Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
Peruse all current, past, and planned research projects from C2SMART center researchers and affiliates.
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.
Led by INTERCEP founding director Bill Raisch, this project aims to adapt an information sharing and situational awareness technology platform currently used by INTERCEP’s Metropolitan Resilience Network to support transportation data sharing and stakeholder engagement in New York City and each of the C2SMART consortium member cities. This platform is designed to help users understand their larger operating environment, identify risks in that environment, and make informed decisions during disruptions using the assembled data.
This research project will investigate the design and operations of dedicated lanes for fully automated trucks, the suitability of existing infrastructure to accommodate these novel technologies, and the potential economic ramifications on the surrounding region. The project will use the I-10 Freeway in El Paso, Texas, from the New Mexico border in the west to milepost 55 in the east, as the testbed.
This project aims to develop new models of pedestrian mobility using WiFi probe data as a novel data source. The models will be designed to scale to any region with a similar WiFi network infrastructure. Deliverables will also include a research paper submitted to a peer-reviewed journal and a final report.
The research team aims to test a new queueing network-based dynamic rebalancing strategy in test cases provided by ReachNow in Brooklyn, NY. In addition, the researchers will develop a MATSim agent model of the study area in NYC and calibrate it based on household travel survey data from NYMTC, Openstreetmaps, traffic data from NYCDOT, and transit schedules from GTFS.
In this project, the research team built on work done in a Year 1 C2SMART project, in which a decomposition method was developed to address traffic signal optimization. This project aims to develop methods to deal with mixed traffic flow and develop CAV-based signal coordination methods with multiple signalized intersections
This project aims to develop a systematic way to design smart highway systems with networked video monitoring and control resiliency against environment disruptions and sensor failures. The research team will investigate deep learning methods for extracting fine-grained local categorical traffic information from surveillance videos and novel graph neural network methods to correlate and propagate the local information through the highway network for global states estimation, such as vehicle tracking and reidentification or traffic prediction in an unobserved area.
Overview Remote repositioning (RR) technology allows a scooter or similar lightweight, slow-moving vehicle to be controlled by a human driver from a remote location. RR can resolve many mis-parking problems