MATraM is a new agent-based transport model that couples adaptive activity scheduling with mobility simulation to generate emergent patterns from dynamic behavior changes.
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Mt-KaRRi is a highly scalable ride-pooling dispatcher that achieves millisecond-scale response times for millions of requests, supporting unprecedented-scale urban simulations.
A higher-order network comparison of real Ile-de-France mobility traces against a synthetic simulator finds the simulator promising yet limited on path-level statistics.
Dynamic traffic emissions from an online-calibrated SUMO model improve hyperlocal NO2 hotspot predictions and peak representation over static baselines when coupled to the CAIRDIO dispersion model.
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.
Vehicle overacceleration is the fundamental mechanism governing traffic breakdown, separable from overdeceleration effects in microscopic models for both human and automated traffic.
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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.