Urban traffic signal control with connected and automated vehicles: A survey
By Qiangqiang Guo, Li Li, Xuegang (Jeff) Ban. Transportation Research Part C: Emerging Technologies, 2019
432 citations recorded by the source corpus.
Abstract Inefficient traffic control is pervasive in modern urban areas, which would exaggerate traffic congestion as well as deteriorate mobility, fuel economy and safety. In this paper, we systematically review the potential solutions that take advantage of connected and automated vehicles (CAVs) to improve the control performances of urban signalized intersections. We review the methods and models to estimate traffic flow states and optimize traffic signal timing plans based on CAVs. We summarize six types of CAV-based traffic control methods and propose a conceptual mathematical framework that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs. The benefits and drawbacks of various CAV-based control methods are explained, and future research directions are discussed. We hope that this review could provide readers with a helpful roadmap for future research on CAV-based urban traffic control and draw their attention to the most challenging problems in this important and promising field.
Subfields: Building and Construction, Control and Systems Engineering, Transportation
View Paper: https://www.semanticscholar.org/paper/cd0232590570c784ef2010c748bc77375008c77b
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Citations in the corpus
- 5G-Advanced Toward 6G: Past, Present, and Future (2023, background): “…used for in-vehicle replication of road signaling (e.g., traffic light information) and to support autonomous driving by providing vehicles with information generated by road infrastructure entities or traffic controllers [164], [165], as well as for dynamic map download and update [166].”