Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
This project’s goal is to research, evaluate and test the ability and effectiveness of using open source ATMS software to center functionalities in a Regional TMC setting as a future procurement option to support the evolving TSMO program.
This project focuses on the feasibility of the proposed EMEH to power sensors that are used to regularly monitor the structural integrity of materials and components of highway bridges such as acceleration and temperature sensors.
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.
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).
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. We propose adding new functionality to the current VR platform to track the subjects’ attention through his/her head-movement and eye-movement to infer his/her gaze pattern. With the introduction of this method to measure the subject’s attention, we plan to capture additional critical information about the decision a worker makes.
This project aims to develop 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.
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 a combined use of reinforcement learning and optimal control techniques.
Overview Remote repositioning (RR) technology allows a scooter or similar lightweight, slow-moving vehicle to be controlled by a human driver from a remote location. RR can resolve many mis-parking problems
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.
Exposure to ambient particulate matter (PM) air pollution is one of the leading risk factors for global disease burden. Preliminary measurements carried out by the PI and colleagues have identified that concentrations of PM2.5 in subway cars frequently reach 100 mg/m2, and concentrations in stations reach 200 mg/m3, almost 6 times EPA standards.