An Artificial Intelligence Platform for Network-wide Congestion Detection and Prediction Using Multi-source Data

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.

A Multiscale Simulation Platform for Connected and Automated Transportation Systems

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).

Integrative Vehicle-Traffic Control in Connected/Automated Cities

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