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Challenges of Microsimulation Calibration with Traffic Waves using Aggregate Measurements |
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Content: |
This work explores the challenges associated with calibrating parameters of microscopic models with aggregate speed data, e.g., obtained from roadside sensors. Using the Intelligent Driver Model, we explore how reliably parameters that do not influence the equilibrium flow (i.e., the Fundamental Diagram), but do control the stability of those equilibria, can be determined from aggregate speed data. Using a carefully controlled computational setup, we show that standard loss functions used for calibrating microsimulation models can perform poorly when the true parameters result in an unstable traffic state. Precisely, it is found that all of the considered loss functions frequently return different and incorrect parameter sets that minimize the expected value of the loss function. These results highlight the need for improved loss functions, or even fundamental additions to the model calibration procedure. |
Id: |
1 |
Place: |
Room: |
Starting date: |
06-Nov-2020 |
10:20 (America/Chicago) |
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Duration: |
05' |
Primary Authors: |
Mr. SHANTO, Sadman Ahmed (Texas Tech University) |
Presenters: |
Mr. SHANTO, Sadman Ahmed |
Material: |
Paper Poster Minutes |
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