Sliding Mode Network Perimeter Control

Abstract

Urban traffic congestion is a chronic problem faced by many cities in the US and worldwide. It results in inefficient infrastructure use as well as increased vehicle fuel consumption and emission levels. Congestion is intertwined with delay, as road users waste precious hours on the road, which in turn reduces productivity. Researchers have developed, and continue to develop, tools and systems to alleviate this problem. Network perimeter control is one such tool that has been studied extensively. It attempts to control the flow of vehicles entering a protected area to ensure that the congested regime predetermined by the Network Fundamental Diagram (NFD) is not reached. In this paper, an approach derived from sliding mode control theory is presented. Its main advantages over proportional-integral controllers include (1) minimal tuning, (2) no linearization of the governing equations, (3) no assumptions with regard to the shape of the NFD, and (4) ability to handle various demand profiles without the need to retune the controller. A sliding mode controller was implemented and tested on a congested grid network. The results show that the proposed controller produces network-wide delay savings and disperses congestion effectively.Index Terms-Network fundamental diagram, network perimeter control, sliding mode control, traffic signal control

Adaptive Traffic Signal Control: Game-Theoretic Decentralized vs. Centralized Perimeter Control

“…Numerous real-time perimeter controllers have been developed based on control theory, including: a standard proportional-integral (PI) controller [ 5 , 43 ], a robust PI controller [ 44 ], a sliding mode controller [ 45 ], a model predictive controller (MPC) [ 46 , 47 ], and a linear quadratic (LQ) controller [ 48 ]. The literature [ 5 , 49 , 50 ] shows that the PI perimeter controller is simple and efficient at reducing traffic congestion in an urban network.…”

Evaluating an Eco-Cooperative Automated Control System

“…The SPD-HARM controller regulates the flow of traffic approaching network bottlenecks. In earlier work, a robust SPD-HARM controller was developed that identifies bottlenecks dynamically and then controls the speed of CAVs to disperse congestion ( 3 , 4 ). The Eco-CACC-I system computes a fuel/energy-optimized speed profile both upstream and downstream of a signalized intersection.…”

“…The Eco-CAC system integrates four controllers: an eco-routing controller, a speed harmonization (SPD-HARM) controller, an Eco-Cooperative Adaptive Cruise Control at signalized intersections (Eco-CACC-I) controller, and an Eco-Cooperative Adaptive Cruise Control for uninterrupted flow (Eco-CACC-U) controller ( 1 – 12 ). Eco-routing is a technique that finds the most energy-efficient route.…”