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DTSTART;TZID=America/New_York:20230814T120000
DTEND;TZID=America/New_York:20230814T130000
DTSTAMP:20260429T070814
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:20230615T150000
DTEND;TZID=America/New_York:20230615T160000
DTSTAMP:20260429T070815
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:20260429T070815
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:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230524T120000
DTEND;TZID=America/New_York:20230524T130000
DTSTAMP:20260429T070815
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:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230519T140000
DTEND;TZID=America/New_York:20230519T150000
DTSTAMP:20260429T070815
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230516T120000
DTEND;TZID=America/New_York:20230516T130000
DTSTAMP:20260429T070815
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230406T120000
DTEND;TZID=America/New_York:20230406T130000
DTSTAMP:20260429T070815
CREATED:20230321T133103Z
LAST-MODIFIED:20230321T133103Z
UID:78946-1680782400-1680786000@c2smart.engineering.nyu.edu
SUMMARY:Optimal dispatching of electric vehicles for providing charging on- demand service leveraging charging-on-the-move technology
DESCRIPTION:  \n  \nRange anxiety and charging infrastructure scarcity have been the main challenges for the mass adoption of electric vehicles (EVs). The emerging mobile electric-vehicle-to-electric-vehicle (mE2) charging technology offers a promising solution\, which combines battery-to-battery and connected and autonomous vehicle technologies to enable an EV with an extra battery to charge another EV on the move. This webinar focuses on the efficient pairing and routing of electricity providers (EPs) to demand (EDs) by extending the existing Charging-as-a-Service (CaaS) strategy to the mE2 charging service (referred to as CaaS + ). Dr. Lili Du will discuss the EP fleet management problem\, which is mathematically modeled as a vehicle routing problem (i.e.\, mE2-VRP)\, aiming to optimally dispatch the minimum number of EPs to approach and serve the EDs using different proportions of EV flows to save EDs’ travel time and mitigate traffic congestion to different extents in different network congestion and charging station coverage scenarios. She will also discuss suggestions for improving the service efficiency of CaaS + . \nPRESENTER  \nDr. Lili Du is an associate professor in the Civil and Coastal Engineering Department\, University of Florida. Before that\, she worked as an assistant and then an associate professor at the Illinois Institute of Technology (IIT) from 2012 to 2017\, and as a Post-doctoral Research Associate for NEXTRANS at Purdue University from 2008 to 2012. Dr. Du received her Ph.D. degree in Decision Sciences and Engineering Systems with a minor in Operations Research and Statistics from Rensselaer Polytechnic Institute in 2008. Dr. Du’s research is characterized by integrating operations research\, network modeling\, game theory\, control theory\, machine learning\, and statistical methods into traffic flow analysis\, transportation system analysis\, and network modeling. Her current research mainly focuses on the impacts of connected and/or autonomous vehicles and electric vehicles\, mobility on demand\, smart curb\, network resilience\, and traffic flow analysis. Dr. Du’s research has been published in Transportation Research Part B\, Part C\, and Part D\, IEEE Transactions on ITS\, Networks and Spatial Economics. Her research has been funded by National Science Foundation (NSF)\, State DOT\, STRIDE UTC\, and Toyota InfoTechnology Center. Dr. Du is a recipient of the NSF CAREER award in 2016. Her recent project\, “Driverless City” won the First Nayar Prize at IIT. She is the founding chair of both TRB AEP40-4 subcommittee on Emerging Technologies in Network Modeling and ASCE-T&DI Artificial Intelligence in Transportation Committee. She serves as an editor for Transportation Research Part B: Methodological\, an associate editor for IEEE Transactions on Intelligent Transportation Systems\, and a member of the editorial advisory board for Transportation Research Part C: Emerging Technologies.
URL:https://c2smart.engineering.nyu.edu/event/optimal-dispatching-of-electric-vehicles-for-providing-charging-on-demand-service-leveraging-charging-on-the-move-technology/
CATEGORIES:Big Data & Planning for Smart Cities
LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20221015
DTEND;VALUE=DATE:20221024
DTSTAMP:20260429T070815
CREATED:20220910T021710Z
LAST-MODIFIED:20220928T193427Z
UID:77455-1665792000-1666569599@c2smart.engineering.nyu.edu
SUMMARY:NYCDOT Smart Routing Hackathon
DESCRIPTION:Currently in NYC\, truckers and trucking companies are reliant on a static map to know where they should and should not go. When events like construction or a road accident require rerouting\, truck drivers are at a loss because existing navigation tools do not include truck route priorities\, elevation restrictions\, or turn restrictions\, truck tolls\, or other details specific to their needs. From Saturday\, October 15 through Sunday\, October 23\, C2SMART\, in partnership with NYCDOT\, will host a Hackathon to invite students to design a 3D visualizer which helps drivers understand where they are in their route\, and helps them navigate the complexities of the city. \nThe event is open to all graduate and undergraduate students in New York City. Folks in New York City are welcome to attend Opening Ceremonies in person through our partnership with Transportation Camp; all participants are welcome to participate virtually during the work period. Final submissions will be presented at 3:00pm on October 23\, followed by an hour of networking opportunities for students\, industry professionals\, and agencies. \nPrizes will be awarded to the top three submissions\, and the winner’s design may be adopted by NYCDOT — allowing the winner to directly impact transportation in New York City. \nRegister for TransportationCamp here. \nRegister for the closing ceremony Zoom here.
URL:https://c2smart.engineering.nyu.edu/event/nycdot-smart-routing-hackathon/
LOCATION:CUNY School of Law\, 2 Ct Square W\, Queens\, NY\, 11101\, United States
CATEGORIES:Big Data & Planning for Smart Cities,Student Events
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220708T150000
DTEND;TZID=America/New_York:20220708T160000
DTSTAMP:20260429T070815
CREATED:20220627T183540Z
LAST-MODIFIED:20220708T162114Z
UID:77366-1657292400-1657296000@c2smart.engineering.nyu.edu
SUMMARY:Individual Path Recommendation Under Public Transit Service Disruptions Considering Behavior Uncertainty and Equity
DESCRIPTION:During a public transit service disruption\, passengers usually need path recommendations to find alternative routes. In this webinar\, MIT PhD Candidate Baichuan Mo will discuss his proposal for a mixed-integer programming (MIP) formulation to model the individual-based path (IPR) recommendation problem during PT service disruptions with the objective of minimizing system travel time and respecting passengers’ path choice preferences. Passengers’ behavior uncertainty in path choices given recommendations and their travel time equity are also considered. He models the behavior uncertainty based on passenger’s prior preferences and posterior path choice probability distribution with two new concepts: epsilon-feasibility and gamma-concentration\, which control the mean and variance of path flows in the optimization problem. The IPR problem with behavior uncertainty is solved efficiently with Benders decomposition. A post-adjustment heuristic is used to address the equity requirement. The proposed approach is implemented in the Chicago Transit Authority (CTA) system with a real-world urban rail disruption as the case study. Results show that the proposed IPR model significantly reduces the average travel times compared to the status quo and outperforms the capacity-based benchmark path recommendation strategy. \n \nBaichuan Mo is a Ph.D. student in the transportation program at MIT. He completed his dual Master’s degree in Transportation and Computer Science at MIT in 2020. Prior to joining MIT\, he got a B.E. degree from the Department of Civil Engineering\, Tsinghua University\, awarded with the Tsinghua Presidential Scholarship. \nBaichuan’s main research interest is data-driven transportation modeling\, demand modeling\, and machine learning. His master thesis was on the network performance model for urban rail system monitoring. His current research focuses on unplanned incident analysis and management in urban rail systems\, sponsored by Chicago Transit Authority (CTA).
URL:https://c2smart.engineering.nyu.edu/event/individual-path-recommendation-under-public-transit-service-disruptions-considering-behavior-uncertainty-and-equity/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Big Data & Planning for Smart Cities
ATTACH;FMTTYPE=image/png:https://c2smart.engineering.nyu.edu/wp-content/uploads/2022/06/C2SMART-Seminar-Individual-Path-Recommendation-Under-Public-Transit-Service-Disruptions-Considering-Behavior-Uncertainty-and-Equity.png
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220401T130000
DTEND;TZID=America/New_York:20220401T140000
DTSTAMP:20260429T070815
CREATED:20220217T150249Z
LAST-MODIFIED:20220308T175338Z
UID:73307-1648818000-1648821600@c2smart.engineering.nyu.edu
SUMMARY:Ramp metering: Control Strategies and New Insights
DESCRIPTION:Instructor: Yu Tang\, New York UniversityHands-on exercise: YesLevel: No prior experience required.Schedule: April 1\, 2022 | 1:00 PM – 2:00 PM ET \nDynamic flow networks are a class of useful models for a variety of engineering systems including transportation systems\, production lines and communication networks. This session will introduce its basic concepts\, mathematical modeling and control strategies. The application will be illustrated with ramp metering\, a typical strategy for freeway management.
URL:https://c2smart.engineering.nyu.edu/event/ramp-metering-control-strategies-and-new-insights/
CATEGORIES:Big Data & Planning for Smart Cities,Virtual Events
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
LOCATION:https://nyu.zoom.us/meeting/register/tJYude-qqj4iG9Wfm-P7NbmXGCfW4qkORUdd
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220215T123000
DTEND;TZID=America/New_York:20220215T133000
DTSTAMP:20260429T070815
CREATED:20220113T172220Z
LAST-MODIFIED:20220204T153422Z
UID:71559-1644928200-1644931800@c2smart.engineering.nyu.edu
SUMMARY:Context Driven Analytics and AI for Infrastructure and Facility Management
DESCRIPTION:Engineers and managers involved in facility/infrastructure operations need situational awareness and accurate assessment of as-is conditions when making daily decisions and developing short- and long-term plans. Currently\, however\, the situational awareness of engineers is often limited by a lack of actionable information relevant to the specific facilities and infrastructure systems in their purview. Advances in sensing and reality capture technologies\, such as 3D imaging via stationary platforms or drones and in-situ sensing\, streamline capturing of data depicting as-is conditions. Data collected from these technologies\, integrated with building information models\, enable context-driven analyses of as-is conditions\, generation of actionable information related to specific facilities/infrastructure systems\, and development of algorithms that help support proactive and predictive operations. Professor Burcu Akinci will provide an overview of the opportunities and research approaches associated with integration of sensor data with building/infrastructure information models and with development of context-driven algorithms. She’ll demonstrate applications of these approaches through specific deployments in several facilities and other infrastructure systems\, and highlight specific research projects being conducted at Carnegie Mellon University with a vision towards self-aware autonomous facilities and infrastructure systems.\n  \nDr. Burcu Akinci is Paul Christiano Professor of Civil & Environmental Engineering at Carnegie Mellon University and a member of the National Academies of Construction. She earned an MBA from Bilkent University (Ankara\, Turkey)\, and master’s and PhD degrees in civil and environmental engineering from Stanford University. Dr. Akinci’s research focuses on investigating utilization and integration of building information models with data capture technologies to create digital twins of construction projects and infrastructure operations and develop approaches to support proactive and predictive operations and management. Recipient of myriad awards\, including 4 best paper awards from top journals and PI of more than $6M grants\, she co-founded and is Chief Innovation Officer at LeanFM Technologies\, recipient of the 2017 Pittsburgh Business Times Innovation Award.
URL:https://c2smart.engineering.nyu.edu/event/improving-contraflow-left-turn-lane-design-at-signalized-intersections-to-decrease-traffic/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Big Data & Planning for Smart Cities
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211130T110000
DTEND;TZID=America/New_York:20211130T170000
DTSTAMP:20260429T070815
CREATED:20211129T175012Z
LAST-MODIFIED:20211129T175012Z
UID:70343-1638270000-1638291600@c2smart.engineering.nyu.edu
SUMMARY:Forensic Analysis of Vehicular Malfunctions Using On-Board Data
DESCRIPTION:This event explores forensic analysis and recording of vehicle dynamic performance tests and investigations. To accomplish these tests and investigations\, researchers developed forensically accurate and reliable test monitoring and recording instruments. Because the tests were usually sequential and interactive between the vehicle operator and the particular exemplar vehicle\, those instruments have to allow for real time graphical readouts and displays of parameters from both vehicle data networks and from instrumentation data networks. \nIt is hoped that the methodologies and examples shown here will encourage students and professionals to explore data analysis designs and solutions beyond basic or textbook procedures by providing examples to show real world data projects and test instruments that are now repeatedly used in many continuing vehicle forensic investigations. Additionally\, these working examples have been shown to assist associated skill disciplines such as Reconstruction and Biometric Analysis of these same accidents. \nSPEAKERS \nWilliam Rosenbluth has been President and Principal Engineer for Automotive Systems Analysis (ASA)\, Reston\, VA\, for 33 years. He has 58 years of experience with complex electro-mechanical\, electronic and computer components and systems. He was employed by the IBM Corporation for 21 years\, until forming ASA. At ASA\, he specializes in the analysis and diagnosis of computer-related vehicle control systems and in the retrieval and analysis of electronic crash-event data in accident vehicles (black box data). His has authored two books\, ‘Investigation and Interpretation of Black Box Data in Automobiles’ (2001) and ‘Black Box Data from Accident Vehicles’ (2009). He holds a BEE (‘61) and an MSEE (‘65) from the Polytechnic Institute of Brooklyn. \nPeter J Sullivan has been President and Principal Engineer for Advanced Analysis Associates\, Inc\, for the past 26 years. His performs Forensic Expert Witness investigations for clients throughout the US\, and he testifies in State and Federal Courts throughout the US. In his investigative capacity\, he performs data downloads and imaging of Electronic Control Modules and ESI\, including analysis and application to elements of accident reconstruction\, validation\, and electronic testing\, on almost all makes and models of vehicles\, equipment. and hand-held electronics. He holds a Bachelor of Science in Chemistry and Physics (‘84) from Texas State University.
URL:https://c2smart.engineering.nyu.edu/event/forensic-analysis-of-vehicular-malfunctions-using-on-board-data/
LOCATION:370 Jay Street\, Room 825\, 370 Jay Street\, Room 1201\, New York City\, NY\, 11201\, United States
CATEGORIES:Big Data & Planning for Smart Cities
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211117T120000
DTEND;TZID=America/New_York:20211117T130000
DTSTAMP:20260429T070815
CREATED:20211102T205148Z
LAST-MODIFIED:20211102T205148Z
UID:69645-1637150400-1637154000@c2smart.engineering.nyu.edu
SUMMARY:Building a Decision Support Tool for Optimal & Equitable Distribution of EV Charging Stations in NYC
DESCRIPTION:Electric vehicles (EV) are key to the world’s decarbonization effort\, but access to charging infrastructure may become a prominent adoption barrier\, the burden of which will disproportionately affect low- and middle-income communities\, communities of color\, areas near multi-family housing\, and residential and rural areas. Researchers led by Professor Yury Dvorkin\, NYU Tandon\, and Professor Burçin Ünel\, Energy Policy Director at the Institute for Policy Integrity at NYU School of Law\, set out to address the accessibility of EV charging to build a decision-support tool to inform where and how to provide optimal\, equitable investments in EV charging infrastructure. In the first phase of their research\, they found that availability and affordability of EV charging stations in NYC are more strongly associated with median household income and the percentage of white population in each zip code\, rather than population density. \nThe team is hosting this update on their research so far\, seeking input from primary stakeholders to understand the technological gaps that prevents widespread expansion of EV charging infrastructure and to encourage their continued participation in this research to drive practically feasible and ethical research outcomes.
URL:https://c2smart.engineering.nyu.edu/event/building-a-decision-support-tool-for-optimal-equitable-distribution-of-ev-charging-stations-in-nyc/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Big Data & Planning for Smart Cities,Equity & Accessibility,Virtual Events
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211019T150000
DTEND;TZID=America/New_York:20211019T160000
DTSTAMP:20260429T070815
CREATED:20211015T152545Z
LAST-MODIFIED:20211015T153257Z
UID:69113-1634655600-1634659200@c2smart.engineering.nyu.edu
SUMMARY:Proactive Safety Management Empowered by Big Data
DESCRIPTION:In the last few decades\, data-driven methods have been used to assist with key tasks of road safety management like hotspot identification\, countermeasure development\, and before-after evaluation. These methods have traditionally relied heavily on historical crash data for safety assessment\, which can take a long time to collect. Professor Kun Xie will share a more proactive and time-efficient approach based on surrogate safety measures (SSMs)\, which can assess safety by capturing the more frequent “near-crash” situations. Massive amounts of data from emerging sources like GPS devices\, smartphone apps\, traffic cameras\, naturalistic driving\, and connected vehicles (CV) can be leveraged to extract SSMs on a large scale\, presenting new opportunities for proactive traffic safety management. Results will show that risk status is a reliable criterion for safety assessment\, and promisingly point towards the use CV data for proactive traffic safety management.
URL:https://c2smart.engineering.nyu.edu/event/proactive-safety-management-empowered-by-big-data/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Big Data & Planning for Smart Cities,Safety in Transportation Systems,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210908T150000
DTEND;TZID=UTC:20210908T160000
DTSTAMP:20260429T070815
CREATED:20210902T204704Z
LAST-MODIFIED:20210902T204704Z
UID:1480-1631113200-1631116800@c2smart.engineering.nyu.edu
SUMMARY:Incentive Design for Promoting Ridesharing
DESCRIPTION:Traffic congestion has become a serious issue around the globe\, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However\, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation\, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. At this event\, Neda Masoud will discuss a study which addresses these obstacles by introducing a Traveler Incentive Program (TIP) to promote community-based ridesharing with a ride-back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides\, and (2) encourage a few\, carefully selected set of travelers to change their travel behavior. We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem and present a Lagrangian Relaxation-based algorithm with worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore\, using a flat tax rate as low as 1% can double the system’s social welfare in the budget-balanced variant of the incentive program. \nBio: Neda Masoud is an Assistant Professor of Civil and Environmental Engineering at the University of Michigan. She holds a Bachelor’s of Science Degree in Industrial Engineering and a Master’s of Science degree in Physics. She received her Ph.D. in Civil and Environmental Engineering from the University of California Irvine. Her research focuses on devising operational and planning tools to facilitate the transition into the next generation of mobility systems\, which are envisioned to be connected\, automated\, electrified\, and shared.
URL:https://c2smart.engineering.nyu.edu/event/incentive-design-for-promoting-ridesharing/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Big Data & Planning for Smart Cities,Virtual Events,Webinars
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