The DOI matches its Crossref record, with a small difference in title or year; 1 of the 1 references it cites were found in a registry or an index.
Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto
Abstract
Population is steadily increasing worldwide, resulting in intractable traffic congestion in dense urban areas. Adaptive traffic signal control (ATSC) has shown strong potential to effectively alleviate urban traffic congestion by adjusting signal timing plans in real time in response to traffic fluctuations to achieve desirable objectives (e.g., minimize delay). Efficient and robust ATSC can be designed using a multiagent reinforcement learning (MARL) approach in which each controller (agent) is responsible for the control of traffic lights around a single traffic junction. Applying MARL approaches to the ATSC problem is associated with a few challenges as agents typically react to changes in the environment at the individual level, but the overall behavior of all agents may not be optimal. This paper presents the development and evaluation of a novel system of multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC). MARLIN-ATSC offers two possible modes: 1) independent mode, where each intersection controller works independently of other agents; and 2) integrated mode, where each controller coordinates signal control actions with neighboring intersections. MARLIN-ATSC is tested on a large-scale simulated network of 59 intersections in the lower downtown core of the City of Toronto, ON, Canada, for the morning rush hour. The results show unprecedented reduction in the average intersection delay ranging from 27% in mode 1 t…
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“MASs for transit use simple kinematic models instead of learned predictions.”
From Real-time bus arrival prediction and bus bunching prevention using a multi-agent LSTM approach (2026) · cites this paper as El-Tantawy et al., 2013
In the citing paper
“Cooperative holding using MAS has achieved more improvement in simulations than deterministic rules”
From Real-time bus arrival prediction and bus bunching prevention using a multi-agent LSTM approach (2026) · cites this paper as El-Tantawy et al., 2013
Citations
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Cited by
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498
“, RSUs, to realize adaptive traffic signal controls [222].”
BackgroundMulti-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control
Chu, T., Wang, J., Codeca, L., & Li, Z. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2901791 · Cited by 1,046
Transfer Learning in Deep Reinforcement Learning: A Survey
Zhu, Z., Lin, K., Jain, A. K., & Zhou, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3292075 · Cited by 861
Deep Reinforcement Learning: An Overview
Li, Y. (2017) · arXiv · doi:10.48550/arxiv.1701.07274 · Cited by 762
Multi-Agent Reinforcement Learning for Traffic Flow Management of Autonomous Vehicles
Mushtaq, A., Haq, I. U., Sarwar, M. A., Khan, A., Khalil, W., & Mughal, M. A. (2023) · Sensors · doi:10.3390/s23052373 · Cited by 61
Metadata
- Authors
- El-Tantawy, S., Abdulhai, B., & Abdelgawad, H.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2013-09
- Volume
- 14
- Issue
- 3
- Pages
- 1140-1150
- ISSN
- 1524-9050
- 1558-0016
- Cited by
- 458
- References
- 1
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- CoChat. (2026). Citation check: Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto. https://app.cochat.ai/papers/10.1109/tits.2013.2255286
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- El-Tantawy, S., Abdulhai, B., & Abdelgawad, H. (2013). Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2013.2255286
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