Reliable route guidance: A case study from Chicago

Yu Nie*, Xing Wu, John F. Dillenburg, Peter C. Nelson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

Reliable route guidance can be obtained by solving the reliable a priori shortest path problem, which finds paths that maximize the probability of arriving on time. The goal of this paper is to demonstrate the benefits and applicability of such route guidance using a case study. An adaptive discretization scheme is first proposed to improve the efficiency in computing convolution, a time-consuming step used in the reliable routing algorithm to obtain path travel time distributions. Methods to construct link travel time distributions from real data in the case study are then discussed. Particularly, the travel time distributions on arterial streets are estimated from linear regression models calibrated from expressway data. Numerical experiments demonstrate that optimal paths are substantially affected by the reliability requirement in rush hours, and that reliable route guidance could generate up to 5-15% of travel time savings. The study also verifies that existing algorithms can solve large-scale problems within a reasonable amount of time.

Original languageEnglish (US)
Pages (from-to)403-419
Number of pages17
JournalTransportation Research Part A: Policy and Practice
Volume46
Issue number2
DOIs
StatePublished - Feb 2012

Keywords

  • Case study
  • Linear regression
  • Reliable a priori shortest path problem
  • Route guidance

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation
  • Management Science and Operations Research

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