Using coarse GPS data to quantify city-scale transportation system resilience to extreme events

TL;DR

This paper introduces a method to assess city transportation resilience to extreme events using coarse GPS data from taxis, by analyzing pace deviations during disruptions like hurricanes.

Contribution

It presents a novel, efficient approach to quantify transportation resilience with minimal GPS data, applicable for real-time monitoring of urban systems.

Findings

Hurricane Sandy caused traffic delays lasting over five days.

Peak delays reached two minutes per mile during the disaster.

Post-disaster reentry caused significant traffic disruptions.

Abstract

This article proposes a method to quantitatively measure the resilience of transportation systems using GPS data from taxis. The granularity of the GPS data necessary for this analysis 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. This method is applied to a dataset of nearly 700 million taxi trips in New York City, which is used to analyze the 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 caused only minor disruptions, but significant delays were encountered during the post-disaster reentry process. 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.