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ITE MET Section Student Mixer
2023-10-14 12:30 - 13:30 (America/New_York) | 370 Jay Street
During the lunchtime portion of TransportationCamp, members of the ITE Met Section student chapters can meet and chat! This will be a great opportunity to get to know interesting and like-minded people from Cooper Union, NYU, Manhattan College, NJIT and TCNJ.

TransportationCamp 2023
2023-10-14 | 370 Jay Street
TransportationCamp brings together thinkers and doers in transportation and technology. It’s not a traditional conference.
The core of TransportationCamp consists of presentations, discussions, and activities led and planned by attendees themselves. TransportationCamp will convene in-person for the ninth consecutive year of events.
This year’s event will be at NYU’s Tandon School of Engineering on October 14, 2023.

Student Learning Hub: Introduction to Neural Networks (NNs): How and When to Use Recurrent NNs and Convolution NNs
2023-10-13 11:30 - 12:30
Instructor: Haggai D. Davis, III, New York University
Hands-on exercise: Yes
Beginner level: No prior experience required.
Schedule: Friday Oct 13th, 11:30am-12:30pm
Description: Machine learning is an incredibly broad term that encompasses a lot of different algorithms which can be applied to solve a variety of problems. Neural networks are one category of ML algorithms which consist of layers of nodes (or neurons) which will activate to transmit information when certain thresholds are met. RNNs and CNNs are subclasses of neural networks whose differing structures allow for the analysis of different types of datasets, usually things like time-series and pattern-recognition, respectively.

CUE PhD Biweekly Friday Seminar
2023-10-13 10:30 - 11:00
Updated Quantifying and Visualizing City Truck Route Network Efficiency Using a Virtual Testbed
Presented by Haggai Davis, III

Biweekly Coffee and Tea Break
2023-10-12 14:00 - 15:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
Come stop by the Data Viz Lab for some light refreshments!

Project Mjolnir : The Journey of Designing an Open Source Adaptive Mountain Bike
2023-10-12 10:00 - 11:00
Presented by NYU Shanghai’s Noel Joyce

Transportation Jeopardy! NYU ITE vs. Northeastern
2023-10-03 18:00 - 19:30 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
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!

Developing a Framework to Optimize Floodnet Sensor Deployments Around NYC for Equitable and Impact-Based Hyper-Local Street-Level Flood Monitoring and Data Collection
2023-09-28 12:00 - 13:00
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.
Presented by NYU’s Riccardo Negri and Prof. Luis Ceferino

Reducing US Transit Costs: An Empirical Review and Comparative Case Study of Portland, Manchester Rail Systems
2023-09-22 11:30 - 12:30
Presented by Chetan Sharma and Prof. Joseph Chow

Reconnecting Communities: Creating More Equitable Outcomes in Transportation Projects
2023-09-22 09:30 - 11:00 | NYU Wagner, 295 Lafayette Street
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.

Oliver Wyman Forum, NYC Climate Week
2023-09-22 09:00 - 10:00 | 1166 Avenue of the Americas, NYC, NY
C2SMARTER Director Dr. Kaan Ozbay will be serving as a panelist at the Oliver Wyman Forum during NYC Climate Week.
About the Oliver Wyman foundation:
The 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.

Seminar: Applications of data analytics in Smart Cities: Spatio-temporal crime prediction; and Epidemic forecasting based on mobility patterns
2023-09-20 15:00 - 16:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
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.
This 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.
The 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.
Presented by Eugenio Cesario, Associate Professor, University of Calabria
Eugenio 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, CloudGrid 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.

Civil Engineering Clubs Welcome Party
2023-09-19 18:00 - 19:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201

Transportation & Urban Systems Fall 2023 Welcome Event
2023-09-14 17:00 - 18:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
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:

Monthly research presentations taught by consortium members and industry partners
Potential research opportunities
Transportation courses offered this semester:

Forecasting Urban Travel Demand
Traffic Operations & Control
Management of Transit Maintenance and Operations
Urban Transportation & Logistics Systems

ECE Seminar: Boundary Stabilization of Polynomial Reaction Diffusion Equations
2023-09-14 14:30 - 15:30 | 370 Jay Street, Room 1013, 370 Jay Street, Brooklyn
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.
Presented by Alfred J. Krener.

NY Statewide Behavioral Impact Decision Support Tool with Replica
2023-09-07 12:00 - 13:00
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.
Presented by Xiyuan Ren; PHD Candidate, NYU; Joseph Chow, Associate Professor, NYU

Highlights of Lane-Free Automated Vehicle Traffic with Nudging
2023-09-01 13:00 - 14:00 | Virtual, 6 MetroTech Center, Brooklyn, NY, 11201, United States
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
manoeuvres. 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.
After 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

PhD New Student Orientation
2023-08-24 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201

C2SMARTER RFP Requirements Webinar
2023-08-16 12:00 - 13:00
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:

Feedback on received project abstracts and potential teaming opportunities
Proposal submission requirements
New USDOT reporting requirements
Project management and tracking systems
Timelines and schedules
Q&A

The Reconstruction of Penn Station
2023-08-15 12:00 - 13:30
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.

Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Phase 2
2023-08-14 12:00 - 13:00 | https://nyu.zoom.us/webinar/register/WN_jQg9UllxRB26w9QmMn9OqQ

Summer Webinar Series: A Variational Autoencoder Approach for Route Choice Set Generation
2023-08-03 14:00 - 15:00
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.
Shlomo 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.
Presented by the Transportation Research Board Subcommittee AEP30(2)/AEP40 on Route Choice and Spatio-Temporal Behavior

Webinar: Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems: Urban Work Zone Detection and Safety Analysis Using Near Miss Data
2023-07-25 12:00 - 13:00 | Virtual, 6 MetroTech Center, Brooklyn, NY, 11201, United States
Jingqin Gao: Assistant Director of Research, C2SMART, NYU; Chuan Xu: PHD Candidate, NYU
Tuesday, July 25, 2023 | 12:00pm ET | Virtual
While 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.

Transit Techies July Meetup
2023-07-17 18:00 - 19:30
Technologists who love transit.
Transit enthusiasts who hack!
At 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.

Webinar: Autonomous Vehicle Good Citizenry Standard
2023-06-29 12:00 - 13:00
Presented by:
Sarah Kaufman, Interim Director, NYU Rudin Center for Transportation
Joseph Chow, Associate Professor, NYU
Thursday, June 29, 2023: 12:00pm – 1:00pm ET | Virtual
New 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.

Summer Webinar Series
2023-06-15 15:00 - 16:00
Presented by the Transportation Research Board (TRB) subcommittee AEP30(2) Route Choice and Spatio-Temporal Behavior
Speaker: Professor Marcela A. Munizaga, Universidad de Chile.

Webinar: Quantifying and Visualizing City Truck Route Network Efficiency Using a Virtual Testbed
2023-06-13 12:00 - 13:00
Presented by: Joseph Chow, Associate Professor, NYU
Haggai Davis, PHD Candidate, NYU
Tuesday, June 13, 2023: 12:00pm – 1:00pm ET | Virtual

Summer Webinar Series- Route Choice and Spatio-Temporal Behavior: The Perturbed Utility Route Choice Model
2023-05-24 12:00 - 13:00
The 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.
SPEAKER
Mogens 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.
Presented by the Transportation Research Board Subcommittee on Route Choice and Spatio-Temporal Behavior (AEP30/AEP40)

USDOT Free Public Webinar: Intersection Safety Challenge Prize Competition
2023-05-22 15:30 - 16:45 | Virtual, 6 MetroTech Center, Brooklyn, NY, 11201, United States
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.
Each 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.
To 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.
The 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.
The 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.
For 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/.

Deep Neural Networks for Choice Analysis
2023-05-19 14:00 - 15:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
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.

SPEAKER
Shenhao 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).
