From Reactive to Predictive: Modeling Urban Event Impacts on Transportation Systems

Term Start:

June 1, 2025

Term End:

August 31, 2026

Budget:

$130,001

Keywords:

Event Detection, Machine Learning, Mobility Prediction

Thrust Area(s):

Data Modeling and Analytic Tools

University Lead:

City College of New York

Researcher(s):

Mahdieh Allahviranloo

Every day, New York City hosts countless events ranging from street festivals and protest marches to unexpected incidents and major sporting events. Each of these events may create ripples through the city’s complex transportation network, affecting how millions of New Yorkers move around their city. But what if we could predict these ripples? This proposal investigates how events change traffic patterns in New York City.

To address this objective, we will use three sources of data: GDELT data, road traffic count data, and subway ridership data. GDELT is a vast database that captures events happening around the world, including every significant happening in New York City. Traffic count data contains information on how many vehicles flow through different streets at different times. Finally, we are incorporating MTA ridership numbers, showing us how people use the subway system. Advanced data mining and machine learning tools will be used to conduct research tasks.

Project scope

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