Adaptability and sustainability of machine learning approaches to traffic signal control

This study investigates how adaptable Machine Learning Traffic Signal control methods are to topological variability. We ask how well can these methods generalize to non-Manhattan-like networks with non-uniform distances between intersections. A Machine Learning method that is highly reliable in var...

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Bibliographic Details
Published in:Scientific reports 2022-10, Vol.12 (1), p.16681-16681, Article 16681
Main Author: Korecki, Marcin
Format: Article
Language:English
Subjects:
639/705/1042
639/705/117
Adaptability
Computer applications
Efficiency
Environmental impact
Humanities and Social Sciences
Learning algorithms
Machine learning
multidisciplinary
Optimization techniques
Science
Science (multidisciplinary)
Smart cities
Sustainability
Traffic congestion
Traffic control
Traffic flow
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Summary:This study investigates how adaptable Machine Learning Traffic Signal control methods are to topological variability. We ask how well can these methods generalize to non-Manhattan-like networks with non-uniform distances between intersections. A Machine Learning method that is highly reliable in various topologies is proposed and compared with state-of-the-art alternatives. Lastly, we analyze the sustainability of different traffic signal control methods based on computational efforts required to achieve convergence and perform training and testing. We show that our method achieves an approximately seven-fold improvement in terms of CO 2 emitted in training over the second-best method.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-21125-3