Robust calibration of macroscopic traffic simulation models using stochastic collocation

Sandeep Mudigonda, Kaan Ozbay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The predictions of a well-calibrated traffic simulation model are much more valid if made for various conditions. Variation in traffic can arise due to many factors such as time of day, work zones, weather, etc. Calibration of traffic simulation models for traffic conditions requires larger datasets to capture the stochasticity in traffic conditions. In this study we use datasets spanning large time periods to incorporate variability in traffic flow, speed for various time periods. However, large data poses a challenge in terms of computational effort. With the increase in number of stochastic factors, the numerical methods suffer from the curse of dimensionality. In this study, we propose a novel methodology to address the computational complexity due to the need for the calibration of simulation models under highly stochastic traffic conditions. This methodology is based on sparse grid stochastic collocation, which, treats each stochastic factor as a different dimension and uses a limited number of points where simulation and calibration are performed. A computationally efficient interpolant is constructed to generate the full distribution of the simulated flow output. We use real-world examples to calibrate for different times of day and conditions and show that this methodology is much more efficient that the traditional Monte Carlo-type sampling. We validate the model using a hold out dataset and also show the drawback of using limited data for the calibration of a macroscopic simulation model. We also discuss the drawbacks of using a single calibrated model for all the data.

Original languageEnglish (US)
Title of host publicationTransportation Research Procedia
PublisherElsevier
Pages1-20
Number of pages20
Volume9
DOIs
StatePublished - 2015

Fingerprint

Stochastic models
simulation model
Calibration
traffic
time of day
methodology
Computational complexity
Numerical methods
Sampling
simulation

Keywords

  • calibration
  • macroscopic traffic flow
  • stochastic collocation

ASJC Scopus subject areas

  • Transportation

Cite this

Mudigonda, S., & Ozbay, K. (2015). Robust calibration of macroscopic traffic simulation models using stochastic collocation. In Transportation Research Procedia (Vol. 9, pp. 1-20). Elsevier. https://doi.org/10.1016/j.trpro.2015.07.001

Robust calibration of macroscopic traffic simulation models using stochastic collocation. / Mudigonda, Sandeep; Ozbay, Kaan.

Transportation Research Procedia. Vol. 9 Elsevier, 2015. p. 1-20.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mudigonda, S & Ozbay, K 2015, Robust calibration of macroscopic traffic simulation models using stochastic collocation. in Transportation Research Procedia. vol. 9, Elsevier, pp. 1-20. https://doi.org/10.1016/j.trpro.2015.07.001
Mudigonda S, Ozbay K. Robust calibration of macroscopic traffic simulation models using stochastic collocation. In Transportation Research Procedia. Vol. 9. Elsevier. 2015. p. 1-20 https://doi.org/10.1016/j.trpro.2015.07.001
Mudigonda, Sandeep ; Ozbay, Kaan. / Robust calibration of macroscopic traffic simulation models using stochastic collocation. Transportation Research Procedia. Vol. 9 Elsevier, 2015. pp. 1-20
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