C2SMART Announces 2021 USDOT Funded Projects

C2SMART has designated funding for 14 new projects that seek to tackle a wide range of issues in transportation that fall under the center’s 6 research areas. This year’s request for proposals emphasized either the implementation of previous research, such as expanding the FloodSense team’s hyperlocal flood sensors to a greater geographic area, or new collaboration between departments, like the joint research project “Equitable Access To Residential (EQUATOR) EV Charging” conducted by NYU CUSP’s Professor Yury Dvorkin and Professor Burçin Ünel, Energy Policy Director at the Institute for Policy Integrity the NYU School of Law. NYU Professor Massoud Ghandehari and Associate Provost for Research at Rutgers University-Camden, Benedetto Piccoli, join the Center’s group of principal investigators.

A request for proposals was put out in November 2020, and proposals were solicited from C2SMART’s member institutions. Submitted proposals underwent a multi-step review process, including multiple independent reviews. The following projects were award in March 2021:

Field Application of a High-Power Density Electromagnetic Energy Harvester to Power Wireless Sensors in Transportation Infrastructures

Principal Investigator: Anil Agrawal, CCNY

Co-PIs: Mohsen Amjadian, CCNY & Hani Nassif, Rutgers

Research Area: Infrastructure Resiliency

The key objective of this research is to fabricate and field test a high-power density electromagnetic energy harvester (EMEH) to provide a power source for conventional sensors installed on transportation infrastructures. This EMEH is expected to be simple, but effective in harvesting kinetic energy and converting it to electric power. Practical and economic feasibility and field implementation of the device on three steel-girder (or truss) highway bridges with three different fundamental frequencies will also be investigated in this work. Based on detailed numerical simulations and modeling, a prototype model of the device will be fabricated and then will be installed on three bridges to demonstrate the technology and its effectiveness in powering typical health monitoring sensors including acceleration sensors. Hence, the expected outcome of this research are:

  • Development and field demonstration of the electromagnetic energy harvester to derive wireless sensors.
  • Development of technical tools and design instruction to install and maintain the EMEH on highway bridges.

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A Multiscale Simulation Platform for Connected and Automated Transportation Systems

Principal Investigator: Xuegang Ban, UW

Co-PIs: Yinhai Wang, UW & Don Mackenzie, UW

Research Area: Connected and Autonomous Vehicles

Traffic simulation is an important tool that can assist researchers, analysts, and policymakers to test vehicle/traffic control algorithms, gain insights of micro/macro traffic dynamics, and design traffic management strategies. However, different implementations require different simulation scales and there is no multiscale simulation platform that satisfies all requirements.

In this research, we propose to establish a multiscale vehicle-traffic-demand (VTD) simulation platform for connected and automated transportation systems (CATS). This is in particular for the control and management of CATS with varying penetration of connected and automated vehicles (CAVs). The research group has built a microscopic vehicle-in-the-loop (VIL) simulation platform, which uses Unity 3D to simulate/visualize vehicle operations/dynamics and Simulation of Urban Mobility (SUMO) to simulate traffic flow dynamics. Figure 1 shows an overview of the VIL simulation model.

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Autonomous Vehicle Good Citizenry Standard

Principal Investigator: Sarah Kaufman, NYU

Co-PI: Joseph Chow, NYU

Research Areas: Equity and Accessibility; Connected and Autonomous Vehicles

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, including safe and accessible for users of all genders, income levels, and abilities, equitably deployed across neighborhoods, especially those will low transit availability, responsible use of data, especially when potentially exposing individuals’ immigration, religious or other private information to third parties, in particular, taking open mobility data standards and examining how they can accommodate under-represented populations, and adaptable to aging infrastructure, particularly in flood zones and in the event of power outages.

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Quantifying and Visualizing City Truck Route Network Efficiency Using a Virtual Testbed

Principal Investigator: Joseph Chow, NYU

Co-PI: Kaan Ozbay, NYU

Research Area: Big Data and Planning for Smart Cities

Cities like NYC and Seattle need to deal with significant growth of urban deliveries as a result of increasing e-commerce compounded by increased stay-at-home behavior due to COVID-19.

Even with the production of vaccines and a return to “normality” in the next year or so, it is likely that the introduction of e-commerce to so many more consumers will maintain its penetration. As a result, these cities are seeing more truck trips going to residential neighborhoods. To combat these challenges even before the emergence of COVID, city agencies have worked with major goods providers, delivery companies, and third-party logistics providers to explore alternative last-mile delivery modes like cargo bike, better curb management policies, off-hour delivery programs, neighborhood loading zones, planning designated truck routes, and evaluating truck route compliance.

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Equitable Access To Residential (EQUATOR) EV Charging

Principal Investigator: Yury Dvorkin, NYU

Co-PI: Burcin Unel, NYU

Research Areas: Equity and Accessibility; Infrastructure Resiliency

The primary objective of this research project is to define quantifiable metrics that make it possible to adequately represent accessibility of EV charging infrastructure and to internalize these metrics in decision-support procedures and tools that are used by utilities and authorities to determine electricity rates (tariffs) and additional incentives to promote investments in EV charging infrastructure.

Accordingly, the proposed research effort will be organized in two phases:

Phase I. Metrics: Define metrics for evaluating the accessibility of EV charging, including:

  • Affordability intends to characterize the cost of EV charging relative to the amount that the purchaser is able to pay
  • Environmental benefits will be defined in terms of the social cost of abated CO2 emissions, air quality benefits, and public health benefits
  • Quality of services seeks to measure the convenience of using EV charging stations

Phase II. Optimal Investments in EV Charging: Internalize the metrics developed in Phase I in decision-support tools for optimizing incentives and investments for public EV charging infrastructure. Using the metrics developed above, we will formulate a planning model to optimize the roll-out of EV charging infrastructure from the viewpoint of a benevolent urban planner (e.g. transportation authority or public service utility commission) that seeks to determine the most economic locations for prospective EV charging stations, while satisfying techno-economic constraints on the operation of the power and transportation systems and avoiding social imbalances across the city.

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A Comprehensive Analysis of the Air Quality in the NYC Subway System

Principal Investigator: Masoud Ghandehari, NYU

Research Areas: Big Data and Planning for Smart Cities; Infrastructure Resiliency

Exposure to ambient particulate matter (PM) air pollution is one of the leading risk factors for global disease burden, including respiratory, cardiovascular, metabolic, and mental health disorders. These include asthma, heart disease, diabetes, as well as bipolar disorder, and anxiety. The mechanism for the development of these adverse outcomes is that the particulates (often as byproducts of fossil fuel combustion) have a very small size (less than 3 microns in diameter) and they can penetrate the lung tissue and into the bloodstream, where the toxicity of the metal ions within the particles leads to oxidative damage, inflammation, and neurotoxicity. In the US, the Environmental Protection Agency regulates the standards for clean air. They also monitor these standards working with local/state agencies.

For example, in the early 2000s, NYC was deemed out of compliance in the Sulphur Dioxide (SO2) concentrations. This resulted in the city initiating a major program to identify the sources and to plan for remedies. Buildings’ fuel oil was subsequently identified as the source, and local laws were set in place to convert from heavy fuel oil to alternatives. At the moment the SO2 concentrations in NYC are very low, well below clean air standards. This has been considered as one of the success stories where timely information and good governance have rapidly led to positive climate action and public health impact. Elimination of heavy fuel oils in NYC also resulted in reductions in the concentrations of particulate matter, namely PM2.5 (particles with a size smaller than 2.5 microns). However, there are still neighborhoods in the city which are out of compliance, in terms of short-term ambient concentrations which are set by US EPA at 35 mg/m3. These measurements pertain to outdoor street-level environments.

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Lane Changing of Autonomous Vehicles in Mixed Traffic Environments: A Reinforcement Learning Approach

Principal Investigator: Zhong-Ping Jiang, NYU

Co-PI: Kaan Ozbay, NYU

Research Area: Connected and Autonomous Vehicles

According to an accident report from Volvo, lane changing maneuver is the main cause of various severe highway accidents, due to human drivers’ inaccurate estimation and prediction of the surrounding traffic, illegal maneuver, and inefficient driving skill. Autonomous driving is regarded as a solution to reduce these human errors. At present, there are many obstacles to developing automated lane-changing technology, including dynamically changing interactions between vehicles, complex routing choices, and the interactions between vehicles and the environment.

In this proposal, in order to guarantee the safety of autonomous vehicles (AV), improve passenger comfort, and increase traffic efficiency, we aim to develop innovative learning-based control methods for lane changing of connected and autonomous vehicles (CAVs) in mixed traffic by the combined use of reinforcement learning and optimal control techniques.

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Evaluating Remote Repositioning for Shared Scooters

Principal Investigator: Don Mackenzie, UW

Research Areas: Shared and Micromobility

The utilization of vehicles, commonly measured in trips per vehicle per day, is a key to the economic and environmental sustainability of shared micromobility (i.e. bikeshare and scootershare) services. With large capital costs tied up in bikes or scooters, a micromobility operator needs its vehicles to be used frequently to break even or turn a profit. Employees or contractors driving vans to reposition scooters, collect them for recharging, or rectify parking problems increase both the net emissions and the operating cost per customer trip served. Therefore, increasing the number of customer trips per day, and reducing the frequency of service vehicle trips, contribute to both environmental and economic goals.

Self-repositioning scooters offer a promising path to increasing micromobility utilization, and lowering costs and emissions. Such scooters would use autonomous driving capabilities to relocate themselves, without having to be moved in a service vehicle.

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Collaborative Driving, Ramp Metering and Mean-field Controls

Principal Investigator: Benedetto Piccoli, Rutgers University

Co-PI: Kaan Ozbay, NYU

Research Area: Connected and Autonomous Vehicles

ADAS and autonomous vehicles allow new control paradigms in traffic management. As the time horizon for driverless cars technology shifted forward in the future, collaborative driving and communication open new possibilities in the next 5-10y to realize control policies aimed at increasing safety, reducing congestion, and dissipate waves.

Collaborative driving results are available for vehicle-level controls and mostly focused on architecture and human-in-the-loop approaches. We aim at a macroscopic and network-level approach to exploit the potential impact of collaborative driving.

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Exploring AI-based video segmentation and saliency computation to optimize imagery-acquisition from moving vehicles

Principal Investigator: Claudio Silva, NYU

Co-PI: Kaan Ozbay, NYU

Research Area: Big Data and Planning for Smart Cities

Mobile sensing has offered efficient, cost-effective data collection procedures that opened new research frontiers, specifically in urban sensing and transportation. In the past, due to highly costly and time-consuming data collection procedures, a limited number of urban indicators were measured and made available to researchers. Hence, our understanding of cities on many frontiers was bounded by the ability to collect, record, manage, and store data. Recent advancements in producing low-cost sensing devices, together with the advent of new techniques in computer vision and machine learning, lead to the creation of massive data sets collected by fleets of sensor-equipped vehicles moving through streets.

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Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems

Principal Investigator: Jeffrey Weidner, UTEP

Co-PIs: Adeeba Raheem, UTEP & Kelvin Cheu, UTEP

Research Area: Big Data and Planning for Smart Cities

Digital Twin (DT) technology represents the next evolution in a gradual shift from physical to digital models in civil engineering. Computer-Aided Drafting (CAD) revolutionized the industry by reducing the time and costs associated with documenting the design. Building Information Modeling (BIM) has since all but eliminated the need for physical design descriptors (i.e., drawings or physical models). A digital twin is a relevant abstraction of the physical asset. Itis most frequently used to model/improve/control manufacturing systems. Civil engineering applications of DT have been starting to emerge, but transportation infrastructure represents a challenging extension of DT technology because of its spatial scale and voluminous and time-varying data. However, DT is a powerful decision support tool for the design, maintenance, and management of transportation infrastructure, particularly for studying the interdependency with other infrastructure systems.

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Deployment and Tech Transfer of a Street-level Flooding Platform: Sensing and Data Sharing for Urban Accessibility and Resilience

Principal Investigator: Elizabeth Henaff, NYU

Co-PI: Andrea Silverman, NYU

Research Areas: Infrastructure Resiliency; Big Data and Planning for Smart Cities

Of the myriad impacts that are predicted to accompany climate change, flooding is expected to have an outsized influence on public health, infrastructure, and mobility in urban areas. In New York City, for example, sea-level rise and an increase in the occurrence of high-intensity rainstorms have led to a dramatic increase in flood risk, particularly in low-lying and coastal neighborhoods. The physical presence of standing water on streets and sidewalks can impede mobility and restrict access to transportation. Additionally, urban floodwater contains a diverse array of contaminants, including industrial and household chemicals, fuels, and sewage., Access to real-time information on flooding can improve resiliency and efficiency by allowing residents to identify navigable transportation routes and make informed decisions to avoid exposure to floodwater contaminants. However, very little data exist on the frequency and extent of urban surface flooding, and there is an unmet need for hyperlocal information on the presence and depth of street-level floodwater. This unmet need for data from urban floods motivated the development of the FloodSense project in early 2020, with the objective of developing a platform to provide real-time, street-level flood information – including the presence, frequency, and severity of local surface flood events – to a range of stakeholders, including policymakers, government agencies, citizens, emergency response teams, community advocacy groups, and researchers.

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Integration and Operation of an Advanced Weigh-in-Motion (A-WIM) System for Autonomous Enforcement of Overweight Trucks

Principal Investigator: Hani Nassif, Rutgers University

Co-PIs: Kaan Ozbay, NYU, Chaekuk Na, Rutgers, & Peng Lou, Rutgers

Research Area: Infrastructure Resiliency

Roadways in New York City handle substantial daily traffic throughout different boroughs. Trucks have been an integral part of the freight movement network in distributing goods and services to various communities; however, a percentage of these trucks are often overloaded beyond legal load limits. The Brooklyn-Queens Expressway (BQE) that connects two boroughs suffers from significant deterioration because of the existing environmental conditions exacerbated by a substantial number of OW trucks.

The New York City Department of Transportation (NYCDOT) has been planning to rehabilitate the bridge to accommodate future traffic volume and weight demands. Accordingly, the team closely worked with the NYCDOT and collected truck traffic and weight data to provide recommendations for future design or rehabilitation work on BQE bridges. The team learned that the average daily number of OW trucks is significantly higher, and the extent of the OW (or OW tonnage) is substantially heavier than the national average.

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Work Zone Safety III: Calibration of Safety Notifications through Reinforcement Learning and Eye Tracking

Principal Investigator: Semiha Ergan, NYU

Co-PI: Kaan Ozbay, NYU

Research Area: Safety of Transportation Systems

According to the Federal Highway Administration (FHWA), work zone fatalities at road construction projects account for up to 3% of all workplace fatalities in a given year [1], and the primary causes are runovers/backovers, collisions, and caught in-between mobile equipment. Hence, drivers and the way they perceive the work zone and related notifications are primary factors required to reduce fatalities. A study of work zone crash data in five states showed that around half of the crashes occur within or adjacent to work activities, putting workers in danger together with drivers [2]. To reduce work zone injuries and fatalities, regulations such as mandated Personal Protective Equipment (PPE), traffic control plans, advance warning signs, the share of traveler information, and signal timing adjustments (ANSI, OSHA) were introduced by the regulatory bodies. However, these mainly aim for changing the behavior of drivers instead of workers. Although there is a large body of analysis and modeling literature related to work zone accidents as documented in [3], the actual safety treatments applicable to real-world work zones are limited at best and there is still a need for proactive approaches to be deployed at highway work zones, capable of warning construction workers of approaching hazards in advance.

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