Ultra-fast Traffic Nowcasting and Control via Differentiable Agent-based Simulation
Traffic digital twins, which inform policymakers of effective interventions based on large-scale, high-fidelity computational models calibrated to real-world traffic, hold promise for addressing societal challenges in our rapidly urbanizing world. However, conventional fine-grained traffic simulations are non-differentiable and typically rely on inefficient gradient-free optimization, making calibration for real-world applications computationally infeasible. Here we present a differentiable agent-based traffic simulator that enables ultra-fast model calibration, traffic nowcasting, and control on large-scale networks. We develop several differentiable computing techniques for simulating i...
| Authors | Fumiyasu Makinoshima, Yuya Yamaguchi, Eigo Segawa et al. |
|---|---|
| Primary category | cs.MA |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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