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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231012T100000
DTEND;TZID=America/New_York:20231012T110000
DTSTAMP:20260406T071443
CREATED:20231003T162816Z
LAST-MODIFIED:20231010T183753Z
UID:80704-1697104800-1697108400@c2smart.engineering.nyu.edu
SUMMARY:Project Mjolnir : The Journey of Designing an Open Source Adaptive Mountain Bike
DESCRIPTION:Presented by NYU Shanghai’s Noel Joyce
URL:https://c2smart.engineering.nyu.edu/event/project-mjolnir-the-journey-of-designing-an-open-source-adaptive-mountain-bike/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231003T180000
DTEND;TZID=America/New_York:20231003T193000
DTSTAMP:20260406T071443
CREATED:20230918T142847Z
LAST-MODIFIED:20230918T142847Z
UID:80639-1696356000-1696361400@c2smart.engineering.nyu.edu
SUMMARY:Transportation Jeopardy! NYU ITE vs. Northeastern
DESCRIPTION:Think you know everything there is to know about traffic and transit? Put your knowledge to the test in this friendly competition between NYU’s chapter of ITE and Northeastern University’s chapter of ITE. Come prepared with bits of transportation trivia and be ready to buzz in when you know the answer!
URL:https://c2smart.engineering.nyu.edu/event/transportation-jeopardy-nyu-ite-vs-northeastern/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230928T120000
DTEND;TZID=America/New_York:20230928T130000
DTSTAMP:20260406T071443
CREATED:20230912T185155Z
LAST-MODIFIED:20230912T192021Z
UID:80564-1695902400-1695906000@c2smart.engineering.nyu.edu
SUMMARY:Developing a Framework to Optimize Floodnet Sensor Deployments Around NYC for Equitable and Impact-Based Hyper-Local Street-Level Flood Monitoring and Data Collection
DESCRIPTION:Urban floods can lead to significant damages\, and accurately measuring flood extents and depths is vital for effective management. Traditional flood measurement methods can be costly and may not provide detailed mapping. Recent advancements in affordable flood-monitoring sensors offer real-time measurements\, but there’s a challenge in prioritizing where to place them\, especially when considering equity and the needs of various stakeholders. This research introduced a framework that uses expert input to determine potential uses for these sensors and metrics to decide where to deploy them. The method was tested in New York City and was found to be more effective than basic prioritization methods\, ensuring stakeholder needs and equity concerns were met. This framework can help local governments prioritize sensor locations for better flood management\, promoting resilient and equitable cities. Future studies can adapt and test this approach in different urban settings. \nPresented by NYU’s Riccardo Negri and Prof. Luis Ceferino
URL:https://c2smart.engineering.nyu.edu/event/developing-a-framework-to-optimize-floodnet-sensor-deployments-around-nyc-for-equitable-and-impact-based-hyper-local-street-level-flood-monitoring-and-data-collection/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230922T113000
DTEND;TZID=America/New_York:20230922T123000
DTSTAMP:20260406T071444
CREATED:20230912T184858Z
LAST-MODIFIED:20230912T185245Z
UID:80560-1695382200-1695385800@c2smart.engineering.nyu.edu
SUMMARY:Reducing US Transit Costs: An Empirical Review and Comparative Case Study of Portland\, Manchester Rail Systems
DESCRIPTION:Presented by Chetan Sharma and Prof. Joseph Chow
URL:https://c2smart.engineering.nyu.edu/event/reducing-us-transit-costs-an-empirical-review-and-comparative-case-study-of-portland-manchester-rail-systems/
CATEGORIES:Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230922T093000
DTEND;TZID=America/New_York:20230922T110000
DTSTAMP:20260406T071444
CREATED:20230912T190235Z
LAST-MODIFIED:20230912T190235Z
UID:80573-1695375000-1695380400@c2smart.engineering.nyu.edu
SUMMARY:Reconnecting Communities: Creating More Equitable Outcomes in Transportation Projects
DESCRIPTION:Presented by the NYU Rudin Center for Transportation and AECOMThe Infrastructure Investment and Jobs Act (IIJA) represents an unprecedented investment in our country’s transportation system. As part of this investment\, the Biden Administration is seeking not only to build more roads and bridges\, but to address the harmful impacts of past transportation projects\, which have disproportionately fallen on low income communities and communities of color. For neighborhoods around the country that remain divided or isolated by transportation infrastructure\, this investment is a once-in-a-generation opportunity for reconnection. This panel discussion\, co-hosted by AECOM\, will address: How might we best reconnect communities? What does reconnection involve\, and how can we ensure future projects mitigate any further disruption and displacement? Join us for a discussion of ongoing developments and how agencies and stakeholders can best seize this opportunity to create more equitable outcomes. Panelists: Ritchie Torres\, United States Representative (NY-15) Meera Joshi\, New York City Deputy Mayor for Operations Tom Prendergast\, Executive Vice President & New York Metro Chief Executive\, AECOM Moderator: Sarah Kaufman\, Director\, NYU Rudin Center for Transportation.
URL:https://c2smart.engineering.nyu.edu/event/reconnecting-communities-creating-more-equitable-outcomes-in-transportation-projects/
LOCATION:NYU Wagner\, 295 Lafayette Street
CATEGORIES:Equity & Accessibility
ORGANIZER;CN="Rudin Center for Transportation Policy and Management":MAILTO:rudin.center@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230922T090000
DTEND;TZID=America/New_York:20230922T100000
DTSTAMP:20260406T071444
CREATED:20230825T160841Z
LAST-MODIFIED:20230825T161157Z
UID:80387-1695373200-1695376800@c2smart.engineering.nyu.edu
SUMMARY:Oliver Wyman Forum\, NYC Climate Week
DESCRIPTION:C2SMARTER Director Dr. Kaan Ozbay will be serving as a panelist at the Oliver Wyman Forum during NYC Climate Week. \nAbout the Oliver Wyman foundation: \nThe Oliver Wyman Forum is Oliver Wyman’s global think tank developing solutions for public and private leaders on social issues\, climate change\, and sustainability. Our mobility program has been working for the past ten months on developing a model-based solution to support cities comply with the 1.5oC global warming target by 2030. Our model allows us to project to what extent cities should promote modal shares\, mobility uses\, or powertrains taking into consideration a set of realistic constraints like city-individual energy sourcing\, socio-economic factors and infrastructure maturity. We have worked on an initial set of 12 cities globally (including New York) to project an individual\, quantified path towards sustainable mobility.
URL:https://c2smart.engineering.nyu.edu/event/oliver-wyman-forum-nyc-climate-week/
LOCATION:1166 Avenue of the Americas\, NYC\, NY
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230920T150000
DTEND;TZID=America/New_York:20230920T160000
DTSTAMP:20260406T071444
CREATED:20230915T170307Z
LAST-MODIFIED:20230915T183831Z
UID:80632-1695222000-1695225600@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Applications of data analytics in Smart Cities: Spatio-temporal crime prediction; and Epidemic forecasting based on mobility patterns
DESCRIPTION:The steadily increasing urbanization is causing significant economic and social transformations in urban areas\, posing several challenges and raising new issues in city development\, public policy\, and resource management. However\, leveraged by a pervasive and large-scale diffusion of sensing networks in modern cities\, huge volumes of geo-referenced urban data are collected every day. Such ever-increasing volumes of urban-related data offers the opportunity to apply data analytics methodologies to discover useful descriptive and predictive models\, which can support city managers in tackling the major issues that cities face\, including\, e.g.\, urban mobility\, air pollution\, virus diffusion\, traffic flows\, crime forecasts\, etc. \nThis talk introduces how data analysis and machine learning techniques can be exploited to design and develop data-driven models as valuable support to inspire and implement smart city applications and services. Then\, it presents two real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting\, based on multi-density clustering and auto-regressive models\, to automatically detect crime hotspots in urban areas and to reliably forecast crime trends in each hotspot. The experimental evaluation has been performed on Chicago crime data\, showing good accuracy in spatial and temporal crime forecasting over rolling time horizons. \nThe second one is an approach to discover predictive epidemic models from mobility and infection data. In particular\, the algorithm first discovers mobility hotspots and patterns. Then\, it detects how urban mobility affects the diffusion of epidemic hotspots\, by extracting a regression model for each hotspot. The experimental evaluation has been performed on mobility and COVID-19 data collected in the city of Chicago\, to assess the effectiveness of the approach in a real-world scenario. \nPresented by Eugenio Cesario\, Associate Professor\, University of Calabria \nEugenio Cesario is an Associate Professor of Computer Engineering at University of Calabria (Italy). His research interests fall in the broad areas of Data Analytics and Parallel/Distributed Data Mining\, and include Urban Computing\, Smart Cities\, Crime Data Mining\, Energy-aware Cloud Computing\, Cloud\Grid services architectures\, Knowledge Discovery applications. He is a member of the Scientific Board of the Ph.D. in ICT of the University of Calabria. He is also member of the Scalable Computing and Cloud Laboratory (DIMES-UNICAL) and co-founder of DtoK Lab s.r.l.\, a spin-off of University of Calabria.
URL:https://c2smart.engineering.nyu.edu/event/seminar-applications-of-data-analytics-in-smart-cities-spatio-temporal-crime-prediction-and-epidemic-forecasting-based-on-mobility-patterns/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230919T180000
DTEND;TZID=America/New_York:20230919T190000
DTSTAMP:20260406T071444
CREATED:20230915T184050Z
LAST-MODIFIED:20230918T160501Z
UID:80637-1695146400-1695150000@c2smart.engineering.nyu.edu
SUMMARY:Civil Engineering Clubs Welcome Party
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/asce-event/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230914T170000
DTEND;TZID=America/New_York:20230914T180000
DTSTAMP:20260406T071444
CREATED:20230830T201528Z
LAST-MODIFIED:20230830T201528Z
UID:80522-1694710800-1694714400@c2smart.engineering.nyu.edu
SUMMARY:Transportation & Urban Systems Fall 2023 Welcome Event
DESCRIPTION:C2SMARTER welcomes new and returning transportation students to the Fall 2023 academic year with a kick-off event. Learn about C2SMARTER\, meet the faculty\, and see the space. Come learn more about: \n\nMonthly research presentations taught by consortium members and industry partners\nPotential research opportunities\nTransportation courses offered this semester:\n\nForecasting Urban Travel Demand \nTraffic Operations & Control \nManagement of Transit Maintenance and Operations\nUrban Transportation & Logistics Systems\n\n\n\n 
URL:https://c2smart.engineering.nyu.edu/event/transportation-urban-systems-fall-2023-welcome-event/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230914T143000
DTEND;TZID=America/New_York:20230914T153000
DTSTAMP:20260406T071444
CREATED:20230912T185833Z
LAST-MODIFIED:20230912T185833Z
UID:80569-1694701800-1694705400@c2smart.engineering.nyu.edu
SUMMARY:ECE Seminar: Boundary Stabilization of Polynomial Reaction Diffusion Equations
DESCRIPTION:We show how to stabilize a polynomial reaction diffusion system using boundary control\, integration by parts\, completing the square and an infinite dimensional extension of Al’brekht’s method. \nPresented by Alfred J. Krener.
URL:https://c2smart.engineering.nyu.edu/event/ece-seminar-boundary-stabilization-of-polynomial-reaction-diffusion-equations/
LOCATION:370 Jay Street\, Room 1013\, 370 Jay Street\, Brooklyn
CATEGORIES:Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230907T120000
DTEND;TZID=America/New_York:20230907T130000
DTSTAMP:20260406T071444
CREATED:20230810T200745Z
LAST-MODIFIED:20250514T173423Z
UID:79990-1694088000-1694091600@c2smart.engineering.nyu.edu
SUMMARY:NY Statewide Behavioral Impact Decision Support Tool with Replica
DESCRIPTION:One of the enduring challenges in statewide transportation planning is that consistent population travel data remains scarce. This is changing with the availability of large-scale ICT data. A one-year project was initiated to develop a behavioral impact decision support tool based on NY statewide synthetic population data provided by Replica Inc. First\, a NY statewide model choice model is developed to deterministically fit heterogeneous coefficients for trips along each census block-group OD pair\, called group-level agent-based mixed (GLAM) logit. Considerations were made for four population segments\, six trip modes\, and twelve attributes. Second\, a decision support tool for statewide mobility service region design was proposed. The tool is based on an assortment optimization problem with agent-specific coefficients and linear constraints\, which can be efficiently solved through linear or quadratic programming (depending on variant). The decision support tool is applied to optimize service regions with one of the three objectives: (1) maximizing the total revenue; (2) maximizing the total change of consumer surplus; (3) minimizing the disparity between communities. \nPresented by Xiyuan Ren; PHD Candidate\, NYU; Joseph Chow\, Associate Professor\, NYU
URL:https://c2smart.engineering.nyu.edu/event/ny-statewide-behavioral-equity-impact-decision-support-tool-with-replica/
CATEGORIES:Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230901T130000
DTEND;TZID=America/New_York:20230901T140000
DTSTAMP:20260406T071444
CREATED:20230829T190602Z
LAST-MODIFIED:20230829T190602Z
UID:80497-1693573200-1693576800@c2smart.engineering.nyu.edu
SUMMARY:Highlights of Lane-Free Automated Vehicle Traffic with Nudging
DESCRIPTION:A novel paradigm (named TrafficFluid) for vehicular traffic in the era of connected and automated vehicles (CAVs) was recently proposed\, which is based on two combined principles. The first principle is lane-free traffic\, which renders the driving task for CAVs smoother and safer\, as risky lane-changing manoeuvres become obsolete; increases the capacity of the roadway due to increased road occupancy; and mitigates congestion-triggering vehicle\nmanoeuvres. Also\, lane-free CAV traffic implies that incremental road widening (narrowing) leads to corresponding incremental increase (decrease) of capacity\, and this opens the way for real-time internal boundary control on highways and arterials to flexibly share the total (both directions) road width and capacity among the two traffic directions in dependence of the bi-directional traffic conditions\, so as to maximize the total system efficiency. The second principle is vehicle nudging\, whereby vehicles may be influencing other vehicles in front of them; this allows for traffic flow to be freed from the anisotropy restriction\, which stems from the fact that human driving is influenced only by downstream vehicles. Nudging leads to improved traffic flow capacity and stability. \nAfter presenting the TrafficFluid motivation and general features\, some highlights of related work will be outlined\, such as: Nonlinear feedback control of CAV in lane-free traffic with nudging; Optimal path planning for individual vehicles and vehicle groups; Emerging macroscopic traffic flow modeling; Internal boundary control; Driving on large-scale lane-free roundabouts (Place Charles de Gaulle in Paris). See www.trafficfluid.tuc.gr
URL:https://c2smart.engineering.nyu.edu/event/highlights-of-lane-free-automated-vehicle-traffic-with-nudging/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230824
DTEND;VALUE=DATE:20230825
DTSTAMP:20260406T071444
CREATED:20230824T172801Z
LAST-MODIFIED:20230824T173031Z
UID:80354-1692835200-1692921599@c2smart.engineering.nyu.edu
SUMMARY:PhD New Student Orientation
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/phd-new-student-orientation/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230816T120000
DTEND;TZID=America/New_York:20230816T130000
DTSTAMP:20260406T071444
CREATED:20230810T200539Z
LAST-MODIFIED:20230810T200539Z
UID:79987-1692187200-1692190800@c2smart.engineering.nyu.edu
SUMMARY:C2SMARTER RFP Requirements Webinar
DESCRIPTION:The purpose of the webinar is to inform prospective PIs and research teams on the new requirements of C2SMARTER Center projects\, answer any questions\, and provide a forum for potential team-building collaboration. The topics we will cover include: \n\nFeedback on received project abstracts and potential teaming opportunities\nProposal submission requirements\nNew USDOT reporting requirements\nProject management and tracking systems\nTimelines and schedules\nQ&A
URL:https://c2smart.engineering.nyu.edu/event/c2smarter-rfp-requirements-webinar/
LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230815T120000
DTEND;TZID=America/New_York:20230815T133000
DTSTAMP:20260406T071444
CREATED:20230810T201206Z
LAST-MODIFIED:20230810T201206Z
UID:79995-1692100800-1692106200@c2smart.engineering.nyu.edu
SUMMARY:The Reconstruction of Penn Station
DESCRIPTION:Please join the NYU Rudin Center for Transportation for an illuminating presentation on the reconstruction of Penn Station\, led by Jamie Torres-Springer\, President\, MTA Construction & Development. Delve into the planned transformation of this essential hub. The presentation will be followed by a conversation between Jamie Torres-Springer and Sarah Kaufman.
URL:https://c2smart.engineering.nyu.edu/event/the-reconstruction-of-penn-station/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230814T120000
DTEND;TZID=America/New_York:20230814T130000
DTSTAMP:20260406T071444
CREATED:20230810T200203Z
LAST-MODIFIED:20230810T200252Z
UID:79982-1692014400-1692018000@c2smart.engineering.nyu.edu
SUMMARY:Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Phase 2
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/digital-twin-technologies-towards-understanding-the-interactions-between-transportation-and-other-civil-infrastructure-systems-phase-2/
CATEGORIES:Big Data & Planning for Smart Cities,Webinars
LOCATION:https://nyu.zoom.us/webinar/register/WN_jQg9UllxRB26w9QmMn9OqQ
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230803T140000
DTEND;TZID=America/New_York:20230803T150000
DTSTAMP:20260406T071444
CREATED:20230627T160953Z
LAST-MODIFIED:20230627T160953Z
UID:79455-1691071200-1691074800@c2smart.engineering.nyu.edu
SUMMARY:Summer Webinar Series: A Variational Autoencoder Approach for Route Choice Set Generation
DESCRIPTION:In the context of route choice modeling\, choice set generation is a challenging task\, since the consideration set is generally unknown to the modelers\, and the full choice set cannot be enumerated in real size networks. The proposed variational autoencoder approach (VAE) is motivated by the idea that the chosen alternatives must belong to the consideration set. The VAE approach explicitly considers maximizing the likelihood of including the chosen alternatives in the choice set\, and infers the underlying generation process. The VAE approach for route choice set generation is exemplified using a real dataset. VAE-CNL model has the best performance in terms of goodness-of-fit and prediction performance\, compared to models estimated with conventionally generated choice sets. \nShlomo Bekhor is a professor in the Faculty of Civil and Environmental Engineering at the Technion – Israel Institute of Technology\, and serving as Faculty Dean since 2019. He has a BSc in Aeronautical Engineering from ITA\, Sao Jose dos Campos\, Brazil. MSc and PhD degrees in Transportation Engineering were obtained at the Technion. He teaches and conducts research in transportation planning and network equilibrium models and has special interest in route choice modeling. \nPresented by the Transportation Research Board Subcommittee AEP30(2)/AEP40 on Route Choice and Spatio-Temporal Behavior
URL:https://c2smart.engineering.nyu.edu/event/summer-webinar-series-a-variational-autoencoder-approach-for-route-choice-set-generation/
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230725T120000
DTEND;TZID=America/New_York:20230725T130000
DTSTAMP:20260406T071444
CREATED:20230707T185854Z
LAST-MODIFIED:20230817T213550Z
UID:79467-1690286400-1690290000@c2smart.engineering.nyu.edu
SUMMARY:Webinar: Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems: Urban Work Zone Detection and Safety Analysis Using Near Miss Data
DESCRIPTION:Jingqin Gao: Assistant Director of Research\, C2SMART\, NYU; Chuan Xu: PHD Candidate\, NYU\nTuesday\, July 25\, 2023 | 12:00pm ET | Virtual\nWhile computer vision technology is widely acknowledged\, it remains underutilized for routine operations and traffic safety in complex urban environments. Our research develops a deep learning-based\, automatic data acquisition and analytics approach utilizing vision-based sensors. This approach harnesses data from existing transportation infrastructure and connected and automated vehicles (CAVs)\, illustrating how computer vision can create fresh streams of mobility and safety information for strategic planning and operations. It presents two applications: WorkZoneX\, leveraging 900+ traffic cameras in NYC for real-time urban work zone identification and size estimation\, and SAFExMAP\, a risk indicator scoring system using near-miss data from in-vehicle cameras. Findings suggest significant potential for computer vision in smart cities\, offering cost-effective solutions to improve planning\, operations and traffic safety analysis.
URL:https://c2smart.engineering.nyu.edu/event/webinar-exploring-cost-effective-computer-vision-solutions-for-smart-transportation-systems-urban-work-zone-detection-and-safety-analysis-using-near-miss-data/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230717T180000
DTEND;TZID=America/New_York:20230717T193000
DTSTAMP:20260406T071444
CREATED:20230620T170709Z
LAST-MODIFIED:20230620T170709Z
UID:79435-1689616800-1689622200@c2smart.engineering.nyu.edu
SUMMARY:Transit Techies July Meetup
DESCRIPTION:Technologists who love transit.\nTransit enthusiasts who hack!\nAt Transit Techies NYC\, speakers present transit-related projects. Presenters may be household hackers\, data scientists\, researchers\, product developers\, or you! All presentations will be technical and awesome.
URL:https://c2smart.engineering.nyu.edu/event/transit-techies-july-meetup/
CATEGORIES:Connected & Autonomous Mobility
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230629T120000
DTEND;TZID=America/New_York:20230629T130000
DTSTAMP:20260406T071444
CREATED:20230620T165219Z
LAST-MODIFIED:20230622T153755Z
UID:79431-1688040000-1688043600@c2smart.engineering.nyu.edu
SUMMARY:Webinar: Autonomous Vehicle Good Citizenry Standard
DESCRIPTION:Presented by:\nSarah Kaufman\, Interim Director\, NYU Rudin Center for Transportation\nJoseph Chow\, Associate Professor\, NYU\nThursday\, June 29\, 2023: 12:00pm – 1:00pm ET | Virtual \nNew York City is moving toward a more efficient\, safer\, and sustainable future that includes autonomous vehicles for transit\, e-commerce\, and medical transport. However\, autonomy often runs on incomplete or flawed foundations: training data sets might not prepare vehicles to “see” people of color; transit shuttles may operate without safety considerations for women\, frequent targets of sexual harassment on transit; delivery pods might be sharing personal data with several third parties. Although the City will regulate vehicle safety and efficacy on the street\, autonomous mobility must be evaluated under more ambitious and holistic standards. The Responsible Autonomous Mobility (RAM) Framework aims to identify partnership in several areas.
URL:https://c2smart.engineering.nyu.edu/event/webinar-autonomous-vehicle-good-citizenry-standard/
CATEGORIES:Connected & Autonomous Mobility
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230615T150000
DTEND;TZID=America/New_York:20230615T160000
DTSTAMP:20260406T071444
CREATED:20230531T163314Z
LAST-MODIFIED:20230531T163536Z
UID:79276-1686841200-1686844800@c2smart.engineering.nyu.edu
SUMMARY:Summer Webinar Series
DESCRIPTION:Presented by the Transportation Research Board (TRB) subcommittee AEP30(2) Route Choice and Spatio-Temporal Behavior \nSpeaker: Professor Marcela A. Munizaga\, Universidad de Chile.
URL:https://c2smart.engineering.nyu.edu/event/summer-webinar-series/
CATEGORIES:Big Data & Planning for Smart Cities
LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230613T120000
DTEND;TZID=America/New_York:20230613T130000
DTSTAMP:20260406T071444
CREATED:20230602T162031Z
LAST-MODIFIED:20230602T162031Z
UID:79286-1686657600-1686661200@c2smart.engineering.nyu.edu
SUMMARY:Webinar: Quantifying and Visualizing City Truck Route Network Efficiency Using a Virtual Testbed
DESCRIPTION:Presented by: Joseph Chow\, Associate Professor\, NYU\nHaggai Davis\, PHD Candidate\, NYU\nTuesday\, June 13\, 2023: 12:00pm – 1:00pm ET | Virtual
URL:https://c2smart.engineering.nyu.edu/event/webinar-quantifying-and-visualizing-city-truck-route-network-efficiency-using-a-virtual-testbed/
CATEGORIES:Big Data & Planning for Smart Cities
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
LOCATION:
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230524T120000
DTEND;TZID=America/New_York:20230524T130000
DTSTAMP:20260406T071444
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
LOCATION:
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230522T153000
DTEND;TZID=America/New_York:20230522T164500
DTSTAMP:20260406T071444
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:20230519T140000
DTEND;TZID=America/New_York:20230519T150000
DTSTAMP:20260406T071444
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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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230516T120000
DTEND;TZID=America/New_York:20230516T130000
DTSTAMP:20260406T071444
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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BEGIN:VEVENT
DTSTART;VALUE=DATE:20230515
DTEND;VALUE=DATE:20230519
DTSTAMP:20260406T071444
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
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230505T120000
DTEND;TZID=America/New_York:20230505T130000
DTSTAMP:20260406T071444
CREATED:20230414T120001Z
LAST-MODIFIED:20230417T195811Z
UID:78989-1683288000-1683291600@c2smart.engineering.nyu.edu
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230428T110000
DTEND;TZID=America/New_York:20230428T120000
DTSTAMP:20260406T071444
CREATED:20230307T165118Z
LAST-MODIFIED:20230307T165154Z
UID:78904-1682679600-1682683200@c2smart.engineering.nyu.edu
SUMMARY:Python for GIS: An Introduction to OSMnx
DESCRIPTION:Instructor: Shajnush Amir\, North South University & University of Twente\nHands-on exercise: Yes\nBeginner level: No prior experience required.\nSchedule: Friday April 28\, 11:00am-12:00pm\nDescription: In this course\, you will learn how to harness the power of OSMnx\, a Python library for extracting and visualizing Open Street Maps data. Through hands-on exercises\, you will gain practical experience in using OSMnx to model and simulate projects and have a solid understanding of this Python library and be able to apply OSMnx to real-world problems.
URL:https://c2smart.engineering.nyu.edu/event/python-for-gis-an-introduction-to-osmnx/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Student Events
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230414T100000
DTEND;TZID=America/New_York:20230414T110000
DTSTAMP:20260406T071444
CREATED:20230307T164243Z
LAST-MODIFIED:20230307T164658Z
UID:78896-1681466400-1681470000@c2smart.engineering.nyu.edu
SUMMARY:Stairway Towards Systematic Review: Utilizing Rayyan Software & PRISMA Guidelines
DESCRIPTION:Instructor: Samiha Tasnim\, North South University\nHands-on exercise: Yes\nBeginner level: No prior experience required.\nSchedule: Friday April 14\, 10:00am-11:00am\nDescription: Rayyan is a free web tool that aims to help researchers work on systematic reviews by accelerating the process of screening and selecting articles. Besides\, the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) checklist is an extensively used tool for reporting systematic reviews\, which strives to create transparent\, credible\, and reliable research results. This course intends to cover the fundamentals of using Rayyan and PRISMA\, why\, and how to utilize them to kickstart your pathway toward research.
URL:https://c2smart.engineering.nyu.edu/event/stairway-towards-systematic-review-utilizing-rayyan-software-prisma-guidelines/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Student Events
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
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