Empirically quantifying city-scale transportation system resilience to extreme events. (June 2017)

• Record Type: Journal Article
• Authors: Donovan, Brian; Work, Daniel B.
• Is Part Of: Transportation research. Volume 79(2017)
• Page Start: 333
• Page End: 346
• Publication Date: 2017-06
• Subjects: GPS -- Taxi data -- Urban resilience -- Transportation data analytics; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2017.03.002
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: A transportation resilience measure based on origin destination paces is proposed. The method is applied to New York City using data from nearly 700, 000, 000 taxis. The method indicates minor disruptions are observed immediately before Hurricane Sandy. Grid-lock traffic conditions are observed following Hurricane Sandy. 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.