Modelling risk aversion using a disaggregate stochastic process model in congested transit networks

Abstract

Existing transit assignment models which either use equilibrium or stochastic processes assume full knowledge of the network. This assumption leads to an assignment process wherein passengers renounce their individual experiences to base their route choice on the collective experiences on each route. This seems unrealistic especially in a congested network in the absence of any external source of information. The fact that passengers failing to board a service of their choice experience a different level of reliability to a passenger being able to board the same needs to be acknowledged and a model, sensitive to the fact that reliability is an individual entity, needs to be explored. The stochastic process model proves to be one of the advantageous methods for countering the asymmetric non-separable nature of strict capacity constraint transit assignment, thereby making it a possible choice for modelling reliability. In the current paper such a ‘Reliability based disaggregate stochastic process model (R-DSPM)’ following the Markov principles with strict capacity constraints is proposed. The R-DSPM framework provides a stationary and ergodic process model. The model is implemented onto an example network and its sensitivity to various parameters is discussed along with a case study.