Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
자동 회귀 통합 이동 평균 모델 적용을 통한 한국의 자동차 사고에 대한 시계열 예측Time Series Forecasting on Car Accidents in Korea Using Auto-Regressive Integrated Moving Average Model
- Other Titles
- Time Series Forecasting on Car Accidents in Korea Using Auto-Regressive Integrated Moving Average Model
- Authors
- 신현경
- Issue Date
- Dec-2019
- Publisher
- 중소기업융합학회
- Keywords
- 시계열 분석; 확률 론적 고정 시계열; ARIMA 모델; ADF (Augmented Dickey-Fuller) 테스트; 예측; 자동차 사고 데이터; Time series analysis; stochastic stationary time series; ARIMA model; Augmented Dickey-Fuller (ADF) test; Forecasting; Car accident data
- Citation
- 융합정보논문지, v.9, no.12, pp.54 - 61
- Journal Title
- 융합정보논문지
- Volume
- 9
- Number
- 12
- Start Page
- 54
- End Page
- 61
- ISSN
- 2586-4440
- Abstract
- Recently, IITS (intelligent integrated transportation system) has been important topic in Smart City related industry. As a main objective of IITS, prevention of traffic jam (due to car accidents) has been attempted with help of advanced sensor and communication technologies. Studies show that car accident has certain correlation with some factors including characteristics of location, weather, driver’s behavior, and time of day. We concentrate our study on observing auto correlativity of car accidents in terms of time of day. In this paper, we performed the ARIMA tests including ADF (augmented Dickey-Fuller) to check the three factors determining auto-regressive, stationarity, and lag order. Summary on forecasting of hourly car crash counts is presented, we show that the traffic accident data obtained in Korea can be applied to ARIMA model and present a result that traffic accidents in Korea have property of being recurrent daily basis.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 경영대학 > 금융수학과 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.