Identifies flaws in SUMO's mesoscopic model based on Eissfeldt (2004) and proposes a discrete-time link transmission model that follows LWR principles with explicit backward traveling spaces for better queue dynamics.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Multi-agent DRL framework shows dynamic incentives and pricing can cut commuter costs ~20%, emissions ~10%, and double public transport profit in simulated morning peak scenarios.
citing papers explorer
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Revisiting mesoscopic traffic flow simulation in SUMO: Limitations, analysis, and an alternative
Identifies flaws in SUMO's mesoscopic model based on Eissfeldt (2004) and proposes a discrete-time link transmission model that follows LWR principles with explicit backward traveling spaces for better queue dynamics.
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Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks
Multi-agent DRL framework shows dynamic incentives and pricing can cut commuter costs ~20%, emissions ~10%, and double public transport profit in simulated morning peak scenarios.