Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
C2SMART researchers developed a more efficient, secure, blockchain-based system to story mobility data on a distribute ledger. To store this data at scale, researchers leverage InterPlanetary File System (OPFS), a scalable distributed peer-to-peer data storage system, and develop efficient consensus algorithms to prevent users from injecting malicious or fake trajectories into the ledger.
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.
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.
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.
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.
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.