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Henry X. Liu

Researcher at University of Michigan

Publications -  241
Citations -  8547

Henry X. Liu is an academic researcher from University of Michigan. The author has contributed to research in topics: Traffic flow & Traffic simulation. The author has an hindex of 46, co-authored 226 publications receiving 6515 citations. Previous affiliations of Henry X. Liu include University of California, Irvine & University of Minnesota.

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Real-time queue length estimation for congested signalized intersections

TL;DR: This paper solves the problem of measuring intersection queue length by exploiting the queue discharge process in the immediate past cycle by using high-resolution "event-based" traffic signal data and applying Lighthill-Whitham-Richards shockwave theory.
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String stability for vehicular platoon control: Definitions and analysis methods

TL;DR: This paper aims to clarify the relationship of ambiguous definitions and various analysis methods, providing a rigorous foundation for future studies on platoon control, and provides insights for practical selection of analyzing methods for vehicle platoons.
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Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections

TL;DR: A mixed integer linear programming (MILP) model is presented to optimize vehicle trajectories and traffic signals in a unified framework at isolated signalized intersections in a CAV environment to validate the advantages of the proposed control method over vehicle-actuated control in terms of intersection capacity, vehicle delays, and CO2 emissions.
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Optimal vehicle speed trajectory on a signalized arterial with consideration of queue

TL;DR: In this paper, a multi-stage optimal control formulation is proposed to obtain the optimal vehicle trajectory on signalized arterials, where both vehicle queue and traffic light status are considered, and a constrained optimization model is proposed as an approximation approach, which can be solved much quicker.
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Use of Local Linear Regression Model for Short-Term Traffic Forecasting

TL;DR: A proposed local linear regression model was applied to short-term traffic prediction and consistently showed better performance than the k-nearest neighbor and kernel smoothing methods.