A stochastic process approach to the analysis of temporal dynamics in transportation networks

Equilibrium analyses of transportation networks are by their nature ‘static’, with equilibrium configuration defined as ‘fixed’ or ‘autoreflexive’ points, i.e. flow patterns reproducing themselves on the basis of the assumptions made on users’ behavior once reached by the system. In this paper it is argued that no transportation system remains in the same state over successive periods because of the action of several causes (e.g. temporal fluctuation of level and composition of demand, users’ choices, and travel costs). This implies that the sequence of states occupied by the system over successive epochs or times of similar characteristics (e.g. AM peak hour of working days) is the realization of a stochastic process, the type of which depends on, among other things, the choice mechanism followed by travelers. Stationarity of the stochastic process within fixed potential demand and network structures is considered to be a desirable property because it allows a flow pattern distributon to be associated to each demand-network system independently of its starting configuration and elapsed time. Furthermore, this stationarity makes it possible to define expected path and link flows and compare them with those of stochastic user equilibrium (SUE). In this paper rather general sufficient conditions for the process stationarity are given, essentially calling for a ‘‘stable’’ choice mechanism of potential users. In the following a particular model of temporal dynamics (STODYN), based upon a number of simplifying assumptions on users’ behavior common to most assignment models is described. Exact and approximate relationships between STODYN steady-state expected flows and SUE average flows are also analyzed both in the case of unique and multiple equilibria. The possible use of STODYN as an assignment model giving unique average flows along with their variances and covariances is then discussed. The model takes into account stochastic fluctuations of demand and can be easily extended to other ‘dimensions’ such as distribution and modal choice. Some results of an empirical analysis comparing STODYN average flows with SUE and observed flows on two urban car networks are also reported.