• Title/Summary/Keyword: 가중 및 지수평활법

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Predictive Modeling of the Bus Arrival Time on the Arterial using Real-Time BIS Data (실시간 BIS자료를 이용한 간선도로의 버스도착시간 예측모형구축에 관한 연구)

  • Kim, Tae Gon;Ahn, Hyeun Chul;Kim, Seung Gil
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.29 no.1D
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    • pp.1-9
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    • 2009
  • Bus information system(BIS), as a part of the intelligent transportation system(ITS), is one of the most advanced public transportation systems which provide the real-time bus traffic information for the users waiting the buses at the bus stop. However, correct bus information data, such as the present bus location, the user waiting time, the bus arrival time, etc. are not provided for the bus users because the proper bus arrival time predictive models are not used yet in most of the cities operating the bus information system, including the metropolitan City of Ulsan. Thus, the purpose in this study is to investigate real-time bus traffic characteristic data for identifying the bus operation characteristics on the arterial under the study in the metropolitan City of Ulsan, analyze real-time bus traffic characteristic data on the ID locations of the arterial under the study, construct the optimal unit segment models for the unit segments which are the bus stop, node and travel section using the exponential smoothing, weighted smoothing and Kalman Filter methods, respectively, and finally suggest the optimal integrated model for predicting the real-time bus arrival time at the bus stop of the arterial under the study.

A Study on Long-term Maximum power Demand Forescasting Using Exponential Smoothing (지수평활에 의한 장기 최대전력 수요 예측에 관한 연구)

  • 고희석;이태기
    • The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.6 no.3
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    • pp.43-49
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    • 1992
  • Forecasting of electric power demand has been a basic element for electric power system operation and system development, and it's accuracy has very strong influence on reliability and economical efficience of power supply. So, in this paper, long―term maximum electric power demand has been forecasted by using the triple exponential smoothing method initiated R.G.Brown. It has been regarded this method as high accuracy and operational convenience. The smoothing function is a liner combination of all past observations and the weight given to previous observations decreases geometrically with age.

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Temporal hierarchical forecasting with an application to traffic accident counts (시간적 계층을 이용한 교통사고 발생건수 예측)

  • Jun, Gwanyoung;Seong, Byeongchan
    • The Korean Journal of Applied Statistics
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    • v.31 no.2
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    • pp.229-239
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    • 2018
  • This paper introduces how to adopt the concept of temporal hierarchies to forecast time series data. Similarly as in hierarchical cross-sectional data, temporal hierarchies can be constructed for any time series data by means of non-overlapping temporal aggregation. Reconciliation forecasts with temporal hierarchies result in more accurate and robust forecasts when compared with the independent base and bottom-up forecasts. As an empirical example, we forecast traffic accident counts with temporal hierarchies and observe that reconciliation forecasts are superior to the base and bottom-up forecasts in terms of forecast accuracy.

The Study of Prediction Model of Gas Accidents Using Time Series Analysis (시계열 분석을 이용한 가스사고 발생 예측 연구)

  • Lee, Su-Kyung;Hur, Young-Taeg;Shin, Dong-Il;Song, Dong-Woo;Kim, Ki-Sung
    • Journal of the Korean Institute of Gas
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    • v.18 no.1
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    • pp.8-16
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    • 2014
  • In this study, the number of gas accidents prediction model was suggested by analyzing the gas accidents occurred in Korea. In order to predict the number of gas accidents, simple moving average method (3, 4, 5 period), weighted average method and exponential smoothing method were applied. Study results of the sum of mean-square error acquired by the models of moving average method for 4 periods and weighted moving average method showed the highest value of 44.4 and 43 respectively. By developing the number of gas accidents prediction model, it could be actively utilized for gas accident prevention activities.