Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
This project aims to improve the efficiency of mobility-on-demand services with the help of machine learning. The goal is to create an algorithm that public paratransit services, private rideshare companies, and future autonomous vehicle fleets could use to improve operations and lower costs.
This project aims to quantify the various safety, environmental and financial benefits of connected vehicle technologies applied to the New York City municipal fleet.
This project will develop a mobile app that streamlines the reporting of mis-parked dockless scooters and bikes, relaying data to companies responsible and/or local governments while generating a data set that can support a variety of research questions.
This project aims to extend and field-test CAV-based traffic signal/vehicle control methods developed by the research team in previous projects to understand and quantify the benefits of CAV-based control in the real world.
The main objective of this study is the assessment of the Construction Impact Analysis (CIA) and Work Zone Impact and Strategy Estimator (WISE) tools, and determining the feasibility of their customization with respect to New York City Department of Transportation (NYCDOT) and New York State Department of Transportation (NYSDOT)’s needs and requirements, cost of adoption and modification, and related issues.
This project aims to develop a student parking lot zoning and zone permit pricing model, and then integrate the total demand model, “base price” model, and the zoning model into a software tool named Sparkman, which can be used by all university parking offices.
In previous years, the research team has developed and calibrated a base model implemented in MATSim and SUMO. This virtual testbed simulates an 8-million-person population and includes cars, trains, bus, bikeshare, taxi, and other for-hire vehicles calibrated to the year 2016. The team is building the architecture to host this virtual test bed and developing system design and user guide documentation.
Through wearable sensors and realistic representations of work zones in virtual reality, we plan to collect worker behavioral and physiological (heart rate) responses to warnings issued under various realistic scenarios and various warning mechanisms.
This project aims to develop a comprehensive holistic model of urban transportation demand given multiple available modes, including for-hire vehicles and their shared options. The model will enable assessment of the impact of shared mobility on urban transportation mode choice, which can be further translated into economic, social, and environmental impacts.
This research will focus on false data injection attacks, in which a malicious agent aims to affect the behavior of vehicles in the network by injecting false information about, for example, the traffic condition in the area or the availability of charging stations.