A lossless spatial aggregation procedure for a class of capacity constrained traffic assignment models incorporating point queues

In this paper two novel spatial aggregation procedures are proposed. A network aggregation procedure based on a travel time delay decomposition method and a zonal aggregation procedure based on a path redistribution scheme. The effectiveness of these procedures lies in the fact that they, unlike existing aggregation methods, exploit available information regarding the application context and the characteristics of the adopted traffic assignment procedure. The context considered involves all applications that require path and inter-zonal travel times as output. A typical example of such applications are quick-scan methods, which have become increasing popular in recent years. The proposed procedures are compatible with a class of traffic assignment procedures incorporating (residual) point queues. Furthermore, one can choose to combine network aggregation with zonal aggregation to increase the effectiveness of the procedure. Results are demonstrated via theoretical examples as well as a large-scale case study. In the case study it is shown that network loading times can be reduced to as little as 4% of the original situation without suffering any information loss

Capacity constrained stochastic static traffic assignment with residual point queues incorporating a proper node model

Static traffic assignment models are still widely applied for strategic transport planning purposes in spite of the fact that such models produce implausible traffic flows that exceed link capacities and predict incorrect congestion locations. There have been numerous attempts in the literature to add capacity constraints to obtain more realistic traffic flows and bottleneck locations, but so far there has not been a satisfactory model formulation. After reviewing the literature, we come to the conclusion that an important piece of the puzzle has been missing so far, namely the inclusion of a proper node model. In this paper we propose a novel path-based static traffic assignment model for finding a stochastic user equilibrium in which we include a first order node model that yields realistic turn capacities, which are then used to determine consistent traffic flows and residual point queues. The route choice part of the model is specified as a variational inequality problem, while the network loading part is formulated as a fixed point problem. Both problems are solved using existing techniques. We illustrate the model using hypothetical examples, and also demonstrate feasibility on large-scale networks

General solution scheme for the Static Link Transmission Model

Until the present day most static traffic assignment models are neither capacity constrained nor storage constrained. Recent studies have resulted in novel approaches that consider capacity constraints and sometimes storage constraints. We build upon the results of these works and the model formulated in our companion paper Bliemer and Raadsen (2018a) which comprises a static assignment model formulation that is both capacity constrained as well as storage constrained. The formulation of this model is derived from a continuous time dynamic network loading model proposed in Bliemer and Raadsen (2018b). The prospect of being able to capture spillback effects in static assignment provides new opportunities for making this modelling method more capable. It is well known that the absence of spillback typically results in significant underestimation of path travel times. This is especially true for paths that do not traverse bottleneck(s) directly, but that are affected by the space occupied of queues that are spilling back. Similar to Smith (2013) and Smith et al. (2013), Bliemer and Raadsen (2018a) did not provide a solution algorithm. In this paper, we take their model formulation and propose a general solution scheme suitable for large scale networks