This paper proposes a new dynamic model to describe road users' day-to-day behavioural changes with guidance from a Reliable Path Searching System (RPSS) in the Advanced Traffic Information Systems. When given a desired on-time arrival probability, the RPSS provides a suggested travel time budget (TTB) to road users for planning their trips in the stochastic road network with day-to-day traffic demand variation. A discrete-time day-to-day learning mechanism is adopted to capture how road users perceive the stochastic travel times from dayto- day. A Bayesian dynamic process is used to model the adjustments of road users'TTBs with guidance from the RPSS. Both the learning mechanism and the Bayesian dynamic process are incorporated into the day-to-day dynamic model. The properties of the day-to-day dynamic model are then presented, and a solution algorithm is proposed to solve the day-to-day dynamics. Numerical examples are used to illustrate the applications of the day-to-day dynamic model and the proposed algorithm. The effects of the accuracy level of the guidance information on road users' behavioural change are also investigated through sensitivity tests. In addition, the day-to-day dynamic model is further used to examine the day-to-day dynamics when there are changes in road capacities due to temporary roadwork or network expansion. This paper represents a conjecture on how road users adjust their TTBs and route choices with reference to the desired on-time arrival probabilities in stochastic road network. And the numerical results on road users' behavioural change over time can provide insights into the evolution trajectories of traffic state over time for consistent evaluation of network performance.
- Bayesian theory
- On-time arrival probability
- Reliable Path Searching System
- Road users' behavioural change over time
- Stochastic traffic demand
ASJC Scopus subject areas
- Modelling and Simulation