Empirically quantifying city-scale transportation system resilience to extreme events

Brian Donovan, Daniel B. Work

Research output: Contribution to journalArticle

Abstract

This article proposes a method to quantitatively measure the resilience of transportation systems using GPS data from probe vehicles such as taxis. The granularity of the GPS data necessary for the method is relatively coarse; it only requires coordinates for the beginning and end of trips, the metered distance, and the total travel time. The method works by computing the historical distribution of pace (normalized travel times) between various regions of a city and measuring the pace deviations during an unusual event. Periods of time containing extreme deviations are identified as events. The method is applied to a dataset of nearly 700 million taxi trips in New York City, which is used to analyze the city transportation infrastructure resilience to Hurricane Sandy. The analysis indicates that Hurricane Sandy impacted traffic conditions for more than five days, and caused a peak delay of two minutes per mile. Practically, it identifies that the evacuation announcements coincided with only minor disruptions, but significant delays were encountered during the post-disaster response period. Since the implementation of this method is very efficient, it could potentially be used as an online monitoring tool, representing a first step toward quantifying city scale resilience with coarse GPS data.

Original languageEnglish (US)
Pages (from-to)333-346
Number of pages14
JournalTransportation Research Part C: Emerging Technologies
Volume79
DOIs
StatePublished - Jun 1 2017

Keywords

  • GPS
  • Taxi data
  • Transportation data analytics
  • Urban resilience

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

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  • Datasets

    New York City Taxi Trip Data (2010-2013)

    Donovan, B. (Creator) & Work, D. (Creator), University of Illinois at Urbana-Champaign, May 19 2016

    Dataset

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