Author: Liu Yang

Work Zone Safety: Virtual reality-based traffic co-simulation platform for workforce training and pedestrian behavior analysis

Liu Yang

Building off of the research team’s previous work on a smartwatch alarm application and worker attention monitoring system, this project will expand the scope to a) understand workers’ behaviors to modalities of alarms in real physical work environments, and b) improve the VR based traffic co-simulation platform to co-simulate workers position in SUMO in real time as obstacles to be recognized and calibrate the vehicle trajectories in SUMO through larger work zone/traffic vehicle trajectory datasets.

Automated Lane Change and Robust Safety

Liu Yang

Inappropriate lane changes are responsible for one-tenth of all accidents, due to human drivers’ inaccurate estimation and prediction of the surrounding traffic, illegal maneuver, and inefficient driving skill. Autonomous lane changing is regarded as a solution to reduce these human errors. At present, there are many obstacles to developing automated lane-changing technology, including interactions between vehicles, complex routing choice, and interactions between vehicles and the environment. Building on our prior work on lane keeping and lane changing, this collaborative research project aims to take a significant step forward to develop innovative solutions for autonomous lane change maneuvers.

Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Phase 2

Liu Yang

This project is a continuation of a C2SMART funded project from 2021 titled “Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems.” In the phase 1 project, the team built a digital shadow of campus civil infrastructure and visualized impacts of construction project schedule on the surrounding transportation infrastructure. Phase 2 is focused on expanding the work accomplished in phase 1, to extend the digital twin and enable live data feeds.

C2SMART Mobility Data Dashboard: MATSim

Liu Yang

As part of the MATSim-NYC development, we calibrated a behavioral model (estimated as a tour-based nested logit model) for choosing different modes of transportation in NYC, which was encoded into the MATSim-NYC simulation as utility function parameters. Using the COVID mobility data, we recalibrated the behavior model to reflect the increased aversion to use shared use modes like public transit.

C2SMART Mobility Data Dashboard COVID-19 Travel Survey

Liu Yang

An online survey focusing on travel trends under the impact of COVID-19 was administered from July to October 2020, after approval by the Institutional Review Board (IRB). The objective of the survey is to look at how different disadvantaged population groups, especially people with disabilities and low-income households, were affected by the changes as a result of COVID-19.