• Journal of information and communication convergence engineering
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• 제20권4호
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• pp.273-279
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• 2022

Owing to the need to establish a cooperative-intelligent transport system (C-ITS) environment in the transportation sector locally and abroad, various research and development efforts such as high-tech road infrastructure, connection technology between road components, and traffic information systems are currently underway. However, the current central control center-oriented information collection and provision service structure and the insufficient road infrastructure limit the realization of the C-ITS, which requires a diversity of traffic information, real-time data, advanced traffic safety management, and transportation convenience services. In this study, a network construction method based on the existing received signal strength indicator (RSSI) selected as a comparison target, and the experimental target and the proposed intelligent edge network compared and analyzed. The result of the analysis showed that the data transmission rate in the intelligent edge network was 97.48%, the data transmission time was 215 ms, and the recovery time of network failure was 49,983 ms.

• 한국안전학회지
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• 제27권4호
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• pp.108-114
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• 2012

Traffic Accident Merging Index(TAMI) is developed for TMACS(Traffic Safety Information Management Complex System). TAMI is calculated by combining 'Severity Index' and 'Frequency'. This paper suggest the accurate TAMI prediction model by time series forecasting. Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. Searches the model which minimizes the error of 230 local self-governing groups. TAMI of 2007~2009 years data predicts TAMI of 2010. And TAMI of 2010 compares an actual index and a prediction index. And the error is minimized the constant where selects. Exponential Smoothing model was selected. And smoothing constant was decided with 0.59. TAMI Forecasting model provides traffic next year safety information of the local government.

• 한국정보시스템학회지:정보시스템연구
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• 제26권3호
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• 한국도로학회논문집
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• 제13권3호
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• pp.83-91
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• 2011

VMS(Variable Message Sign)는 도로의 이용자에게 하류부의 교통정보를 제공하여 운전자의 경로선택을 위한 의사결정정보와 목적지까지 이동하기위하여 소요되는 통행시간정보를 제공한다. 하지만, VMS에 표출할 수 있는 정보의 개수는 표출면수에 의한 제약이 있기 때문에 신중하게 결정되어야 한다. 지금까지 기 구축된 이벤트의 메시지 표출우선순위는 발생된 이벤트의 심각도만을 고려하여 우선순위 규칙을 적용하고 있다. 하지만 VMS를 더욱 효율적으로 운영하기 위해서는 통행수요가 많은 경로에 교통정보를 우선적으로 제공할 때 그 정보의 수혜자가 가장 많을 것이다. 그러므로 실시간으로 기종점을 파악하여 더욱 많은 운전자가 경로선택이 가능한 정보를 제공받을 수 있도록 정보제공 전략을 수립할 필요가 있다. 본 연구에서는 주도로와 진출입 램프의 검지기 데이터를 이용하여 시간분할 기종점 추정방법론으로 통행수요를 추정하였으며, 추정된 기종점자료를 바탕으로 정보제공 우선순위를 결정할 수 있는 정보제공 전략을 소개하였다.

• 한국콘텐츠학회논문지
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• 제12권9호
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• pp.500-511
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• 2012

최근 운행경로와 소통상태 등을 제공하는 교통정보의 이용자 활용빈도가 높아지고 있다. 특히, 교통정보 제공 매체 중 편의성이 가장 좋은 인터넷 포탈사이트의 이용자 선호도가 가장 높은 것으로 조사되었다. 하지만, 포탈사이트 교통정보콘텐츠는 민간(포탈업체)에서 자체적으로 정보를 수집 제공하고 있어 공공에서 제공하는 콘텐츠에 비해서 신뢰성 등에 관한 검증 체계가 부재한 실정이다. 본 연구의 목적은 포탈사이트에서 제공하는 정보의 실제 정확도와 이용자가 느끼는 정확도를 알아보는 것이다. 이에 본 연구에서는 포탈사이트를 중심으로 현장 데이터를 통하여 정확도를 검증해보고, 설문조사를 통하여 이용자의 콘텐츠 이용실태와 이용자가 느끼는 교통정보콘텐츠의 정확도를 알아보았다. 또한, 교통정보콘텐츠에 대한 이용자의 기대치와 만족도를 조사하고, IPA기법을 적용하여 개선을 요구하는 콘텐츠를 선정하였다. 이동식 DSRC(Dedicated Short Range Communication)검지장비를 활용한 현장데이터 정확도 검증결과, 오차율은 14~32%로 조사되었으며 경우에 따라 아주 높은 오차율을 보이기도 하였다. 또한 설문에 응한 전체 응답자 중 28.3%가 포탈사이트의 정보를 가장 선호한다고 답변했으며, 포탈 교통정보 콘텐츠의 정확도에 대해서는 50.8%가 부정확하다는 답변하였다. 가장 부정확한 정보 콘텐츠는 '실시간 소통현황'을 지목하였으며, IPA기법을 적용한 결과에서도 '실시간 소통현황' 정보의 집중적인 개선노력이 필요한 것으로 분석되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2473-2489
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• 2022

Urban development has brought about the increasing saturation of urban traffic demand, and traffic congestion has become the primary problem in transportation. Roads are in a state of waiting in line or even congestion, which seriously affects people's enthusiasm and efficiency of travel. This paper mainly studies the discrete domain path planning method based on the flow data. Taking the traffic flow data based on the highway network structure as the research object, this paper uses the deep learning theory technology to complete the path weight determination process, optimizes the path planning algorithm, realizes the vehicle path planning application for the expressway, and carries on the deployment operation in the highway company. The path topology is constructed to transform the actual road information into abstract space that the machine can understand. An appropriate data structure is used for storage, and a path topology based on the modeling background of expressway is constructed to realize the mutual mapping between the two. Experiments show that the proposed method can further reduce the interpolation error, and the interpolation error in the case of random missing is smaller than that in the other two missing modes. In order to improve the real-time performance of vehicle path planning, the association features are selected, the path weights are calculated comprehensively, and the traditional path planning algorithm structure is optimized. It is of great significance for the sustainable development of cities.

• 한국통신학회논문지
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• 제39B권6호
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• pp.350-359
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• 2014

최근 급격한 인터넷의 발전으로 효율적인 네트워크관리를 위해 응용 트래픽 데이터 분석의 중요성이 강조되고 있다. 네트워크 관리를 위해 관리자는 트래픽 데이터를 각각 어떠한 응용에서 발생 하였는지 탐지할 수 있어야 한다. 응용을 탐지하기 위한 방법들 중 하나인 통계정보 트래픽 분류방법을 사용하여 트래픽을 분류할 수 있지만, 이러한 통계정보를 그대로 사용하여 분류하기에는 트래픽 수집지점에서 발생하는 패킷 역전, 재전송에 의한 패킷 중복과 같은 문제점들이 있다. 본 논문에서는 응용에서 발생된 트래픽의 탐지 및 분석률 향상을 위해 패킷 역전 문제와 재전송에 의한 패킷 중복 문제를 탐지하고 개선하는 방법론을 제안하였다. 이렇게 제안한 개선 방법론을 실제 트래픽 분석 시스템에 적용시킴으로써 응용별 바이트 기준 최대 4%의 탐지 및 분석률 향상을 보였다. 이는 제안한 방법론이 실제 트래픽 망에 부담을 줄 수 있는 heavy 플로우의 분석에 기여함을 확인하였다.

• 지능정보연구
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• 제6권1호
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• pp.113-128
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• 2000

This paper presents a first year report of an ongoing multi-year project to develop a systolic parallel simulation system for dynamic traffic assignment. The fundamental approach to the simulation is systolic parallel processing based on autonomous agent modeling. Agents continuously act on their own initiatives and access to database to get the status of the simulation world. Various agents are defined in order to populate the simulation world. In particular existing modls and algorithm were incorporated in designing the behavior of relevant agents such as car-following model headway distribution Frank-Wolf algorithm and so on. Simulation is based on predetermined routes between centroids that are computed off-line by a conventional optimal path-finding algorithm. Iterating the cycles of optimization-then-simulation the proposed system will provide a realistic and valuable traffic assignment. Gangnum-Gu district in Seoul is selected for the target are for the modeling. It is expected that realtime traffic assignment services can be provided on the internet within 3 years.

• 한국분무공학회지
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• 제23권2호
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• pp.81-89
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• 2018

The objective of this study is to estimate the level of exposure of traffic-related air pollutants (TRAPs) on the pedestrians in Seoul area. The road network's link-based pollutant emission was calculated by using a set of mobile source emission factor package and associated activity information. The population information, which is the number of pedestrian, was analyzed in conjunction with the link-based traffic emissions in order to quantify exposure level by selected 23 spots. We proposed the Exposure Intensity, which is defined by the amount of traffic emission and the population, to quantify the probability of exposure of pedestrian. Link-based traffic NOx and PM emissions vary by up to four times depending on the location of each spot. The Hot-spots is estimated to be around 1.8 times higher Exposure Intensity than the average of the 23 selected spots. The information of Exposure Intensity of each spot allows us to develop localized policies for air quality and health. Even in the same area, the Exposure Intensity over time also shows a large fluctuation, which gives suggestions for establishing site-specific counter-measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.41-60
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• 2018

Recently, road traffic congestion is becoming a serious urban phenomenon, leading to massive adverse impacts on the ecology and economy. Therefore, solving this problem has drawn public attention throughout the world. One new promising solution is to take full advantage of vehicular ad hoc networks (VANETs). In this study, we propose a new traffic congestion detection and quantification method based on vehicle clustering and fuzzy assessment in VANET environment. To enhance real-time performance, this method collects traffic information by vehicle clustering. The average speed, road density, and average stop delay are selected as the characteristic parameters for traffic state identification. We use a comprehensive fuzzy assessment based on the three indicators to determine the road congestion condition. Simulation results show that the proposed method can precisely reflect the road condition and is more accurate and stable compared to existing algorithms.