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DTSTART;TZID=America/New_York:20230516T120000
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SUMMARY:Learning from big and small data for transportation planning and resilience analysis
DESCRIPTION:POSTPONED – TO BE RESCHEDULED \nCOVID has exacerbated two emerging trends in transportation analysis: (1) the rise of passively-generated big data; and (2) the increasing need to deal with the “unexpected” disruptions. This talk emphasizes the need for learning big and small data for transportation planning and resilience analysis. Different ways of learning are described\, with applications ranging from long-term planning analysis to rapid responses under disruptions. \nPRESENTER  \nCynthia Chen is a professor in the Department of Civil & Environmental Engineering at the University of Washington (Seattle). She is also a professor and the interim chair of the Department of Industrial & Systems Engineering at UW. She is an internationally renowned scholar in transportation science and directs the THINK (Transportation-Human Interaction and Network Knowledge) lab at the UW. Cynthia has published over 60 peer-reviewed publications in leading journals in transportation and systems engineering including Transportation Research Part A-F and Omega\, as well as interdisciplinary journals such as PNAS. Her research has been supported by federal agencies such as NSF\, NIH\, APAR-E\, NIST\, USDOT\, and FHWA as well as state and regional agencies. Cynthia served a two-year assignment (2017-19) as the Program Director of Civil Infrastructure Systems\, CMMI (Civil\, Mechanical\, and Manufacturing Innovation) division with the National Science Foundation. She is an associate director of TOMNET (Center for Teaching Old Models New Tricks)\, a USDOT-funded Tier 1 University Transportation Center led by ASU\, as well as a key member of the new Center of Understanding Future Travel Behavior and Demand\, a USDOT-funded national center led by UT Austin. Currently\, Cynthia serves as an associate editor for Transportation Science\, and is on the editorial board of Sustainability Analytics and Modeling.
URL:https://c2smart.engineering.nyu.edu/event/learning-from-big-and-small-data-for-transportation-planning-and-resilience-analysis/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Big Data & Planning for Smart Cities
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DTSTART;TZID=America/New_York:20230519T140000
DTEND;TZID=America/New_York:20230519T150000
DTSTAMP:20260411T102048
CREATED:20230503T170846Z
LAST-MODIFIED:20230504T194131Z
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SUMMARY:Deep Neural Networks for Choice Analysis
DESCRIPTION:Individual choice has been an enduring question across disciplines. Deep neural networks (DNNs) have demonstrated their high predictive power over the classical discrete choice models (DCMs) in many empirical studies. However\, DNNs as a new modeling paradigm still present pressing challenges in interpretation\, generalization\, and robustness. This presentation introduces a deep choice framework that synergizes DNNs and DCMs to model individual travel decision. It demonstrates that the DNNs can provide economic information as complete as classical DCMs\, including choice predictions\, choice probabilities\, market shares\, substitution patterns of alternatives\, social welfare\, heterogeneous values of time\, among many others\, thus partially resolving the interpretation challenge. It introduces how to use the prior behavioral knowledge to design a particular DNN architecture with alternative-specific utility functions\, which improves the generalizability of DNNs with a domain-knowledge-based regularization method. It then extends the framework to deep hybrid models\, which integrates classical numerical data and the unstructured data (i.e.\, imagery and graphs) to analyze travel behavior. Overall\, this presentation lays out a new foundation of using DNNs to analyze travel demand\, enhancing economic interpretation\, architectural design\, and robustness of deep learning through classical utility theory. \n\n\nSPEAKER \nShenhao Wang is an assistant professor and the director of the Urban Artificial Intelligence Laboratory at the University of Florida. He is also a research affiliate to Urban Mobility Lab and Media Lab at the Massachusetts Institute of Technology. He seeks to develop fundamental theory for urban science using artificial intelligence. He develops deep choice models\, which analyze individual decision-making by integrating discrete choice models and deep learning with applications to urban travel behavioral analysis. He also analyzes collective mobility networks by integrating classical network theory and graph neural networks to quantify risk and uncertainty\, thus promoting resilient economic growth. Dr. Wang completed his interdisciplinary Ph.D. in Computer and Urban Science at Massachusetts Institute of Technology in 2020. He received B.A. in Economics from Peking University (2014) and B.A. in architecture and law from Tsinghua University (2011)\, Master of Science in Transportation\, and Master of City Planning from MIT (2017).
URL:https://c2smart.engineering.nyu.edu/event/deep-neural-networks-for-choice-analysis/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Big Data & Planning for Smart Cities,Student Events
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DTSTART;TZID=America/New_York:20230524T120000
DTEND;TZID=America/New_York:20230524T130000
DTSTAMP:20260411T102048
CREATED:20230425T133642Z
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SUMMARY:Summer Webinar Series- Route Choice and Spatio-Temporal Behavior: The Perturbed Utility Route Choice Model
DESCRIPTION:  \nThe perturbed utility route choice model represents traveler behavior as a utility maximizing assignment of flow across an entire network under a flow conservation constraint. Substitution between routes depends on how much they overlap. The model is estimated considering the full set of route alternatives\, and no choice set generation is required. Nevertheless\, estimation requires only linear regression and is very fast. Predictions from the model can be computed using convex optimization\, and computation is straightforward even for large networks. In this talk\, Professor Fosgerau presents results from application to large datasets (1\,337\,096 GPS traces of car trips\, 280\,000 GPS traces of bicycle trips) in Copenhagen. \nSPEAKER \nMogens Fosgerau is a professor in the Economics Department\, University of Copenhagen. His areas of research include micro-economics and micro-econometrics applied to problems in transportation\, in particular to issues concerning time\, reliability and congestion. \nPresented by the Transportation Research Board Subcommittee on Route Choice and Spatio-Temporal Behavior (AEP30/AEP40)
URL:https://c2smart.engineering.nyu.edu/event/route-choice-and-spatio-temporal-behavior-the-perturbed-utility-route-choice-model/
CATEGORIES:Big Data & Planning for Smart Cities
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
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