A Self-Adaptive Collaborative Multi-Agent based Traffic Signal Timing System

Torabi, Behnam; Wenkstern, Rym Z.; Saylor, Robert

doi:10.1109/isc2.2018.8656659

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…. In practice, the intersection signal controller is equipped with computation unit that can control the traffic light signal[54].3. In this paper, we assume that vehicles arrive their destination at the exit link, thus the vehicles on the exit link can be omitted…”

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confidence: 99%

School of Teacher, Nanjing Xiao Zhuang University, Nanjing, 210000, China

Wang¹

2017

Advances in Psychological Science

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The nuclide 14 C can be produced in the atmosphere by high energy particles and γ-rays from high-energy phenomena. Through the carbon cycle, some of 14 CO 2 produced in the atmosphere can be retained in annual tree rings 1, 2 . Four events of rapid increase of the 14 C content occurred in AD 775, AD 994, BC 660 and BC 3371 were found 3-6 . Recently, the data of Jull et al. (2020) was inconsistent with our records around BC 3371. We measured our sample again and found the 14 C records are consistent with the value in Wang et al. (2017). Therefore, our 14 C records are robust. The inconsistency may be caused by the difference of calendar ages for the wood samples, or the physical origin of the event. First, crossdating on ring width can be performed only between trees whose growth has the same environmental conditions. Because the master tree-ring for dendrochronology is lack for Chinese trees. The master tree-ring from California has to be used. Therefore, the calendar ages derived from dendrochronology may be not precise. Second, the 14 C even may be not global. One evidence

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confidence: 99%

School of Teacher, Nanjing Xiao Zhuang University, Nanjing, 210000, China

Wang¹

2017

Advances in Psychological Science

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“…Depending on the size of the prediction model and the number of possible combinations of alternatives that can be taken, a challenge for these approaches is that the computation time for prediction, optimization and control could be long. Methods based on reinforcement learning (Torabi, Wenkstern, & Saylor, 2018) have the ability to flexibly respond to such environmental changes, but they need long time for learning.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive System for a Real-Time Matching Application

Matsutsuka

Ogawa

Toriyama

et al. 2022

International Journal of Web Services Research

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In order to enhance the customer experience, it is important not only to provide functions, but also to respond to changes in environments and requirements. It is a difficult task to evaluate and manage which function with different locations and contents is most valuable to the user's experience without using computationally time-consuming optimization calculations. To address this, this paper is focusing on self-adaptive software technology. The authors built a software adaptation mechanism that can be immediately calculated online using a performance characteristic map and threshold judgment with a learning function and sequential updates. The results confirmed the effectiveness of the mechanism in an application that supports personnel exchange events.

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“…• Using real traffic data, realistic networks, and large-scale networks, and considering traffic heterogeneity: Real traffic are highly dynamic over time and realistic traffic network environment demands more challenges and concerns in dealing with applying RL rather than simple hypothetical setups using traffic simulations. Although there are a few research papers which recently focused on this challenge (Wei et al, 2018;Torabi et al, 2018a), there are still several challenges in dealing with the application of RL in the real-world setup. Considering traffic heterogeneity in traffic control models can also be of interest to research (Nuli and Mathew, 2013).…”

Section: Key Findingsmentioning

confidence: 99%

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

Noaeen¹,

Naik²,

Goodman³

et al. 2021

Preprint

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Improvement of traffic signal control (TSC) efficiency has been found to lead to improved urban transportation and enhanced quality of life. Recently, the use of reinforcement learning (RL) in various areas of TSC has gained significant traction; thus, we conducted a systematic literature review as a systematic, comprehensive, and reproducible review to dissect all the existing research that applied RL in the network-level TSC (NTSC) domain. The review only targeted the network-level articles that tested the proposed methods in networks with two or more intersections. We used natural language processing to define the search strings and searched Google Scholar, Web of Science, IEEE Xplore, ACM Digital Library, Springer Link, and Science Direct databases. This review covers 160 peer-reviewed articles from 30 countries published from 1994 to March 2020. The goal of this study is to provide the research community with statistical and conceptual knowledge, summarize existence evidence, characterize RL applications in NTSC domains, explore all applied methods and major first events in the defined scope, and identify areas for further research based on the explored research problems in current research.

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A Self-Adaptive Collaborative Multi-Agent based Traffic Signal Timing System

Cited by 6 publications

References 13 publications

School of Teacher, Nanjing Xiao Zhuang University, Nanjing, 210000, China

School of Teacher, Nanjing Xiao Zhuang University, Nanjing, 210000, China

An Adaptive System for a Real-Time Matching Application

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

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