed by examining the exceptions provided by the CJEU, and if there is no possibility of unconstitutionality, reasons should be provided why the same fundamental right should be different from the CJEU's judgement. 지금까지 통신사실확인자료 보관의 법적 근거와 법적 성질이 무엇인지 명확하게밝혀지지 않았다. 통신사실확인자료는 민감한 개인정보인 동시에 통신비밀과 사생활의 비밀에 속하는 정보다. 따라서 통신사실확인자료 보관의 법적 근거와 법적 성질은개인정보보호법과 통실비밀보호법의 관련 규정들을 연혁적, 체계적으로 고찰하여 도출할 수 있다. 통신사실확인자료 보관의 법적 근거는 개인정보보호법 제21조 제3항의보관의무와 통실비밀보호법 제15조의2 및 동법 시행령 제41조의 결합에서 나온다. 보관의무 불이행은 개인정보보호법 제75조 제4항 제1호에 의해서 과태료 처분을 받게되므로 법적 성질은 강행규정이다. 안전조치의무를 통해서 이러한 보관의무는 더욱강화된다. 또한 수사기관 등에 대한 통신사실확인자료의 제공도 개인정보보호법 제18 조 제2항과 이에 대한 벌칙규정에 따라 강제되므로 강행규정이다. 독일 전기통신법은 범죄수사와 같은 공익목적을 위하여 전기통신사업자에게 트래픽데이터 보관의무를 부과하고 있다. 이러한 점에서 우리 통신사실확인자료 보관의법적 성질은 독일 전기통신법의 트래픽데이터 보관의 법적 성질과 동일하다. 유럽사법재판소는 2022년 9월 22일 독일 전기통신법의 트래픽데이터 보관조항이 유럽연합기본권헌장 제7조(사생활존중권, 통신비밀보호), 제8조(개인정보보호), 제11조(표현의 자유) 그리고 제52조 제1항(기본권 제한의 법률주의와 비례성원칙준수)에 위반된다고 판결하였다. 트래픽데이터 보관조항의 기본권 헌장 위반은 당연히 이의 제공을요청하는 형사소송법 규정에도 적용된다. 이러한 유럽사법재판소의 판결을 우리의 통신사실확인자료 보관조항에 적용해 보면 관련 기본권에 대한 해석이 다르지 않는 한동일한 결론이 도출될 가능성이 있다. 따라서 유럽사법재판소 판결을 정확히 분석하여 우리의 통신사실확인자료 보관조항의 위헌성 여부를 검토해야 할 것이다. 만일 위헌가능성이 충분하다면 유럽사법재판소가 제시한 예외적 허용을 검토하여 새로운 입법론이 논의되어야 하고, 위헌가능성이 없다면 어떤 근거에서 동일한 기본권이 유럽사법재판소의 판결과 달라야 하는지 규명되어야 할 것이다.

Peak Hour Identification for Traffic Congestion Based on IoT Environments

Vasanth Ragu,Saraswathi Sivamani,이명배,조현욱,조용윤,박장우,신창선 한국지식정보기술학회 2018 한국지식정보기술학회 논문지 Vol.13 No.3

This study deals with the analysis of traffic congestion and the peak hour identification by using Kalman Filter and Ensemble Model. There are different types of traffic congestion, Roadway Traffic congestion, Airways Traffic congestion, Network Traffic congestion, and so on. This study focuses on Roadway Traffic congestion. The peak hour identification is essential to prevent roadway traffic congestion. In roadway traffic congestion, there are two categories in traffic data, namely Roadside Equipment (RSE) data and Video Detection System (VDS) data. Both data were collected from RSE devices and VDS devices, which are located in roadways signals, toll plaza, private sectors, and etc. In traffic data, it may contain error values. So, this paper applies the Kalman Filter for the purpose of removing the error values or inaccurate values and providing the cleaned Traffic data. The suggested study also uses the Ensemble Model to average the traffic data at corresponding hours easily to analyse the traffic data. To identify peak hour, it defines four different models by considering numbers, average times, and average speeds of vehicles. With the suggested method, the perfect peak hour in the traffic data can be easily and exactly obtained. In the tests and results, this paper showed the detailed process of peak hour identification in traffic congestion.

교통사고 데이터의 마이닝을 위한 연관규칙 학습기법과 서브그룹 발견기법의 비교

김정민(Jeongmin Kim),류광렬(Kwang Ryel Ryu) 한국지능정보시스템학회 2015 지능정보연구 Vol.21 No.4

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world’s roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to

빅데이터 분석 기법을 이용한 실시간 대중교통 경로 안내 시스템의 설계 및 구현

임종태,복경수,유재수 한국콘텐츠학회 2019 한국콘텐츠학회논문지 Vol.19 No.2

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. 최근 빅데이터 분석을 통해 새로운 정보들을 도출해내기 위한 분석 기법들과 이를 이용한 다양한 서비스들이 개발되고 있다. 그 중에서 빅데이터가 중요하게 활용될 수 있는 분야 중의 하나가 교통 분야이다. 기존 대중교통 안내 서비스의 경우 현재 교통정보를 기준으로 추천하기 때문에 실제로는 최적이 아닌 경로가 추천될 수 있다. 본 논문에서는 빅데이터 분석을 통한 실시간 최적 교통 경로 안내 시스템을 설계하고 구현한다. 설계한 시스템은 실시간 교통정보를 활용함과 동시에 과거 수집된 교통 정보를 분석하여 각 경로들의 교통 상황을 예측하여 경로 이동 계획을 설정해준다. 또한 중간에 교통상황이 급변하여 경로를 수정해야할 필요가 있을 때 사용자에게 알림을 주고 그에 대한 조치를 취할 수 있도록 지원한다.

C-ITS 및 Location Intelligence 데이터 융합을 통한 음영구간 교통량 추정 연구

손지언,김종형,이태헌 한국도로학회 2024 한국도로학회논문집 Vol.26 No.4

PURPOSES : This study aims to investigate the reliability of the real-time estimation of intersection traffic volumes based on the integration of location intelligence data and smart intersection data. METHODS : Location intelligence data (LID) and smart intersection data were obtained at eight intersections in Inju-daero, Incheon. The two datasets were then integrated to estimate traffic volumes for intersections in the shadow section, where traffic information was not expected to be obtained. The traffic estimation accuracy was evaluated using the total traffic, approach traffic, and turning movement volumes at the intersections. The estimated traffic was compared with the actual traffic volumes in the smart intersection data to validate the reliability of traffic estimation. RESULTS : The average traffic estimation error for the total intersection volume was approximately 4.5% for the five intersections in the shadow section. The estimation errors for the approach volumes (less than 5%) were also consistently low, except from 12 pm to 1 pm. CONCLUSIONS : The findings of this study suggest that location intelligence data can be combined with smart intersection data to estimate real-time traffic for shadow sections on roadways. This could enable a cost-effective cooperative intelligent transport system (C-ITS) when the municipal budget is limited, ultimately leading to the sustainable operation of C-ITS.

이동통신 서비스 이용행태를 고려한 무선데이터 트래픽 분석에 관한 연구

변희섭 한국정보사회학회 2023 정보사회와 미디어 Vol.24 No.3

무선통신 기술의 진화에 따른 초연결 사회로의 전환으로 인해 무선데이터 트래픽이 급격히 증가하고 있다. 정책적 관점에서, 트래픽 변화 양상이 효과적으로 파악되어야 주파수 신규할당, 이용 효율 개선, 기술 혁신 등과 같은 대안을 마련할 수 있다. 기존 연구는 대개 최번시 이용시간, 설문조사 등을 통해 트래픽을 분석하고 있어 다변화하는 소비자의 이용행태를 반영하거나, 객관성을 담보하기 힘들다. 이러한 논의 하에서, 본 연구는 소비자의 경제적 유인과 이용행태를 반영하는 요인들이 무선데이터 트래픽에 미치는 효과를 분석한다. 트래픽 영향요인으로, 요금, 소득, 주파수 할당량, 서비스 품질, 기술방식 이용도, 콘텐츠 이용행태 등을 고려하며, 방법론으로 주성분 회귀분석을 활용한다. 이동통신시장 자료를 활용한 실증분석결과, 서비스 품질, 기술방식 이용도 등이 무선데이터 트래픽에 유의미한 영향을 미치는 것으로 확인된다. 본 연구의 결과는 통신정책 수립의 객관화·과학화에 기여할 것으로 기대된다. 구체적으로, 분석된 트래픽은 주파수의 할당, 재할당, 회수·재배치 등과 같은 정책설계에 있어 수요평가의 근거로 활용될 수 있다. Due to the evolution of wireless communication technology and the subsequent transition to a hyper-connected society, there has been a rapid increase in wireless data traffic. From a telecommunication policy standpoint, it is crucial to analyze this traffic in order to devise effective policies, such as spectrum allocation and re-farming. Traditionally, traffic analysis has predominantly relied on factors like busy hour usage and survey outcomes. The busy hour usage approach operates under the assumption that consumer service usage remains fixed at a specific level (maximum) for a set period. However, consumer behavior regarding mobile communication services is evolving rapidly. The emergence of data-intensive services like AR (augmented reality), VR (virtual reality), and cloud computing is driving changes in consumer behavior patterns. Traffic analysis via survey results tends to be qualitative in nature, potentially raising concerns regarding objectivity and reliability. While there is a push for the scientificization and objectification of spectrum management policies, utilizing survey-based approaches may not fully meet these requirements. Therefore, there is an emphasized need to develop methodologies that transcend the limitations posed by busy hour usage assumptions and qualitative survey-based analyses to ensure more objective and reliable spectrum management policies. In light of these discussions, this study aims to explore the impact of various factors that mirror consumers' incentives and behaviors on wireless data traffic. Specifically, I delve into consumers' economic motivations (such as average revenue per user and service quality), their technology adoption, and their content usage patterns. The theoretical framework of this study can be viewed as an effort to apply and broaden the demand function of consumer theory within economics. Given that consumers are the primary entities generating traffic, it's a logical approach to construct an analytical model based on inferences drawn from their choices and behaviors. Methodologically, this study employs principal component regression. This methodology unfolds in two distinct steps. Initially, I categorize factors that may influence traffic and identify key components that maximize the information contained within each category of factors. Subsequently, I conduct regression analysis by integrating the principal component factors from each category along with other pertinent variables into a model. This approach aims to enhance the existing demand analysis model, which traditionally relies on single information sources such as past traffic (time-series analysis) and the number of subscribers (diffusion model). By employing principal component regression, this study seeks to augment the explanatory power of the model and expand its scope. This study uses the Korean mobile market as its empirical sample. The rationale behind this choice is the substantial number of consumers within this market and their tendency to exhibit sensitive changes in behavior. Moreover, from the perspective of telecommunication policy, this market presents a significant demand for accurate traffic analysis concerning spectrum allocation and the determination of spectrum prices. The empirical analysis conducted in this study reveals that service quality and the acceptance of technology have a significant influence on mobile data traffic. These findings carry two noteworthy policy implications. Firstly, they advocate for the consideration of service quality in traffic analysis for the formulation of spectrum allocation and assignment strategies. Secondly, they serve as evidence supporting the notion that the introduction of new mobile communication technologies correlates with an upsurge in consumer demand (traffic). Furthermore, the results of this study can contribute significantly to the objectification and scientific grounding of telecommunication policy. Specifically, the observed...

The Study on Divide about Data Traffic use between Mobile User Groups

Hongjae Lee,Sang-won Kim,Jong-Bae Kim 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.8

Mobile data traffic has been rising rapidly caused by spreads and development of smart devices As a result, it brought a lot of changes throughout our life and usage pattern for mobile users, as well as mobile technology advance and market alternation after data bandwidth growth and decline of mobile rate for data. Recently, the “Unlimited mobile tariff” has been released due to the severe competition between domestic mobile carriers, thus, the user of unlimited mobile tariff system increases while users have mobile tariff cost relief. Also, the various devices are connected to each other everywhere in our life, not only past mobile phone and tablet. It is overly connected in one IoT era, then users of its services increases sharply, so that the data traffic of IoT devices are regarded as significant element to find out next mobile data traffic usage and pattern study. The Goal of this study is to analyze the traffic of Korean mobile and wired line internet statistics by user group upon the network development and mobile tariff progress. And it is required to find out IoT devices market state and influence for mobile market based on empirical data and statistics in Korea.

대용량 과거 교통 이력데이터 관리를 위한 방법론 설계

우찬일,전세길 (사)디지털산업정보학회 2010 디지털산업정보학회논문지 Vol.6 No.2

Historical archived traffic data management system enables a long term time-series analysis and provides data necessary to acquire the constantly changing traffic conditions and to evaluate and analyze various traffic related strategies and policies. Such features are provided by maintaining highly reliable traffic data through scientific and systematic management. Now, the management systems for massive traffic data have a several problems such as, the storing and management methods of a large volume of archive data. In this paper, we describe how to storing and management for the massive traffic data and, we propose methodology for logical and physical architecture, collecting and storing, database design and implementation, process design of massive traffic data.