Forecasting urban travel demand for quick policy analysis with disaggregate choice models: A Monte Carlo simulation approach

Abstract

A spatial aggregation methodology based on continuous mathematical functions is employed in urban passenger travel demand prediction. The approach derives aggregate travel d models in the form of multi-dimensional integrals which are solved by Monte Carlo simulation. Approximate empirical relationships are developed in the paper to examine the statistical properties of biases and random errors in Monte Carlo prediction. The methodology has been used in developing a comprehensive urban travel demand prediction model suitable for policy-sensitive sketch planning.