New York City’s traffic puzzle: Can Artificial Intelligence solve fire truck delays?

January 29, 2024
FDNY Bronx New York Fire fighters

New York City is no stranger to traffic congestion, and when it comes to emergency response times, every second counts.

The New York Times recently published an investigation into a research project led by a consortium of seven universities known as C2SMARTER.

The project’s goal is to determine if artificial intelligence (AI) can be harnessed to expedite the arrival of fire trucks and emergency medical vehicles amidst the city’s bustling streets.

Understanding the traffic challenge

Fire Commissioner Laura Kavanagh stressed the urgency of improving response times, stating: “Every second counts when it comes to emergency response. Shorter response times are directly linked to better outcomes.”

The increasing complexity of city traffic patterns and conditions has posed a significant challenge to the Fire Department.

In part, this has led to longer response times for both medical emergencies and structural fires.

The impact of urban changes on response times

Last year, the average response time for medical emergencies in New York City clocked in at 7 minutes and 59 seconds, a considerable increase from 2013.

Similarly, response times for structural fires have also experienced delays.

These setbacks can be attributed to various factors, including changes in the urban landscape, such as protected bicycle and bus lanes and outdoor dining setups, all of which limit the maneuverability of emergency vehicles.

Assistant Fire Commissioner Rebecca Mason emphasized the importance of reducing travel time in emergency operations.

Addressing modern challenges

In addition to traffic-related obstacles, modern challenges like fires involving lithium-ion batteries have emerged, known for their rapid and volatile spread.

The contemporary building environment, characterized by materials more susceptible to quick ignition, has heightened the urgency of efficient response times.

Innovative approach with C2SMARTER

C2SMARTER, led by the transportation center at the N.Y.U. Tandon School of Engineering, is embarking on a groundbreaking project to tackle these challenges head-on.

Their approach involves collecting and analyzing real-time traffic data from a selected 30-block section of Harlem.

This data will be leveraged to create a ‘digital twin’—a computerized replica of the area—to simulate traffic delays and identify potential solutions.

Joseph Chow, associate director of C2SMARTER, said he believes that the project could significantly benefit historically underserved communities like Harlem by providing faster emergency services.

The project aims to replicate how various vehicles, including emergency units, navigate the neighborhood, effectively optimizing response routes.

Artificial intelligence at the core

At the heart of the project lies the use of AI to replicate driver behavior and responses to emergency vehicles.

Jinqin Gao, assistant director of research at C2SMARTER, elaborated that the AI software seeks to not only mimic traffic conditions but also the reactions of other vehicles to emergency situations.

FSJA Comment

The integration of AI into emergency response systems represents a forward-thinking approach to urban challenges.

C2SMARTER’s research has the potential to be a significant advancement in public safety, addressing the critical need for timely emergency response in densely populated urban environments.

This project serves as a testament to the synergy of technology and public service, offering a blueprint for other cities grappling with similar challenges.

If successful, it could lead to more efficient emergency responses, ultimately saving lives and enhancing public safety.

Moreover, the project’s focus on historically underserved communities like Harlem underscores the importance of equitable technology deployment in urban planning.

Read the New York Times investigation here.

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