Dr. Zhou and Dr. Ozbay found that, if the traffic flow parameters are time-varying and/or the knowledge of these parameters are biased, the performances of a traffic state estimator that has assumed them to be known and fixed-valued can be significantly downgraded. Moreover, only augmenting these parameters into the state vector and then resorting to nonlinear recursive estimation techniques such as extended Kalman filter (EKF) cannot solve the issue. This is because, under a CTM-based traffic estimator, the critical density is unobservable under free-flow conditions, and hence biased initial knowledge of the critical density can cause false switching of the working model of the estimator and distort the estimation afterward.
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).
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.
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.
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