Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
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.
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.
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.
Over the course of the Mobility Data Dashboard’s development, C2SMART has published multiple white papers alongside peer-reviewed articles for transportation journals and conferences.
In 2022, C2SMART is focusing on collaboration, implementation, and equity, looking to ensure that all our projects have long-term impact on the transportation systems in our Consortium cities.​
C2SMART Mobility Data Dashboard An all-in-one data dashboard was built with interactive analytics and visualization of various mobility data sources. The mobility board presents multi-data views in terms of vehicular