C Chen, YP Huang, WHK Lam, TL Pan, SC Hsu… - … Research Part C …, 2022 - Elsevier
Existing data-driven and feedback traffic control strategies do not consider the heterogeneity of real-time data measurements. Besides, traditional reinforcement learning (RL) methods …
City-scale control of urban road traffic poses a challenging problem. Dynamical models based on the macroscopic fundamental diagram (MFD) enable development of model …
With unbalanced travel demand distribution over time and space, a stationary cordon location hinders the full potential of perimeter flow control based on network Macroscopic …
This paper presents a novel actor-critic deep reinforcement learning approach for metro train scheduling with circulation of limited rolling stock. The scheduling problem is modeled as a …
ZC Su, AHF Chow, CL Fang, EM Liang… - … Research Part B …, 2023 - Elsevier
This study proposes a hierarchical control framework to maximize the throughput of a road network driven by travel demand with uncertainties. In the upper level, a perimeter controller …
Perimeter control regulates the traffic flows between different regions of a road network by coordinating the signal timings at region boundaries with the aim of improving the overall …
D Zhou, VV Gayah - Transportation Research Part C: Emerging …, 2023 - Elsevier
Perimeter metering control based on macroscopic fundamental diagrams has attracted increasing research interests over the past decade. This strategy provides a convenient way …
S Gao, D Li, N Zheng, R Hu, Z She - Transportation Research Part B …, 2022 - Elsevier
Understanding the resilience of transportation networks has received considerable research attention. Nevertheless in the field of network traffic flow control, few control approaches …
D Zhou, VV Gayah - Transportation Research Part C: Emerging …, 2021 - Elsevier
Various perimeter metering control strategies have been proposed for urban traffic networks that rely on the existence of well-defined relationships between network productivity and …