STATIONARY STATES IN STOCHASTIC PROCESS MODELS OF TRAFFIC ASSIGNMENT: A MARKOV CHAIN MONTE CARLO APPROACH

Abstract

Traditional methods of traffic assignment are now widely perceived to have serious shortcomings, particularly in relation to the modelling requirements arising from increasingly saturated networks, complex traffic control and emerging driver information systems. This has led to increased interest in more complex assignment models, including approaches incorporating stochastic elements to account for errors in drivers' perceptions and day-to-day variation in behaviour. However, whilst such newer, more complex models, have the scope to provide much greater realism, they are generally mathematically intractable. Furthermore, empirical investigation of these models by direct simulation tends to be impossible if travellers interact in a contemporaneous fashion. Nevertheless, simulation of such models is possible using statistical techniques known as Markov Chain Monte Carlo methods. The objective of this paper is to demonstrate how existing stochastic process models can potentially be generalized to permit more complex and realistic representations of traveller memory and travller interactions, and to show how the equilibria of such models can be computed using Markov Chain Monte Carlo techniques. The properties of different types of stochastic assignment models are investigated both theoretically, and through small and medium scale empirical examples. Avenues for further research in stochastic modelling of transport systems are discussed. (A) For the covering abstract see IRRD 886400.

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