Adaptive Transit Signal Priority Control for Traffic Safety and Efficiency Optimization: A Multi-Objective Deep Reinforcement Learning Framework

This study introduces a multi-objective deep reinforcement learning (DRL)-based adaptive transit signal priority control framework designed to enhance safety and efficiency in mixed-autonomy traffic environments. The framework utilizes real-time data from connected and automated vehicles (CAVs) to d...

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Detalles Bibliográficos

Principais autores:	Yuxuan Dong, Helai Huang, Gongquan Zhang, Jieling Jin
Formato:	Artigo
Idioma:	Inglês
Publicado:	MDPI AG 2024-12-01
Series:	Mathematics
Assuntos:	transit signal priority deep reinforcement learning multi-objective optimization traffic safety and efficiency mixed-autonomy traffic
Acceso en liña:	https://www.mdpi.com/2227-7390/12/24/3994
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