Danjue Chen

Assistant Professor
University of Massachusetts Lowell

Featured Projects

Research and Background

Dr. Chen's research interests include: (1) modeling and control of connected and automated vehicles, (2) traffic flow theory & simulation, (3) truck freight, and (4) traffic-environment interaction. Her research aims to (1) better understand the fundamental nature of traffic flow, particularly with cutting-edge vehicle technologies such as connected vehicles and autonomous vehicles, (2) understand the interconnection among transportation, energy, and environment emission, and (3) develop simple but innovative control strategies to promote smart and eco transportation. One example of her recent work, “Towards Vehicle Automation: Roadway Capacity Formulation for Traffic Mixed with Regular and Automated Vehicles”, studied the dynamic roadway capacity when the traffic stream has both conventional vehicles and automated vehicles. Specifically, the study provides formulations of traffic operational capacity in mixed traffic. The capacity formulations take into account (1) AV penetration rate, (2) micro/mesoscopic characteristics of regular and automated vehicles (e.g., platoon size, spacing characteristics), and (3) different lane policies to accommodate AVs such as exclusive AV and/or RV lanes and mixed-use lanes. A general formulation is developed to determine the valid domains of different lane policies and more generally, AV distributions across lanes with respect to demand, as well as optimal solutions to accommodate AVs. Another recent study utilized connected vehicles (CV) to develop innovative traffic control to improve highway flow and reduce system delay (“Variable Speed Limit Control at Fixed Freeway Bottlenecks Using Connected Vehicles”). Specifically, CV technology is applied to develop VSL strategies to improve bottleneck discharge rates and reduce system delays. Three VSL control strategies are developed with different levels of complexity and capabilities to enhance traffic stability using: (i) only one CV (per lane) (Strategy 1), (ii) one CV (per lane) coupled with variable message signs (Strategy 2), and (iii) multiple CVs (Strategy 3). We further develop adaptive schemes for the three strategies to remedy potential control failures in real time. These strategies are designed to accommodate different queue detection schemes (by CVs or different sensors) and CV penetration rates. Finally, probability of control failure is formulated for each strategy based on the stochastic features of traffic instability to develop a general framework to (i) estimate expected delay savings, (ii) assess the stability of different VSL control strategies, and (iii) determine optimal control speeds under uncertainty.