The research team has already established an online transportation platform, named the Digital Roadway Interactive Visualization and Evaluation Network (DRIVE Net). DRIVE NET can be used for sharing, integration, visualization, and analysis of transportation-related data. The proposed research aims to extend the functions of DRIVE Net by developing an AI platform for network-wide congestion detection and prediction using multi-source data.
This research project aimed to develop a data-driven approach for modeling cities, with a focus on pedestrian dynamics, which play a fundamental role in urban planning. It focused on detecting and counting objects such as pedestrians, cars, and bicycles in visual data sources that can provide insight into how people move around a city. The research team used an image database made up of tens of millions of images produced by Brooklyn-based start-up Carmera as its main data source.
This project developed real-time distributed network control techniques capable of utilizing various types of real-time traffic data, from both fixed and mobile sources. The work is divided into two major parts: traffic state estimation when data is limited and adaptive control.
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.
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