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Title:Stochastic numerical approximation approaches for estimation of traffic volume under travel demand uncertainties
Author(s):Shukla, Kumar Neelotpal
Advisor(s):Meidani, Hadi
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(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.
Issue Date:2017-07-19
Rights Information:Copyright 2017 Kumar Neelotpal Shukla
Date Available in IDEALS:2018-03-02
Date Deposited:2017-08

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