Static Traffic Assignment with Queuing: model properties and applications

Abstract

This paper describes the road traffic assignment model Static Traffic Assignment with Queuing (STAQ) that was developed for situations where both static (STA) and dynamic (DTA) traffic assignment models are insufficient: strategic applications on large-scale congested networks. The paper demonstrates how the model overcomes shortcomings in STA and DTA modelling approaches in the strategic context by describing its concept, methodology and solution algorithm as well as by presenting model applications on (small) theoretical and (large) real-life networks. The STAQ model captures flow metering and spillback effects of bottlenecks like in DTA models, while its input and computational requirements are only slightly higher than those of STA models. It does so in a very tractable fashion, and acquires high-precision user equilibria (relative gap < 1E-04) on large-scale networks. In light of its accuracy, robustness and accountability, the STAQ model is discussed as a viable alternative to STA and DTA modelling approaches.

Emission estimation based on traffic models and measurements

“…5.2 Quasi-dynamic network loading as a post-processing approach STAQ (Brederode et al, 2010;Leeuwen, 2011;Bliemer et al, 2012;Brederode et al, 2018) is a network loading model based on the LWR first-order traffic flow model. Although in LWR the transitions between traffic states are not described in detail and could be unrealistic regarding the rates of acceleration (Treiber and Kesting, 2013), shockwave positions are accurately described.…”

Estimating Emissions from Static Traffic Models: Problems and Solutions

“…During the squeezing phase, each path share f * rs k of the static (first static assumption) demand, q rs , is loaded instantaneously (second static assumption) into the network through a path-based network loading model [43], which ensures that no link flow, Q a , a ∈ D rs,k , exceeds capacity. e squeezing phase is formulated as a fixed point problem which is iteratively solved [43,44], aiming at determining a set of consistent, path-desegregated sending and receiving flows for any network node. e queuing phase is based on the LWR first-order traffic flow model [45,46].…”

“…A route-choice submodel, then, computes new path demand shares which are inputs to the squeezing phase, and the procedure is repeated until some convergence criterion is satisfied. Brederode et al [44] demonstrated that STAQ can improve the modelling accuracy in congested networks compared to STA, without losing in robustness and accountability. Additionally, STAQ keeps the input and computational requirements at lower levels compared to DTA models.…”

“…Finally, at time ε 6 , a new forward wave will reach the downstream end a L . e main output of the event-based loading algorithms described in Bliemer et al [16] and Brederode et al [44] is average link travel times computed from the cumulative inflow and outflow curves. Besides the travel times, cumulative flow curves can provide some additional information on inflow and outflow rates and spatial average link densities.…”

Route Set Generation for Quick Scan Applications of Dynamic Traffic Assignment

“…However, for large congested networks the problem of choice set generation is particularly tough, given that there is a clear trade-off between including all relevant alternative routes for behavioral realism and limiting the overall number of routes for computational efficiency (or even feasibility). For example, the planning model for the Western part of the Randstad (The Netherlands) contains˜3.4k centroids and˜1.2M routes, while other important regional models in The Netherlands and Belgium contain up to˜4M routes [5].…”