Machine Learning

Measuring the Last-Mile: Leveraging Synthetic Data to Evaluate the Effects of Urban Freight Interventions

In most urban areas, there is currently a misalignment between difficult-to-measure freight vehicle demands and infrastructure capacities (e.g., parking and loading zones) to accommodate these demands. This misalignment typically results in high costs for industry (e.g., wasted time, wasted fuel, parking fines) as well as in congestion, traffic conflicts, and other externalities for the surrounding […]

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

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

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How Much Do Attitudinal Variables Improve Travel Demand Models? Evaluation Using an Overlap Sample from an Attitude-rich Survey and the 2017 National Household Travel Survey

A line of research has recently been launched on attitude imputation using machine learning (ML) functions trained on variables common to two survey datasets (Mokhtarian, 2024). It was discovered that using a handful of attitudinal marker variables (i.e., the one or two attitudinal items most strongly associated with each attitude) as common variables for imputation

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Exploring Top-Down Visual Attention for Transportation Behavior Analysis

This project stands at the intersection of cognitive psychology, AI and computer vision, and transportation safety and efficiency. By focusing on the nuanced ways in which humans allocate their visual attention, and how this can inform the development of artificial intelligence (AI) and machine learning (ML) to aid in self-driving cars, transportation safety automation, and

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How Effective Are Marker Variables at Predicting Attitudinal Factor Scores? An Out-of-Sample Evaluation

Despite the fact that our existing models are not up to the job of predicting travel behavior in today’s rapidly changing landscape, and despite considerable evidence that attitudes help us explain behavior more completely and more meaningfully, attitudes are nowhere to be found in practice-oriented travel demand forecasting models.  Two main objections have been raised

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