Travel demand matrix estimation for strategic road traffic assignment models with strict capacity constraints and residual queues. (January 2023) - Brederode, Luuk; Pel, Adam; Wismans, Luc; Rijksen, Bernike; Hoogendoorn, Serge
Highlights: An efficient solution method for large scale matrix estimation problems is proposed. The proposed solution method is solves a series of convex and smooth subproblems. The method can include observed queuing delays and congestion patterns. The added value of these inclusions are demonstrated on the small Sioux Falls model. The added value and scalability of the method is demonstrated on the large BBMB model. Abstract: This paper presents an efficient solution method for the matrix estimation problem using a static capacity constrained traffic assignment (SCCTA) model with residual queues. The solution method allows for inclusion of route queuing delays and congestion patterns besides the traditional link flows and prior demand matrix whilst the tractability of the SCCTA model avoids the need for tedious tuning of application specific algorithmic parameters. The proposed solution method solves a series of simplified optimization problems, thereby avoiding costly additional assignment model runs. Link state constraints are used to prevent usage of approximations outside their valid range as well as to include observed congestion patterns. The proposed solution method is designed to be fast, scalable, robust, tractable and reliable because conditions under which a solution to the simplified optimization problem exist are known and because the problem is convex and has a smooth objective function. Four test case applications on the small Sioux Falls model are presented, each consisting of 100 runs with varied input for robustness. The applications demonstrate the added value of inclusion of observed congestion patterns and route queuing delays within the solution method. In addition, application on the large scale BBMB model demonstrates that the proposed solution method is indeed scalable to large scale applications and clearly outperforms the method mostly used in current practice.