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 framework for transportation and land use integration as a parallel constrained multiple discrete-continuous extreme value (PC-MDCEV) home production model , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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Goal pursuit theory is presented as an illustrative behavioral framework that models multiple goals in travel decisions across activity scheduling, vehicle ownership, and location choice applications.
citing papers explorer
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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.
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Why Do We Need Travel Behavior Theory in the Age of AI? Multiple Goal Pursuit as an Illustrative Theory
Goal pursuit theory is presented as an illustrative behavioral framework that models multiple goals in travel decisions across activity scheduling, vehicle ownership, and location choice applications.