Data driven model free adaptive iterative learning perimeter control for large-scale urban road networks

Most perimeter control methods in literature are the model-based schemes designing the controller based on the available accurate macroscopic fundamental diagram (MFD) function with well known techniques of modern control methods. However, accurate modeling of the traffic flow system is hard and time-consuming. On the other hand, macroscopic traffic flow patterns show heavily similarity between days, and data from past days might enable improving the performance of the perimeter controller. Motivated by this observation, a model free adaptive iterative learning perimeter control (MFAILPC) scheme is proposed in this paper. The three features of this method are: (1) No dynamical model is required in the controller design by virtue of dynamic linearization data modeling technique, i.e., it is a data-driven method, (2) the perimeter controller performance will improve iteratively with the help of the repetitive operation pattern of the traffic system, (3) the learning gain is tuned adaptively along the iterative axis. The effectiveness of the proposed scheme is tested comparing with various control methods for a multi-region traffic network considering modeling errors, measurement noise, demand variations, and time-changing MFDs. Simulation results show that the proposed MFAILPC presents a great potential and is more resilient against errors than the standard perimeter control methods such as model predictive control, proportional-integral control, etc.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Nikolaos Geroliminis, Isik Ilber Sirmatel, Ye Ren
2020
Journal paper

Abstract

Official source

https://infoscience.epfl.ch/record/277940?ln=en

About this result

Related concepts (4)

Related publications (1)

, ,

Most perimeter control methods in literature are the model-based schemes. However, accurate modeling of the traffic flow system is hard and time-consuming. On the other hand, macroscopic traffic flow

2020

Traffic

Traffic comprises pedestrians, vehicles, ridden or herded animals, trains, and other conveyances that use public ways (roads) for travel and transportation. Traffic laws govern and regulate traffic, while rules of the road include traffic laws and informal rules that may have developed over time to facilitate the orderly and timely flow of traffic. Organized traffic generally has well-established priorities, lanes, right-of-way, and traffic control at intersections.

Traffic flow

In mathematics and transportation engineering, traffic flow is the study of interactions between travellers (including pedestrians, cyclists, drivers, and their vehicles) and infrastructure (including highways, signage, and traffic control devices), with the aim of understanding and developing an optimal transport network with efficient movement of traffic and minimal traffic congestion problems.

Learning

Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single event (e.g. being burned by a hot stove), but much skill and knowledge accumulate from repeated experiences. The changes induced by learning often last a lifetime, and it is hard to distinguish learned material that seems to be "lost" from that which cannot be retrieved.