Using GPS Data to Help Cities in Emergencies

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In October of 2012, Hurricane Sandy struck the East Coast of the United States causing billions of dollars in damage. The super storm became the most destructive hurricane of the season but it also provided transportation researchers a new way to analyze the effect that extreme events have on traffic patterns in major cities. Engineers from the University of Illinois used GPS data gathered from New York City taxis with the hope that municipalities can better study the strength of their transportation systems in emergencies using their methods.

In order to evaluate New York City’s traffic in response to Hurricane Sandy, assistant professor Dan Work and graduate student Brian Donovan first had to determine what constituted the city’s normal traffic patterns, and the two chose to analyze the GPS data regularly recorded from taxis. This data displayed both travel times as well as the distance of almost 700 million taxi trips around the city during the day and night, a data set that represented about four years of taxi travel.

The large amount of data was a challenge of its own. Donovan and Work needed the right tools in order to process that much data and then somehow turn it into information that can be acted upon. They also needed algorithms that responded within reasonable timeframes. When disasters strike, answers are needed immediately.

The two requested the taxi data from the New York City Taxi and Limousine Commission under the Freedom of Information Law. The GPS data proved to be superior to traditional methods of monitoring traffic, including roadway sensors or traffic cameras. That equipment is expensive and can be difficult to employ throughout a metropolis like New York City.

Using this GPS data, Work and Donovan developed a computational method of analyzing traffic by determining normalized travel times, or pace distributions, between the various regions of the city. Just like monitoring a normal heartbeat, the pair observed traffic patterns expected for a major urban area – light traffic in the middle of the night and congestion every morning during rush hour. They then looked for arrhythmias by measuring the distribution of pace during an unusual event like Hurricane Sandy.

A visualization comparing GPS data from New York City taxis in the days surrounding Hurricane Sandy with the same data under normal traffic conditions.

A visualization comparing GPS data from New York City taxis in the days surrounding Hurricane Sandy with the same data under normal traffic conditions.

What they found was surprising. The research showed that the greatest traffic delays took place not during the evacuation but as people returned to the city to resume their normal lives. However, most of the disaster literature is focused on getting people out quickly and safely as the initial response. Work remarked that there is a need for measures to facilitate re-entry because cities do not want their first responders stuck in gridlock.

This project from Work and Donovan demonstrates that additional research on transportation resiliency is needed, especially for the initial period following a disaster. A resilient system is one that can bounce back relatively quickly with minimal service disruption, and cities can use Work and Donovan’s methods to determine how their traffic systems respond to extreme events. The ultimate goal, according to Work, is to enhance post-disaster transportation policy as well as management.

Reference:

“Taxi GPS data helps researchers study Hurricane Sandy’s effect on NYC traffic.” http://engineering.illinois.edu/news/article/9717

 


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