Quantifying Uncertainty and Distributed Adaptive Control for Unanticipated Traffic Patterns as a Result of Major Natural and Man-made Disruptions

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.

Dual Rebalancing Strategies for Electric Vehicle Carsharing Operations

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.