Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
Peruse all current, past, and planned research projects from C2SMART center researchers and affiliates.
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.
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.
Overview Cement, the primary binding agent in concrete, accounts for up to 7 percent of global carbon dioxide emissions, according to the International Energy Agency, due to its widespread use and
Safety Performance Functions (SPFs) in the Highway Safety Manual (HSM) were developed using historic crash data collected in different states. Because local state or geographic conditions vary, to make the SPFs better accommodate the local data, two strategies are usually undertaken: the first strategy is to calibrate SPFs provided in HSM so that the contents of HSM can be fully leveraged and the second strategy is to develop location-specific SPFs regardless of the predictive modeling framework in the HSM.
An NSF project named “study of driving volatility in connected and cooperative vehicle systems” aims at extracting driving volatility, characterized by hard acceleration/braking, jerky movements, sharp lane changes or turns, and abnormally high speeds in a connected vehicle environment. The objective of this project is to model computationally efficient algorithms for predicting driver actions and volatility using information about their prior behaviors combined with positions and motions obtained via wireless communications.