The Study of Aggregate Bi-Modal Urban Travel Supply, Demand and Network Behavior using Simultaneous Equations with Autoregressive Residuals

This paper emphasizes the existence of a difference among demand functions which describe how consumers react, supply functions, which analyze the behaviour of suppliers, the cost functions, which specify how prices and levels of service on a link or in a network vary with vehicle demand, supply and cost models are formulated: each one regroups a subset of demand, supply and cost functions for two modes in Montreal; a significant part of parameters of all models are estimated by at least two of four limited information and full information estimation techniques. All of these procedures are designed to take proper account of equation-specific auto correlation schemes of the residuals; i) a least squares generalized autoregressive estimator ii) a two stage least squares generalized autoregressive estimator; iii) an iterated version of Parks' seemingly unrelated procedure generalized to multiple auto correlation cases; iv) an iterative version of Fair's full information instrumental variables efficient estimator. It is shown that removing simultaneous equations biases from demand function estimates can modify the relative importance of waiting time and in-vehicle time elasticities of demand and the measures of the value of the time associated with them.