Framework for Connected and Automated Bus Rapid Transit with Sectionalized Speed Guidance based on deep reinforcement learning: Field test in Sejong City

Document Type

Journal Article

Publication Date


Subject Area

place - asia, place - urban, mode - bus rapid transit, technology - intelligent transport systems, technology - automatic vehicle monitoring, infrastructure


Cooperative intelligent transportation system, Connected and automated vehicles, Bus rapid transit speed guidance, Deep reinforcement learning


Nowadays, Automated Vehicle (AV) technology is gaining attention as a candidate to improve the efficiency of Bus Rapid Transit (BRT) systems. However, there are still some challenges in AV technology including limited perception range and lack of cooperation capability in mixed traffic situations with drivers. The emerging Connected and Automated Vehicles (CAVs) and Cooperative Intelligent Transportation System (C-ITS) offer an unprecedented opportunity to solve such challenges. As a result, this study presents a framework for Connected and Automated BRT (CA-BRT), including a cloud-based architecture and a deep reinforcement learning system for Sectionalized Speed Guidance (SSG) system designed for CAVs. The proposed framework is field-tested in Sejong City in South Korea, where there are various road environments such as bus stops, overpasses, underground tunnels, intersections, and crosswalks. The driving performance of the proposed system is compared with different types of control scenarios, and the results from the field tests show that the proposed system improves the driving performance of the AVs in various aspects including driving safety, ride comfort, and energy efficiency with downstream information obtained from road infrastructures.


Permission to publish the abstract has been given by Elsevier, copyright remains with them.


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