Forecasting transportation demand for the U.S. market

In this paper we forecast air, road and train transportation demand for the U.S. domestic market based on econometric and machine learning methodologies. More specifically, we forecast transportation demand for various horizons up to 18 months ahead, for the period 2000:1–2015:03, employing, from the domain of machine learning, a Support Vector Regression (SVR) and from econometrics, the Least Absolute Shrinkage and Selection Operator and the Ordinary Least Squares regression. In doing so, we follow the relevant literature and consider the contribution of selected variables as potential regressors in forecasting. Our empirical findings suggest that while all models outperform the Random Walk benchmark, the machine learning applications adhere more closely to the data generating process, producing more accurate out-of-sample forecasts than the classical econometric models. In most cases, we find that the transportation demand is driven by fuel costs, except for road transportation where macroeconomic conditions affect transportation volumes only for specific forecasting horizons. This finding deviates from the existing literature, given the support of previous studies to macroeconomic conditions are driving factors of transportation demand. Our work relates directly to decisions on transport infrastructure improvement, while it can also be used as a forecasting tool in shaping transportation-oriented policies.