A Cell-Based Model for Multi-class Doubly Stochastic Dynamic Traffic Assignment

This paper proposes a cell-based multi-class dynamic traffic assignment problem that considers the random evolution of traffic states. Travelers are assumed to select routes based on perceived effective travel time, where effective travel time is the sum of mean travel time and safety margin. The proposed problem is formulated as a fixed point problem, which includes a Monte-Carlo-based stochastic cell transmission model to capture the effect of physical queues and the random evolution of traffic states during flow propagation. The fixed point problem is solved by the self-regulated averaging method. Numerical examples are set up to illustrate the properties of the problem and the effectiveness of the solution method. The key findings include the following: i) Reducing perception errors on traffic conditions may not be able to reduce the uncertainty of estimating system performance, ii) Using the selfregulated averaging method can give a much faster rate of convergence in most test cases compared with using the method of successive averages, iii) The combination of the values of the step size parameters highly affects the speed of convergence, iv) A higher demand, a better information quality, or a higher degree of the risk aversion can lead to a higher computation time, v) More driver classes do not necessary results in a longer computation time, and vi) Computation time can be significantly reduced by using small sample sizes in the early stage of solution processes. * To whom correspondence should be addressed. E-mail: [email protected]