Forecasting with strategic transport models corrected for endogeneity

Abstract

The correction of endogeneity is a problem in strategic transport modelling; the question remains on how to make appropriate forecasts in this case. We propose a variation of the classical Control Function (CF) method, called Control Function Updated (CFU), which considers updating the endogeneity correction using information from the future equilibria. The proposed method is assessed using Monte Carlo simulation for a strategic transport model affected by three endogeneity sources, examining the equilibrium results for various future scenarios. We compare the CFU method by doing nothing and with the classical CF approach. The forecasts are evaluated in terms of recovering the true (simulated) travel times and two indices of fit. Results show that the endogenous (do nothing) model produces large biases in simulated travel times and poor goodness-of-fit measures that steeply worsen with time in future scenarios. The corrected models perform much better and, in particular, the new CFU approach shows statistically significant improvements over the classical approach in all scenarios tested.