University of Illinois Urbana-Champaign

Stochastic numerical approximation approaches for estimation of traffic volume under travel demand uncertainties

Shukla, Kumar Neelotpal

Content Files
SHUKLA-THESIS-2017.pdf

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https://hdl.handle.net/2142/99117

Description

Title
Stochastic numerical approximation approaches for estimation of traffic volume under travel demand uncertainties
Author(s)
Shukla, Kumar Neelotpal
Issue Date
2017-07-19
Director of Research (if dissertation) or Advisor (if thesis)
Meidani, Hadi
Department of Study
Civil & Environmental Eng
Discipline
Civil Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2018-03-02T19:59:44Z
Keyword(s)
  • Trip assignment
  • User equilibrium
  • Variational inequality
  • Smolyak sparse grid
  • Polynomial chaos expansions
Abstract
The traditional deterministic process of trip assignment does not account for uncertainties in traffic demands. These point-estimate based solutions often results in large differences between forecasted and actual traffic volumes thereby imposing huge financial burdens upon development agencies. In this work, stochastic treatment has been given to the trip assignment problem, specifically the network user equilibrium problem solved using the variational inequality method, under demand uncertainties modeled as random inputs. Smolyak sparse grid interpolation technique was successfully applied to the problem and compared to Monte Carlo sampling. Performance of constructed interpolant was evaluated through output distribution recovery , statistical moment estimation, and computation time comparisons. Ability of sparse grid to efficiently handle demand uncertainties using as many as 5 times fewer points than Monte Carlo sampling in pragmatically sized transportation networks was demonstrated.
Graduation Semester
2017-08
Type of Resource
text
Permalink
http://hdl.handle.net/2142/99117
Copyright and License Information
Copyright 2017 Kumar Neelotpal Shukla

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