• Title/Summary/Keyword: Traffic information and prediction System

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Parallelization scheme of trajectory index using inertia of moving objects (이동체의 관성을 이용한 궤적 색인의 병렬화 기법)

  • Seo, Young-Duk;Hong, Bong-Hee
    • Journal of Korea Spatial Information System Society
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    • v.8 no.1 s.16
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    • pp.59-75
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    • 2006
  • One of the most challenging and encouraging applications of state-of-the-art technology is the field of traffic control systems. It combines techniques from the areas of telecommunications and computer science to establish traffic information and various assistance services. The support of the system requires a moving objects database system (MODB) that stores moving objects efficiently and performs spatial or temporal queries with time conditions. In this paper, we propose schemes to distribute an index nodes of trajectory based on spatio-temporal proximity and the characteristics of moving objects. The scheme predicts the extendible MBB of nodes of index through the prediction of moving object, and creates a parallel trajectory index. The experimental evaluation shows that the proposed schemes give us the performance improvement by 15%. This result makes an improvement of performance by 50% per one disk.

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The System for Predicting the Traffic Flow with the Real-time Traffic Information (실시간 교통 정보를 이용한 교통 혼잡 예측 시스템)

  • Yu Young-Jung;Cho Mi-Gyung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.7
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    • pp.1312-1318
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    • 2006
  • One of the common services of telematics is the car navigation that finds the shortest path from source to target. Until now, some routing algorithms of the car navigation do not consider the real-time traffic information and use the static shortest path algorithm. In this paper, we prosed the method to predict the traffic flow in the future. This prediction combines two methods. The former is an accumulated speed pattern, which means the analysis results for all past speeds of each road by classfying the same day and the same time inteval. The latter is the Kalman filter. We predicted the traffic flows of each segment by combining the two methods. By experiment, we showed our algorithm gave better precise predicition than only using accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

System Identification of Internet transmission rate control factors

  • Yoo, Sung-Goo;Kim, Young-Seok;Chong, Kil-To
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.652-657
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    • 2004
  • As the real-time multimedia applications through Internet increase, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example meeting this necessity. The TCP-friendly (TFRC) is an UDP-based protocol that controls the transmission rate based on the available round transmission time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used for the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.

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Spectrum Requirement Estimation for IMT Operation (IMT 운용을 위한 주파수 소요량 산출)

  • Han, Tae-Young;Kim, Nam;Yang, Jae-Soo;Choi, Jung-Hun;Kim, Cheol-Ho
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.19 no.2
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    • pp.161-167
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    • 2008
  • This paper describes the overview of spectrum requirement estimation recommended in ITU-R Rec. M.1390 and [IMT.METH] and its difference for the IMT mobile service, and a (IMT.METH) methodology is applied to the spectrum estimation of the recent IMT service. The traffic model and traffic calculation algorithm is briefly described for the carried traffic which is determined in terms of the offered traffic, system rapacity, and the criteria of quality of service. And the spectrum requirement demand which is required from year 2010 to year 2015 is calculated as an example for the IMT service which is recently operated and deployed in the current Korean market after obtaining the reasonable market data and the ITU market prediction data.

Development of Yóukè Mining System with Yóukè's Travel Demand and Insight Based on Web Search Traffic Information (웹검색 트래픽 정보를 활용한 유커 인바운드 여행 수요 예측 모형 및 유커마이닝 시스템 개발)

  • Choi, Youji;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.23 no.3
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    • pp.155-175
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    • 2017
  • As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

Analysis and Prediction of Bicycle Traffic Accidents in Korea (자전거 교통 사고 현황 및 예측 분석)

  • Choi, Seunghee;Lee, Goo Yeon
    • Journal of the Institute of Electronics and Information Engineers
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    • v.53 no.9
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    • pp.89-96
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    • 2016
  • According to the promoting policy for bicycle riding, the bicycle road infrastructure in Korea has been widely established. As the number of bicycle rider increases, bicycle traffic accidents also increase year after year. In this paper, we analyze bicycle traffic accident data from 2007 to 2014 which is provided by Road Traffic Authority and present statistical results of bicycle traffic accidents. And also regression analysis is applied to predict the number of daily traffic accidents in Seoul using ASOS(Automated Synoptic Observing System) climate data observed in the Seoul sector which are provided by Korea Meteorological Administration. In addition, decision tree analysis techniques are used to forecast the level of traffic accidents severity. In the analytic results of this research, we expect that it will be helpful to establish the collective policy of bicycle accident data and protective strategy in order to reduce the number of bicycle accidents.

Preliminary Study for Establishing the Realtime Ocean Prediction System in Busan Harbor (부산항 실시간 해양예보시스템 구축을 위한 기초연구)

  • Jung, Yun-Chul;Lee, Ho-Jin
    • Journal of Navigation and Port Research
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    • v.32 no.3
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    • pp.245-250
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    • 2008
  • Recently the numerical prediction technique is applied to many fields, because numerical models are developed so much for last decades. The real-time ocean prediction system is one of them and is capable of providing the real-time marine information for users to promote the safety af maritime traffic and preservation of marine resources. The system is composed of observing system, data distribution system and modelling system. In this study authors develop the modelling system and show the results as preliminary study for establishing the real-time ocean prediction system in Busan port. The system test is performed only for M2 tidal modelling due to the lack qf observation data, thus a full-scale test is required in future if enough data are provided Also observing system and data distribution system will be constructed continuously in future, then service for real-time data for users will be initiated.

Plurality Rule-based Density and Correlation Coefficient-based Clustering for K-NN

  • Aung, Swe Swe;Nagayama, Itaru;Tamaki, Shiro
    • IEIE Transactions on Smart Processing and Computing
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    • v.6 no.3
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    • pp.183-192
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    • 2017
  • k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

A Case Study on Foreign Intelligent Transport System (지능형 교통 시스템의 해외 사례 연구)

  • Lee, Dong-Woo
    • Journal of Digital Convergence
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    • v.12 no.6
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    • pp.259-264
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    • 2014
  • Digital convergence means a service or new product which appeared through fusion of unit technologies in information and communication regions. In 2011, The Government introduced "IT Convergence Technology Prediction Survey 2025". Smart mobility is a main factor in smart city which is main example of convergence. A intelligent transport system(ITS) is a key factor of smart mobility. The conventional transport systems include road, car, signal systems. But the ITS is a transport system containing additional technologies such as electronics, control, communication to increase traffic safety and effectiveness of traffic facilities. In this paper, we described intelligent transport system related with our life.

A Study on Distributed Message Allocation Method of CAN System with Dual Communication Channels (중복 통신 채널을 가진 CAN 시스템에서 분산 메시지 할당 방법에 관한 연구)

  • Kim, Man-Ho;Lee, Jong-Gap;Lee, Suk;Lee, Kyung-Chang
    • Journal of Institute of Control, Robotics and Systems
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    • v.16 no.10
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    • pp.1018-1023
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    • 2010
  • The CAN (Controller Area Network) system is the most dominant protocol for in-vehicle networking system because it provides bounded transmission delay among ECUs (Electronic Control Units) at data rates between 125Kbps and 1Mbps. And, many automotive companies have chosen the CAN protocol for their in-vehicle networking system such as chassis network system because of its excellent communication characteristics. However, the increasing number of ECUs and the need for more intelligent functions such as ADASs (Advanced Driver Assistance Systems) or IVISs (In-Vehicle Information Systems) require a network with more network capacity and the real-time QoS (Quality-of-Service). As one approach to enhancing the network capacity of a CAN system, this paper introduces a CAN system with dual communication channel. And, this paper presents a distributed message allocation method that allocates messages to the more appropriate channel using forecast traffic of each channel. Finally, an experimental testbed using commercial off-the-shelf microcontrollers with two CAN protocol controllers was used to demonstrate the feasibility of the CAN system with dual communication channel using the distributed message allocation method.