2013
DOI: 10.1080/15472450.2013.806854
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Disaggregate Path Flow Estimation in an Iterated Dynamic Traffic Assignment Microsimulation

Abstract: This text describes the first application of a novel path flow and origin/destination (OD) matrix estimator for iterated dynamic traffic assignment (DTA) microsimulations. The presented approach, which operates on a trip-based demand representation, is derived from an agent-based DTA calibration methodology that relies on an activity-based demand model (Flötteröd et al., 2011a). The objective of this work is to demonstrate the transferability of the agent-based approach to the more widely used OD matrix-based … Show more

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Cited by 15 publications
(6 citation statements)
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“…Further improvement can potentially be achieved by a more comprehensive and diverse initial pool of routes. Additionally, different optimization algorithms like Cadyts [31]- [33] can be applied for a comparison of traffic flow calibration procedures. In order to increase the accuracy of the simulation, fixed-time traffic lights must be replaced by a traffic adaptive traffic light control using rule-based or data-driven techniques.…”
Section: Discussionmentioning
confidence: 99%
“…Further improvement can potentially be achieved by a more comprehensive and diverse initial pool of routes. Additionally, different optimization algorithms like Cadyts [31]- [33] can be applied for a comparison of traffic flow calibration procedures. In order to increase the accuracy of the simulation, fixed-time traffic lights must be replaced by a traffic adaptive traffic light control using rule-based or data-driven techniques.…”
Section: Discussionmentioning
confidence: 99%
“…At the upper level (1a), the decision variable (𝑋) is the vector of time-dependent OD flows, and the problem can be presented as the constrained generalized/ordinary least squares (GLS/OLS) problem. Furthermore, at the lower level (1a), the traffic assignment 𝑨(𝑿) is derived analytically (Maher et al 2001), according to simulation-based models (Flötteröd and Liu 2014;Shafiei et al 2018;Zhang et al 2021), based on machine learning as dynamically learn mapping between link flow and OD demands according to the traffic measurements data (Ou et al 2019;Krishnakumari et al 2020), or combinations of them (Osorio 2019a).…”
Section: Fig 1 General Bi-level Od Estimation Frameworkmentioning
confidence: 99%
“…At the same time, within-day and day to day dynamics of traffic re-routing has been considered by Bie and Lo (2010), Cantarella and Cascetta (1995), Flötteröd andLiu (2014), Liu et al (2006), Smith (1984a) and others. These papers do not involve RPAP.…”
Section: A1 Route Choice Modellingmentioning
confidence: 99%