LQ Control of Traffic Flow Models via Variable Speed Limits

2024-03-04 · Brian Block, Stephanie Stockar

In this paper, an extension of a linear control design for hyperbolic linear partial differential equations is presented for a first-order traffic flow model. Starting from the Lighthill-Whitham-Richards (LWR) model, variable speed limit control (VSL) is applied through a modification of Greenshield's equilibrium flow model. Then, an optimal linear quadratic (LQ) controller is designed on the linear LWR model. The LQ state feedback function is found via the solution of a Riccati differential equation. Unlike previous studies, the control input is the rate of change of the input, not the input itself. The proposed controller is then verified on both the linear and nonlinear models. In both cases, the controller is able to drive the system to a desired density profile. In the nonlinear application, a higher control gain is needed to achieve similar results as in the linear case.

📄 PDF Abstract BibTeX arXiv:2403.02507

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

SPEED The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene…

Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic

2024-02-26 · Han Wang, Zhe Fu, Jonathan Lee, Hossein Nick Zinat Matin 외

This paper introduces a novel control framework for Lagrangian variable speed limits in hybrid traffic flow environments utilizing automated vehicles (AVs). The framework was validated using a fleet of 100 connected auto…

State Estimation

DVS-RG: Differential Variable Speed Limits Control using Deep Reinforcement Learning with Graph State Representation

2024-05-15 · Jingwen Yang, Ping Wang, Fatemeh Golpayegani, Shen Wang

Variable speed limit (VSL) control is an established yet challenging problem to improve freeway traffic mobility and alleviate bottlenecks by customizing speed limits at proper locations based on traffic conditions. Rece…

Deep Reinforcement Learning

MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits

2023-10-18 · Yuhang Zhang, Marcos Quinones-Grueiro, Zhiyao Zhang, Yanbing Wang 외

Variable Speed Limit (VSL) control acts as a promising highway traffic management strategy with worldwide deployment, which can enhance traffic safety by dynamically adjusting speed limits according to real-time traffic …

Decision Making Management Multi-agent Reinforcement Learning reinforcement-learning

Differential Variable Speed Limits Control for Freeway Recurrent Bottlenecks via Deep Reinforcement learning

2018-10-25 · Yuankai Wu, Huachun Tan, Bin Ran

Variable speed limits (VSL) control is a flexible way to improve traffic condition,increase safety and reduce emission. There is an emerging trend of using reinforcement learning technique for VSL control and recent stud…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

MARL-FWC: Optimal Coordination of Freeway Traffic Control Measures

2018-08-27 · Ahmed Fares, Walid Gomaa, Mohamed A. Khamis

The objective of this article is to optimize the overall traffic flow on freeways using multiple ramp metering controls plus its complementary Dynamic Speed Limits (DSLs). An optimal freeway operation can be reached when…

Multi-agent Reinforcement Learning Q-Learning Reinforcement Learning