Computational Technology Resources - IJRT

Keywords: forecasting, railway, passenger service, SARIMA, State Space Models, Kalman filtering.

Abstract

Efficient management of a railway company requires planning. In order to be effective, it is necessary to have expectations of the future conditions under which the company will operate. Forecasting represents an indispensable activity in rail transportation planning. Forecasting future demand for rail passenger services is very difficult but is necessary if companies are to succeed. This paper presents a forecasting approach for railway passenger traffic using the popular Autoregressive Integrated Moving Average (ARIMA) models in state space form. It was found that the recurring pattern of the monthly rail passenger flows is well described by a Seasonal ARIMA, that is the SARIMA (0,1,1)(0,1,1)₁₂ model. The identified model was incorporated in the state space framework where classical Kalman recursion is applied for the calculation of forecasting values. The Kalman procedure is presented as an elegant approach for the prediction of SARIMA processes in state space form. Prediction performance of the developed SARIMA-Kalman model is compared with observed values, as well as with the outputs of simple seasonal exponential smoothing method, and demonstrate the capability and effectiveness of the proposed model in assisting managers to better predict rail passenger demand.

purchase the full-text of this paper (price £20)

go to the previous paper
go to the next paper
return to the table of contents
return to IJRT
purchase this issue (price £70 +P&P)

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	International Journal of Railway Technology ISSN 2049-5358 IJRT, Volume 3, Issue 2, 2014 A Recursive Kalman Filter Approach to Forecasting Railway Passenger Flows M. Milenkovic and N. Bojovic Division for Management in Railway, Rolling Stock and Traction, The Faculty of Transport and Traffic Engineering, University of Belgrade, Serbia doi:10.4203/ijrt.3.2.3 purchase the full-text of this paper Full Bibliographic Reference for this paper M. Milenkovic, N. Bojovic, "A Recursive Kalman Filter Approach to Forecasting Railway Passenger Flows", International Journal of Railway Technology, 3(2), 39-57, 2014. doi:10.4203/ijrt.3.2.3 Keywords: forecasting, railway, passenger service, SARIMA, State Space Models, Kalman filtering. Abstract Efficient management of a railway company requires planning. In order to be effective, it is necessary to have expectations of the future conditions under which the company will operate. Forecasting represents an indispensable activity in rail transportation planning. Forecasting future demand for rail passenger services is very difficult but is necessary if companies are to succeed. This paper presents a forecasting approach for railway passenger traffic using the popular Autoregressive Integrated Moving Average (ARIMA) models in state space form. It was found that the recurring pattern of the monthly rail passenger flows is well described by a Seasonal ARIMA, that is the SARIMA (0,1,1)(0,1,1)₁₂ model. The identified model was incorporated in the state space framework where classical Kalman recursion is applied for the calculation of forecasting values. The Kalman procedure is presented as an elegant approach for the prediction of SARIMA processes in state space form. Prediction performance of the developed SARIMA-Kalman model is compared with observed values, as well as with the outputs of simple seasonal exponential smoothing method, and demonstrate the capability and effectiveness of the proposed model in assisting managers to better predict rail passenger demand. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to IJRT purchase this issue (price £70 +P&P)
Back to top	©Civil-Comp Limited 2023 - terms & conditions