Estimation of path travel time distributions in stochastic time-varying networks with correlations

Monika Filipovska, Hani S. Mahmassani*, Archak Mittal

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial datamining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

Original languageEnglish (US)
Title of host publicationTransportation Research Record
PublisherSAGE Publications Ltd
Pages498-508
Number of pages11
Edition11
DOIs
StatePublished - 2021

Publication series

NameTransportation Research Record
Number11
Volume2675
ISSN (Print)0361-1981
ISSN (Electronic)2169-4052

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

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