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http://dx.doi.org/10.22156/CS4SMB.2019.9.12.054

Time Series Forecasting on Car Accidents in Korea Using Auto-Regressive Integrated Moving Average Model  

Shin, Hyunkyung (Department of Financial Mathematics, Gachon University)
Publication Information
Journal of Convergence for Information Technology / v.9, no.12, 2019 , pp. 54-61 More about this Journal
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.
Keywords
Time series analysis; stochastic stationary time series; ARIMA model; Augmented Dickey-Fuller (ADF) test; Forecasting; Car accident data;
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