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DTSTART;TZID=America/New_York:20230505T120000
DTEND;TZID=America/New_York:20230505T130000
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SUMMARY:Old Models with New Tricks: Bridging the Gap in Bureau of Public Roads (BPR) Functions with A Cross-Resolution Perspective of Theoretical Fundamentals and Emerging Applications
DESCRIPTION:  \nIn transportation planning\, volume-delay functions (VDFs) are essential functions used for traffic assignment and network design problems. However\, the static Bureau of Public Roads (BPR) function\, created by the US Bureau of Public Roads in 1964\, can only provide average travel time measures and cannot capture traffic dynamics at an oversaturated bottleneck. This talk will systematically review VDF-related research\, including modeling efforts that connect traffic flow’s fundamental diagrams (FDs) to queueing models and link delay/performance functions under both undersaturated and oversaturated conditions. We will discuss a cross-resolution modeling approach for understanding the dynamic relationship between demand and supply and resulting congestion. We will also describe oversaturated system dynamics using parsimonious macroscopic analytical formulations with consistent mesoscopic queue vehicular fluid models. By providing a unified integration of multi-scale models\, city planners can have a valid analytical framework to analyze queue saturation evolution processes. The effectiveness of this approach will be demonstrated through case studies using empirical data from heavily congested corridors in metropolitan areas\, including New York\, Los Angeles\, and Phoenix. This talk will showcase how old models can be revitalized with new tricks to address the challenges of transportation planning and feedback-based control in the modern era. \nPRESENTER \nXuesong (Simon) Zhou is an Associate Professor of Transportation Systems at the School of Sustainable Engineering and the Built Environment\, Arizona State University (ASU)\, Tempe\, Arizona. Dr. Zhou’s research focuses on developing methodological advancements in multimodal transportation planning applications\, including dynamic traffic assignment\, traffic estimation and prediction\, large-scale routing\, and rail scheduling. Dr. Zhou serves as an Associate Editor of Transportation Research Part C\, an Executive Editor-in-Chief of Urban Rail Transit\, and an Editorial Board Member of Transportation Research Part B. He was the former Chair of INFORMS Rail Application Section (2016) and currently serves as a subcommittee chair of the TRB Committee on Transportation Network Modeling (AEP40). \nDr. Zhou is the Director of the ASU Transportation+AI Lab\, where he is the principal architect and programmer for several open-source packages\, including DTALite\, NEXTA\, and OSM2GMNS\, which have collectively received over 100\,000 downloads and many system deployments at various metropolitan planning agencies and state DOTs. He has published over 100 papers in Transportation Research Part B\, Transportation Research Part C\, and other leading transportation journals\, with an H-index of 54 and a total of 9\,000 citations in Google Scholar. \nIn addition to his academic achievements\, Dr. Zhou is passionate about connecting practitioners\, researchers\, academics\, students\, and others involved in transportation planning and travel modeling. He serves as the conference chair for the TRB Innovations in Travel Analysis and Planning Conference in 2023\, and a board member of Zephyr Foundation\, a non-profit organization dedicated to advancing transportation research and education.
URL:https://c2smart.engineering.nyu.edu/event/old-models-with-new-tricks-bridging-the-gap-in-bureau-of-public-roads-bpr-functions-with-a-cross-resolution-perspective-of-theoretical-fundamentals-and-emerging-applications/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
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DTSTART;VALUE=DATE:20230515
DTEND;VALUE=DATE:20230519
DTSTAMP:20260502T050457
CREATED:20230510T203146Z
LAST-MODIFIED:20230510T203146Z
UID:79170-1684108800-1684454399@c2smart.engineering.nyu.edu
SUMMARY:KAIST-NYU: KN-C³ Workshop
DESCRIPTION:Members of faculty from KAIST (Korea Advanced Institute of Science & Technology) will visit NYU for the first KN-C³ workshop at NYU Tandon. This four-day workshop consists of research exchanges between the two schools focusing on transportation and urban research. The goal is to connect researchers and find common research interests for future collaboration. Academic exchange programs including dual and/or joint degree will also be discussed. Learn more.
URL:https://c2smart.engineering.nyu.edu/event/kaist-nyu-kn-c%c2%b3-workshop/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Conferences
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
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DTSTART;TZID=America/New_York:20230516T120000
DTEND;TZID=America/New_York:20230516T130000
DTSTAMP:20260502T050457
CREATED:20230405T182650Z
LAST-MODIFIED:20230515T215414Z
UID:78984-1684238400-1684242000@c2smart.engineering.nyu.edu
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:20260502T050457
CREATED:20230503T170846Z
LAST-MODIFIED:20230504T194131Z
UID:79072-1684504800-1684508400@c2smart.engineering.nyu.edu
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:20230522T153000
DTEND;TZID=America/New_York:20230522T164500
DTSTAMP:20260502T050457
CREATED:20230503T171010Z
LAST-MODIFIED:20230504T154007Z
UID:79082-1684769400-1684773900@c2smart.engineering.nyu.edu
SUMMARY:USDOT Free Public Webinar:  Intersection Safety Challenge Prize Competition
DESCRIPTION:The U.S. Department of Transportation (DOT) will host a webinar on May 22 to discuss the Intersection Safety Challenge Prize Competition\, which launched on April 25\, 2023. \nEach year\, roughly one-quarter of traffic fatalities and about one-half of all traffic injuries in the United States are attributed to intersections. According to the latest data from the National Highway Traffic Safety Administration (NHTSA)\, an estimated 42\,939 people died in motor vehicle traffic crashes in 2021\, a 10.1% increase compared to 39\,007 fatalities reported in 2020. From 2020 to 2021\, pedestrian and pedalcyclist fatalities and injuries increased at an alarming rate. For example\, pedestrian fatalities increased 13% and pedestrian injuries increased 11% from 2020 to 2021. In response to growing concerns regarding the safety of vulnerable road users at intersections and as part of the recent National Roadway Safety Strategy (NRSS) Call to Action\, the DOT aims to transform intersection safety through the innovative application of emerging technologies to identify and mitigate unsafe conditions involving vehicles and vulnerable road users. \nTo help address this growing problem and support U.S. DOT’s vision\, U.S. DOT is launching the Intersection Safety Challenge. This Challenge includes a multi-stage Prize Competition to encourage teams of innovators and end-users to develop and test their intersection safety systems (ISS) to compete for up to $6 million total in prizes. \nThe Challenge is considering the potential of emerging technologies to transform intersection safety and ensure equity among all road users (including vehicles and vulnerable road users). Leveraging emerging technologies to anticipate\, prevent\, and mitigate unsafe roadway conditions could augment traditional safety engineering in roadway design and intersection control. These emerging technologies could include machine vision\, machine perception\, sensor fusion\, real-time decision-making\, artificial intelligence\, and vehicle-to-everything (V2X) communications (among other approaches). These technologies in most cases rely on real-time decision-making informed by data ingested and analyzed from multiple sensor systems. \nThe webinar will discuss the U.S. DOT Intersection Safety Challenge Prize Competition\, including a program overview\, the Prize Competition structure\, and Stage 1A expectations. Please register for this webinar by visiting the following registration link: https://iscwebinar.eventbrite.com. \nFor more information about the program\, please visit the program website: https://its.dot.gov/isc. For more information about the ITS JPO\, please visit: https://www.its.dot.gov/.
URL:https://c2smart.engineering.nyu.edu/event/usdot-free-public-webinar-intersection-safety-challenge-prize-competition/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Safety in Transportation Systems,Virtual Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230524T120000
DTEND;TZID=America/New_York:20230524T130000
DTSTAMP:20260502T050457
CREATED:20230425T133642Z
LAST-MODIFIED:20230504T153822Z
UID:79046-1684929600-1684933200@c2smart.engineering.nyu.edu
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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