Short-term traffic flow prediction using seasonal ARIMA model with limited input data

Abstract

Background Accurate prediction of traffic flow is an integral component in most of the Intelligent Transportation Systems (ITS) applications. The data driven approach using Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models reported in most studies demands sound database for model building. Hence, the applicability of these models remains a question in places where the data availability could be an issue. The present study tries to overcome the above issue by proposing a prediction scheme using Seasonal ARIMA (SARIMA) model for short term prediction of traffic flow using only limited input data. Method A 3-lane arterial roadway in Chennai, India was selected as the study stretch and limited flow data from only three consecutive days was used for the model development using SARIMA. After necessary differencing to make the input time series a stationary one, the autocorrelation function (ACF) and partial autocorrelation function (PACF) were plotted to identify the suitable order of the SARIMA model. The model parameters were found using maximum likelihood method in R. The developed model was validated by performing 24 hrs. ahead forecast and the predicted flows were compared with the actual flow values. A comparison of the proposed model with historic average and naive method was also attempted. The effect of increase in sample size of input data on prediction results was studied. Short term prediction of traffic flow during morning and evening peak periods was also attempted using both historic and real time data. Concluding remarks The mean absolute percentage error (MAPE) between actual and predicted flow was found to be in the range of 4-10, which is acceptable in most of the ITS applications. The prediction scheme proposed in this study for traffic flow prediction could be considered in situations where database is a major constraint during model development using ARIMA.

Optimización de un Sistema de Abastecimiento de Pintura a Concesionarios de Baja y Media Demanda

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Electricity consumption prediction based on LSTM with attention mechanism

“…Therefore, a new method should be proposed to solve such problems. Currently, there are some traditional statistical‐based models, such as Holt‐Winters model and the Auto Regressive Integrated Moving Average (ARIMA) model . The Holt‐Winters model is used to predict the electricity consumption of the data centers, which can remarkably increase the energy efficiency of data centers .…”

“…E-mail: kevinwong@gdut.edu.cn *Laboratory of Cyber-Physical System, Department of Computer Science and Technology, School of Computes, Guangdong University of Technology, Guangzhou, China **School of Computes, Guangdong University of Technology, Guangzhou, China to solve such problems. Currently, there are some traditional statistical-based models, such as Holt-Winters model [4,5] and the Auto Regressive Integrated Moving Average (ARIMA) model [6,7]. The Holt-Winters model is used to predict the electricity consumption of the data centers, which can remarkably increase the energy efficiency of data centers [8].…”

Variation Trend Analysis of Runoff and Sediment Time Series Based on the R/S Analysis of Simulated Loess Tilled Slopes in the Loess Plateau, China

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