Applying Derived Distribution Method to Microlevel Driving Behavior Characteristics to Quantify Uncertainties in Traffic Stream Flow and Density
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6, Issue 1
Abstract
The flows and densities of traffic streams play an important role in defining the performance of roadways and corresponding improvement strategies. Traffic flows and densities are the outcome of complex psychophysical actions of drivers. Actions performed by the drivers while driving can be quantified in terms of the headway and/or spacing that they maintain with respect to the vehicles they follow. However, the inherent randomness that exists in human driving behaviors results in random headway and spacing, which leads to uncertainties in predicted traffic flows and densities. As a result, it is important to quantify these uncertainties, because they play an important role in proposing improvement strategies. In this study, a derived distribution method–based uncertainty quantification of traffic flows and densities is proposed; it involves the modification of deterministic flow–headway and density–spacing relationships into probabilistic ones. Analytical expressions were derived for the probability distributions of flows and densities, given the headway and spacing distributions, respectively, which are conditional on velocities. The estimation of the distribution parameters and the validation of the proposed approach were carried out using the Next Generation Simulation (NGSIM) trajectory dataset. The results indicated that the proposed analytical distribution models represented empirical field observations quite accurately.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request. The following items are available:
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NGSIM trajectory data; and
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Code for processing the trajectory data and obtaining the spacing and headway.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6 • Issue 1 • March 2020
Copyright
©2019 American Society of Civil Engineers.
History
Received: Nov 30, 2018
Accepted: Jun 25, 2019
Published online: Dec 9, 2019
Published in print: Mar 1, 2020
Discussion open until: May 9, 2020
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