Connected Cities with Smart Transportation
Peruse all current, past, and planned research projects from C2SMART center researchers and affiliates.
This perception of transit can be captured through the sentiment of posts made by users on social media. Performance metrics based on user perception can provide the service provider a customer-facing view of their service and help enhance their service and address the user concerns, especially from a public health standpoint in a post-COVID lockdown world, appropriately.
This project’s goal is to research, evaluate and test the ability and effectiveness of using open source ATMS software to center functionalities in a Regional TMC setting as a future procurement option to support the evolving TSMO program.
This project focuses on the feasibility of the proposed EMEH to power sensors that are used to regularly monitor the structural integrity of materials and components of highway bridges such as acceleration and temperature sensors.
The primary objective of this research project is to define quantifiable metrics that make it possible to adequately represent accessibility of EV charging infrastructure and to internalize these metrics in decision-support procedures and tools that are used by utilities and authorities to determine electricity rates (tariffs) and additional incentives to promote investments in EV charging infrastructure.
Traffic simulation is an important tool that can assist researchers, analysts, and policymakers to test vehicle/traffic control algorithms, gain insights of micro/macro traffic dynamics, and design traffic management strategies. However, different implementations require different simulation scales and there is no multiscale simulation platform that satisfies all requirements. In this research, we propose to establish a multiscale vehicle-traffic-demand (VTD) simulation platform for connected and automated transportation systems (CATS).
According to the Federal Highway Administration (FHWA), work zone fatalities at road construction projects account for up to 3% of all workplace fatalities in a given year. We propose adding new functionality to the current VR platform to track the subjects’ attention through his/her head-movement and eye-movement to infer his/her gaze pattern. With the introduction of this method to measure the subject’s attention, we plan to capture additional critical information about the decision a worker makes.
This project aims to develop a platform to provide real-time, street-level flood information – including the presence, frequency, and severity of local surface flood events – to a range of stakeholders, including policymakers, government agencies, citizens, emergency response teams, community advocacy groups, and researchers.
This research aims to explore the basic research on developing signal control and coordination methods under the CV environment, develop a framework for urban traffic signal optimization with CVs, and test the developed methods both in traffic simulation and using real-world CV data.
In order to guarantee the safety of autonomous vehicles (AV), improve passenger comfort, and increase traffic efficiency, we aim to develop innovative learning-based control methods for lane changing of connected and autonomous vehicles (CAVs) in mixed traffic by a combined use of reinforcement learning and optimal control techniques.
This study aimed to develop a demand model for an eFFCS service in the City of Seattle, which can increase the feasibility of eFFCS by reducing the cost of relocation by optimally locating the charging stations near the areas of heavy usage and real-time control to minimize manual relocation.