Abstract
Freeway on-ramp merges are major sources of congestion, causing significant economic and environmental costs. While Deep Reinforcement Learning (DRL) offers a promising solution for ramp metering, existing approaches rely primarily on aggregated macroscopic data. Connected vehicles (CVs) provide vehicle-level observations that can complement aggregate traffic measurements, but their limited penetration produces incomplete microscopic information. This paper proposes a hybrid observation representation combining macroscopic traffic measurements with a two-channel grid encoding observed CV presence and speed. A Dueling Double Deep Q-Network processes these inputs to select ramp-metering green durations. The controller is trained under varying traffic demands and CV penetration rates and evaluated against ALINEA and macroscopic-only DRL variants in SUMO. Across 50 matched evaluation scenarios, the hybrid controller under partial CV visibility reduces the reported total travel time by 11.4 % and mean spillback duration by 84.9 % relative to ALINEA. Evaluating the same trained policy with full CV visibility yields a further travel-time reduction of approximately 1.6 %. Analysis across penetration rates suggests that the performance gap decreases as microscopic observations become more complete. These results support the use of complementary macroscopic and sparse microscopic observations for learning-based ramp metering. The source code implementation of the model is available at: https://github.com/youcefMehamlia/Multimodal-DRL-RMC
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Publication details
- Journal
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- Open access
- Green open access
Cite this article
APA 7
Mehamlia, Y., Farhi, N., & Bouali, M. (2026). Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments. https://omanscience.com/en/articles/ramp-metering-control-via-hybrid-state-deep-reinforcement-learning-in-partially-observable-connected-vehicle-environments
MLA 9
Mehamlia, Youcef, et al. "Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments." https://omanscience.com/en/articles/ramp-metering-control-via-hybrid-state-deep-reinforcement-learning-in-partially-observable-connected-vehicle-environments.
Chicago (author–date)
Mehamlia, Youcef, Nadir Farhi, and Meriem Bouali. 2026. "Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments." https://omanscience.com/en/articles/ramp-metering-control-via-hybrid-state-deep-reinforcement-learning-in-partially-observable-connected-vehicle-environments.
Harvard
Mehamlia, Y., Farhi, N. and Bouali, M. (2026) 'Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments', Available at: https://omanscience.com/en/articles/ramp-metering-control-via-hybrid-state-deep-reinforcement-learning-in-partially-observable-connected-vehicle-environments.
Vancouver
Mehamlia Y, Farhi N, Bouali M. Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments. https://omanscience.com/en/articles/ramp-metering-control-via-hybrid-state-deep-reinforcement-learning-in-partially-observable-connected-vehicle-environments
IEEE
Y. Mehamlia, N. Farhi, and M. Bouali, "Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments," https://omanscience.com/en/articles/ramp-metering-control-via-hybrid-state-deep-reinforcement-learning-in-partially-observable-connected-vehicle-environments.