Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

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 the 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 twofold manner. Firstly, a Monte Carlo simulation-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining 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 Monte Carlo simulation approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches and its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a Monte Carlo simulation approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 19p

Subject/Index Terms

Filing Info

  • Accession Number: 01763999
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-03828
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:17AM