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.

本文提出了一种使用具有剩余队列的静态容量约束交通分配 (SCCTA) 模型来解决矩阵估计问题的有效方法。除了传统的链路流和先验需求矩阵之外，该解决方案方法还允许包含路由排队延迟和拥塞模式，同时 SCCTA 模型的易处理性避免了对应用程序特定算法参数进行繁琐调整的需要。

所提出的解决方法解决了一系列简化的优化问题，从而避免了代价高昂的额外分配模型运行。链路状态约束用于防止在其有效范围之外使用近似值，并包括观察到的拥塞模式。所提出的求解方法被设计为快速、可扩展、稳健、易处理和可靠，因为简化优化问题的解存在的条件是已知的，并且因为问题是凸的并且具有平滑的目标函数。

展示了小型 Sioux Falls 模型上的四个测试案例应用程序，每个案例包含 100 次运行，输入不同，以确保稳健性。这些应用程序展示了在解决方案方法中包含观察到的拥塞模式和路由排队延迟的附加值。此外，在大规模 BBMB 模型上的应用表明，所提出的解决方法确实可扩展到大规模应用，并且明显优于当前实践中最常用的方法。