Overview Highway transportation is the most common mode of transport in the U.S. for the movement of goods and services, as it provides quick delivery of shipments traveling short distances
This project is focused on developing a deep learning based data acquisition and analytics tool using vision-based sensors (i.e., cameras) to understand cities with machine eyes.
Overview Transportation is a major source of greenhouse gas emissions and air pollution, with emissions from light-duty vehicles constituting its major share. For example, the light-duty vehicles in New York
Traffic signs are critical assets for roadway and infrastructure management. They are also in a great variety and different conditions. According to the asset management plan proposed by US DOT, the research team proposes a cost-effective approach to build a traffic sign data inventory using open street images.
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.
A broad API will be developed to handle interfacing any simulation with a multi-agent demand simulator. This will be tested on the existing MATSim-NYC (which will be enhanced to include freight and parcel delivery activities) and aBEAM implementation, BEAM-NYC, for three use cases in electric transit, freight, and traffic.
The project will build a framework to optimize and prioritize locations for FloodNet sensor deployment, for measurement of hyper – local flooding in New York City (NYC).
Overview One of the enduring challenges in statewide transportation planning is that consistent population travel data remains scarce, particularly for underserved and rural communities. Planning models are often only estimated
This research targets a very low-cost Artificial intelligence (AI) based overheight vehicle warning system for bridges based on the use of cutting-edge camera technology, augmented reality and AI based height detection approach.
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.