City College of New York

Exploring Top-Down Visual Attention for Transportation Behavior Analysis: Walkability and Pedestrian Behaviors

Employing the state-of-the-art research on attention and feedback mechanisms, especially with vision language models (VLMs), has not been fully explored previously for transportation behavior analysis, especially for the analysis of a variety of pedestrian behaviors related to sidewalks and streets. This project will expand our previous project exploring top-down visual attention for transportation behavior analysis […]

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Leveraging Vision-Language Models for Efficient Understanding of Vulnerable Roadway Users via a Multimodal Traffic Sensing Approach

The proliferation of 3D and video data from urban intersections offers a unique opportunity to analyze and protect vulnerable road users (VRUs). However, the effectiveness of modern detection models like PointPillar or CenterPoint is limited by the availability of high-quality labeled data. In Year 2, we demonstrated the feasibility of multimodal sensing using LiDAR and

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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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The Home as a Trip Attractor: An Exploratory Study of Residential Service Demand

Taking a two-phase approach, this study aims to investigate the nature and scale of household-based service demands, and to establish a baseline for better data collection and modeling of this under-studied component of travel demand in future research efforts. Part 1 of this project will utilize data from the recently completed Transportation Heartbeat of America

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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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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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Investigation of Emerging Sensing and AI/ML Technologies to Enhance the Safety of Vulnerable Roadway Users at Signalized Intersection

Accurately identifying and analyzing vulnerable roadway users (VRUs) such as pedestrians, bicyclists, and other non-vehicle occupants, are a crucial yet difficult undertaking. VRUs’ behavior is influenced by localized factors such as land use, and their movements are not confined to predefined paths. This study will investigate the use of emerging technologies such as LiDAR, network

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Blockchain Application on Smart Transportation Systems

Blockchain technology, predominantly utilized within cryptocurrency, is being increasingly adapted across diverse sectors, and transportation systems is not an exception. Despite presenting several challenges, blockchain technology also offers various advantages. Understanding Blockchain’s potential applications and benefits in addressing future urban challenges is an emerging field of research which has not been fully investigated. In fact,

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Travel Behavior Data (TBD) Hub

In an era characterized by transformative shifts in demographics, lifestyles, work patterns, technological advances, societal values, and climate and environmental conditions, decision-makers are now confronted with ever-increasing, multifaceted uncertainties. The TBD National Center has launched a flagship initiative, called the TBD Hub, to provide transportation decision-makers information and deep insights about the state of the

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Analysis of Changes in the Activity Prisms of Individuals to Predict a Shared Life Experience Metric Over Different Regions and Population Groups

Technology has changed individuals’ travel behavior and time-use in so many ways. As much as it offers a variety of benefits to societies, it may add to social exclusion phenomena, since the need for travel is being replaced by a click of a button in cell-phone. People don’t feel the need to leave their homes

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