Using Big Data and Efficient Methods to Capture Stochasticity for Calibration of Macroscopic Traffic Simulation Models

The predictions of a well-calibrated traffic simulation model are much more valid if made for various conditions. Variation in traffic can arise as a result of many factors such as time of day, weather, and accidents. Calibration of traffic simulation models for traffic conditions requires larger datasets to capture the stochasticity in traffic conditions. “Big Data” collected using radio frequency identification (RFID) sensors, smart cellphones, and GPS devices provide extensive traffic data. This study shows the utility of Big Data to incorporate variability in traffic flow and speed for various time periods. However, Big 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. This paper proposes 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. This paper shows that this methodology is much more efficient that the traditional Monte Carlo (MC)–type sampling.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: pp 215-229
  • Monograph Title: Celebrating 50 Years of Traffic Flow Theory: A Symposium. August 11-13, 2014, Portland, Oregon
  • Serial:

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Filing Info

  • Accession Number: 01604728
  • Record Type: Publication
  • Files: TRIS, TRB, ATRI
  • Created Date: Jul 6 2016 11:51AM