A Taxonomy of Adaptive Traffic Signal Control
Research on adaptive traffic signal control (ATSC) extends back to at least the 1960s, and many ATSC methods have been proposed over the years. This paper provides a review of this research and proposes a taxonomy for organizing it, accompanied by a consistent vocabulary for discussing the control concepts. We begin from the well-established concept of control generations. Next, we classify the ATSC methods according to their topographic structure (local-only, system/hierarchical), time resolution of decision-making (continuous versus planning-horizon), type of decision (rule-based or optimization), objective function, cyclic/acyclic nature,and additional subcategories relevant to certain "families" of methods. These various elements of system control are organized into a taxonomy of ATSC to help future researchers understand the wide diversity of algorithmic approaches to the signal control problem that have been proposed to date, and which can be updated or expanded to incorporate future research.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingDiversityTraffic Signal ControlSimilar Papers 제목 키워드 기반
An Open-Source Framework for Adaptive Traffic Signal Control
Sub-optimal control policies in transportation systems negatively impact mobility, the environment and human health. Developing optimal transportation control systems at the appropriate scale can be difficult as cities' …
Open-Ended Question AnsweringReinforcement LearningTraffic Signal ControlDeep Reinforcement Learning for Adaptive Traffic Signal Control
Many existing traffic signal controllers are either simple adaptive controllers based on sensors placed around traffic intersections, or optimized by traffic engineers on a fixed schedule. Optimizing traffic controllers …
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Exploring the impact of traffic signal control and connected and automated vehicles on intersections safety: A deep reinforcement learning approach
In transportation networks, intersections pose significant risks of collisions due to conflicting movements of vehicles approaching from different directions. To address this issue, various tools can exert influence on t…
Deep Reinforcement LearningTraffic Signal ControlReinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
The growing demand for road use in urban areas has led to significant traffic congestion, posing challenges that are costly to mitigate through infrastructure expansion alone. As an alternative, optimizing existing traff…
reinforcement-learningReinforcement Learning (RL)Traffic Signal ControlA Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers
This article proposes a methodology for the development of adaptive traffic signal controllers using reinforcement learning. Our methodology addresses the lack of standardization in the literature that renders the compar…
Experimental Designreinforcement-learningReinforcement Learning (RL)