Principal Investigator: Anil K. Agrawal, The City University of New York (City College)

Co-PIs: Mohsen Amjadian, Hani Nassif

While there has been intense focus on ubiquitous sensing of transportation infrastructures, powering sensors and other peripherals by drawing wires from existing utility lines becomes cost-prohibitive and frequently a complex operation. These sensors can be powered by alternative sources of power much more efficiently and at significantly lower costs. Harvesting energy from ambient vibration sources, including traffic induced vibration of transportation infrastructures, is one of the most attractive options for powering sensors on transportation infrastructures. The highway statistics shows that the average daily vehicles miles travelled in the US is more than 5000 million, representing a massive source of kinetic energy that lies unused in the national transportation network. The principal investigators have been investigating the application of electromagnetism in smart transportation infrastructures. The proposed research aims to develop an innovative approach, termed as electromagnetic energy harvesting system (EMEHSs), for energy harvesting from transportation infrastructures.

Principal Investigator: Xuegang (Jeff) Ban, University of Washington

Co-PIs: Yinhai Wang, Don Mackenzie

A modeling framework is proposed to integrate ridesourcing services and connected/automated vehicles with transit to serve different users in an urban area. Multiple travel modes are considered for morning commute: single ride and shared ride in ridesouring, and integrated ridesourding (either single ride or shared ride) and transit. Simulation testing and validation will be conducted on a multimodal network in the Seattle area.

Principal Investigator: Kelvin Cheu, Univerisity of Texas at El Paso (UTEP)

Co-PI: Jeffrey Weidner

In SPARKMAN, UTEP researchers have developed a university student parking lot Zoning and Zone Permit Pricing (Z2P2) model and coded it as a software tool named SPARKMAN, and conducted a student survey determining the ITS needs for campus parking based on the survey data, and established the Level of Service (LOS) criteria for parking. This project will focus on the LOS for parking and will extend the concept of LOS for parking to a Performance Measurement System (PMS) for parking. The objectives of this project are to develop LOS analysis procedures for open surface parking lots, develop a LOS analysis procedure for multistory parking garages, develop a Concept of Operations (ConOps) of a PMS in smart parking garages, and explore PMS for street parking using existing smart technology. The LOS analysis procedure will incorporate field data collection procedures by traditional traffic survey techniques including sensing and potential use of mobile smartphone applications.

Principal Investigator: Joseph Chow, New York University

Co-PIs: Rae Zimmerman, Zhibin Chen

What built environment and sociodemographic settings make ridesharing services most successful? How does dependency on microtransit vary for different types of operations? How do these benefits vary by users of the market? How do they perceive and value these benefits relative to their needs? Consequently, if the city agencies wish to construct a portfolio of solution options, how should they design their portfolios? How do electric charging requirements impact these decisions? This project will develop a model relating performance metrics of different classes of operations to city and service attributes. The model will then be applied within a service coverage optimization model to identify Pareto-dominant projects within a city to construct a portfolio of different service options and user preferences. In its focus on the user setting in terms of demographics and user behavior and preferences, the research differs from typical microtransit research in complementing research that tends to focus on characteristics of the industry and the physical systems.

Principal Investigator: Semiha Ergan, New York University

Co-PI: Junaid Khan

Despite increased regulations, restrictive measures, and devices used for warnings, work zone injuries and fatalities are still observed at highway construction projects with alarms/notifications being ignored. With a vision to reduce the number of injuries and fatalities, Phase 2 of our worker safety project extends the original scope and adds a Hardware in the Loop (HIL) component to simulate real traffic scenarios through simultaneous interactions with variety of vehicles and deployed sensor data in immersive virtual environments. The project aims to understand the key parameters that play roles in behaviors of workers in response to notifications received from various warning mechanisms. Key questions this research answers are, at what conditions workers ignore/response to warnings at work zones? How we can calibrate notification systems for getting responsive actions from workers? What are the modalities, frequencies, and timings of pushing notifications in these calibrated systems? Through wearable sensors and hardware integrated realistic representations of work zones in virtual reality, we plan to collect worker behavioral and physiological (heart rate) responses to alarms/warnings/notifications issued under various realistic scenarios and modalities of warning mechanisms and analyze these captured data towards understanding human behaviors in response to modalities of notifications.

Principal Investigator: Elizabeth Henaff, New York University

Co-PIs: Andrea Silverman, Tega Brain, Junaid Ahmed Khan, Charlie Mydlarz

Of the myriad climate change impacts, flooding is expected to have an outsized influence on public health and infrastructure. In New York City, for example, sea level rise and high intensity rain storms 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 flood water 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. While there exist commercially available sensors that detect the presence of water inside homes, there is an unmet need for hyperlocal information on the presence and depth of street-level floodwater.

Principal Investigator: Zhong-Ping Jiang, New York University

Co-PI: Kaan Ozbay

This project aims to develop innovative adaptive learning algorithms to tackle the combined longitudinal and lateral control of autonomous vehicles and its extension to optimal cooperative adaptive cruise control (CACC) of connected and autonomous vehicles. Learning-based suboptimal vehicle controllers will be designed, along with robustness analysis in the presence of human reaction time and exogenous disturbances.

Principal Investigator: Li Jin, New York University

This project develops tools for building secure-by-design intelligent transportation systems (ITSs). We will develop proactive and reactive mechanisms that protect ITS from cyber attacks and recover ITS from security failures. In particular, we focus on a class of cyber attacks that manipulate the dynamic routing via modifying sensing/ actuating, which is called spoofing. We will consider the allocation of proactive security resources such as redundant communication channels and diagnosis capabilities, and reactive strategies such as secure dynamic routing. We consider a Markovian queuing network model with dynamic routing that is applicable to a variety of ITSs and design defending strategies based on a security game model. We will utilize our expertise in queuing theory and game theory to derive a theoretical foundation for secure-by-design ITS. We will also apply the proposed approach to two typical ITS scenarios: app-based routing and signal-free intersections.

Principal Investigator: Hani Nassif, Rutgers State University of New Jersey

Co-PIs: Kaan Ozbay, Chaekuk Na, Peng Lou, Sami Demiroluk

This proposal will implement an Advanced Weigh-In-Motion (A-WIM) system for autonomous enforcement of overweight trucks and study its effectiveness in reducing the number of illegal overweight trucks in an urban infrastructure environment. The work includes the development of various algorithms to help reduce the error in weighing vehicle weight due to environmental conditions and inherent factors, to accurately quantify the effects of illegal overweight trucks on infrastructure. In addition, the team will integrate and implement different technologies, such as camera, radio frequency identification (RFID), automatic license plate recognition (ALPR), etc. with A-WIM system at two potential sites located on the Brooklyn-Queens Expressway, Brooklyn, NY. The team will also perform life cycle cost analysis for various types of WIM sensors and systems to promote the most efficient and appropriate WIM system for use in autonomous enforcement.

Principal Investigator: Kaan Ozbay, New York University

Co-PI: Yue Zhou

This project proposes a comprehensive effort to resolve several common issues associated with prevailing traffic state estimation algorithms based on discrete-time first-order kinematic wave traffic flow models and to develop an adaptive ramp metering control system based on the improved traffic estimation scheme. The effort is composed of traffic flow model modifications, development of a multi-modal adaptive filtering approach to traffic state and parameter estimation scheme, development of measures to integrate emerging traffic data with conventional fixed-point measurements, development of a measure for on-line estimation of capacity-drop-proportion, development of an adaptive discrete switching feedback controller for ramp metering, and implementations of the proposed schemes in both macroscopic numerical simulation and microscopic traffic simulation platform.

Principal Investigator: John-Ross Rizzo, New York University

Co-PI: Chen Feng

Visual impairment engenders mobility losses, debility, illness and premature mortality. Mobility losses are also tied to compromised quality of life, and an unemployment rate that approaches 60-80% in almost every developed country in the moderately and severely VI stratum. In many cases, health and wellbeing are ‘attacked’ by vision loss in any form factor; psychosocial barriers such as anxiety and depression are compounding influences that increase as deficits scale; fear of falling is a threat that contributes to this downward ‘spiral’ and often goes unchecked; this fear is alarmingly justified, as visual impairment precipitates substantial increases in mechanical trips, falls and long-bone factures.This project will increase the safety profile and ease-of-use of the VIS 4 ION (Visually Impaired Smart Service System for Spatial Intelligence and Onboard Navigation) platform toward ‘connected’ dynamic navigation in complex urban environments, providing a new level of security to the end user and permitting one to break down significant barriers to employment and social interaction.

Principal Investigator: Yinhai Wang, University of Washington

Co-PI: Wei Sun

Environment perception and understanding remains to a challenge for (Advanced Driver Assistance Systems) ADAS. Specifically, precise 3-D detection of vehicles and pedestrians, and understanding road users’ intentions, are needed for roadway traffic decision making and parking assistance. Multiple challenges can be solved by fusing the real-time data of on-board sensors and road-side units instead of solely relying on onboard sensing technologies. The objective of this project is to develop a cooperative perception system that fuses data from on-board and road-side units. Considering the limited computational and storage resources on the edge-side, edge-device- based artificial intelligence algorithms will be developed and employed for enhancing the efficiency of the proposed methods without losing accuracy.