Empirically quantifying city-scale transportation system resilience to extreme events

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 postdisaster 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.In recent years, resilience of city infrastructure has gained a great deal of attention [1]. When disasters and other extreme events occur, infrastructure may fail, incurring large human, economic, and environmental costs. This is especially relevant for transportation infrastructure, since it is crucial for city evacuations and emergency services in post-disaster environments. Methods are needed to quantitatively monitor the transportation infrastructure in terms of its ability to withstand and recover from such events. Measuring the performance of city-scale infrastructure with traditional traffic sensors is cost-prohibitive due to relatively high installation costs, but many cities already have taxi fleets equipped with GPS sensors. Though this analysis could be performed with any GPS data, taxi data is publicly available in some cases. The New York City dataset used in this analysis gives interesting insights about the performance of infrastructure during Hurricane Sandy and other major events.The goal of this article is to develop and implement a method for measuring resilience of city-scale transportation networks using only taxi datasets. The technique is designed with the following characteristics:1. The method can be applied at the city-scale, or larger. Extreme events such as hurricanes have the ability to affect an entire city. For this reason, it is important to examine impacts at a high-level city view, rather than the level of individual vehicles or streets. * bpdonov2@illinois.edu † dbwork@illinois.edu arXiv:1507.06011v1 [physics.soc-ph] 21 Jul 2015 2. The method measures network performance quantitatively, in terms of recovery time and peak pace deviations. Recovery time and peak performance degradation are fairly standard quantities of interest in the resilience literature [2,3]. While travel times are a natural performance m...

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