Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the...

Short-Term Traffic Flow Prediction Based on the Efficient Hinging Hyperplanes Neural Network

Traffic flow (TF) prediction is an important and yet a challenging task in transportation systems, since the TF involves high nonlinearities and is affected by many elements. Recently, neural networks have attracted much attention for TF prediction, but they are commonly black boxes with complex architectures and difficult to be interpreted,...

A General Hierarchical Control System to Model ACC Systems: An Empirical Study

Urged by a close future perspective of a traffic flow made of a mix of human-driven vehicles and automated vehicles (AVs), research has recently focused on studying the traffic flow characteristics of Adaptive Cruise Controls (ACCs), the most typical AV. However, in most works, the ACC system is studied under a simplifying and unrealistic...

A Flow Graph-Based Scalable Critical Branch Identification Approach for AC State Estimation Under Load Redistribution Attacks

This article offers a novel perspective on identifying the critical branches under load redistribution (LR) attacks. Compared to the existing literature that is largely disruption-driven and based on dc state estimation, we propose to address the threat from LR attacks on a more fundamental level by modeling and analyzing the circulation of...