Mathematical analysis based on the Macroscopic Fundamental Diagram proves road transportation networks are fragile, with a skewness indicator for cross-network comparison and simulations showing stochastic reinforcement.
Transportation Science 57, 1115–1133
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative 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.
A value-theory model of AGI predicts that near-complete labor substitution drives living labor, surplus value, and the profit rate toward zero.
citing papers explorer
-
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.