Connected Cities with Smart Transportation
Peruse all current, past, and planned research projects from C2SMART center researchers and affiliates.
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.
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.
The COVID-19 outbreak has dramatically changed travel behavior in cities across the world. With changed travel demand, economic activity, and social-distancing/stay-at-home policies, transportation systems have experienced an unprecedented shift in demand and usage. Since the start of the pandemic, the C2SMART research team has been collecting data and investigating the impact of COVID-19 on mobility and sociability.
The AASHTO-FHWA Joint Agreement for the Implementation of MASH 2016 requires that any roadside safety hardware (guide rail, bridge rail, transitions, attenuators, etc.) to be installed on the National Highway System must be MASH-compliant. Transitions were not previously required to be crash tested, so the NYSDOT designs needs to be.