Network Econometrics and the Evolution of Transport Systems

Abstract

This thesis systematically develops a network correlation matrix that explicitly distinguishes competitive and complementary link pairs in transportation networks. Embedding the matrix in network econometric analysis, this thesis consolidates that incorporating representative spatial information with a network perspective is capable of improving the performance of traffic forecasting models. The method is validated in the context of a real-world transport system rather than within simulated settings adopted by previous research. An Autoregressive-Distributed Lag (ARDL) model is specified, and reveals that the combination of correlation strength and magnitude of lagged flow change on correlated links is an significant predictor of future traffic flow.

This thesis innovatively extends network econometric methods, previously exclusively used for traffic flow forecasting, to the domain of network structure prediction by specifying a logit model. It finds that complementary and competitive links play distinct roles in shaping the network structure. If positively correlated, a link is more likely to undergo the same structural change influential links underwent previously where the influence is measured by a combination of correlation strength and link importance, reflected by historical flow level.

Additionally, this thesis establishes a digitized database of the Sydney tramway system, providing a complete set of data for more research.