Application of time series techniques for forecasting truck traffic attracted by the Bombay metropolitan region

Abstract

Knowledge of future traffic flow is an essential input in the planning, implementation and development of a transportation system. It also helps in its operation, management and control. Time series analysis techniques have been extensively adopted for this purpose in the fields of economics, social sciences and in other fields of technology. An attempt has been made in this study to apply the techniques of time series analysis to goods traffic, particularly truck traffic. Four predominant corridors, N.H.3, N.H.4, N.H.8 and Lal Bahadur Shastri Road (L.B. S. Rd.), accounting for majority of truck movement in the Bombay Metropolitan Region (BMR), have been considered for modeling. Raw data was processed initially to obtain an insight into the structure of time series. Ten candidate models of the Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA) family are investigated to represent each of the four corridors. Models finally proposed to represent each of the four corridors have been selected based on Minimum Mean Square Error (MMSE) and Maximum Likelihood Rule (MLR) criteria. Models ARIMA (2, 1, 0), ARMA (1.0), ARMA (1, 1) and ARIMA (1, 1, 0) are proposed for N.H.3, N.H.4, N.H.8 and L.B.S. Rd. respectively based on significant weekly periodicity.