arXiv:2509. 24725v4 Announce Type: replace-cross Abstract: Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management.
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The paper introduces a quantum-inspired representation of driver behavior that models drivers as evolving density matrices, capturing continuous, probabilistic, context-dependent, and history-dependent interactions among behavioral variables. Trained unsupervised on the I‑24 MOTION dataset, the framework identifies three interpretable driving regimes—free flow, transition, and congestion—and reproduces macroscopic traffic phenomena such as the fundamental diagram and hysteresis loops. The representation also enhances practical applications by providing context-dependent parameters for classical car‑following models and enabling autonomous vehicles to forecast nearby drivers’ motion in real time.
arXiv:2609.21945v1 Announce Type: new Abstract: Urban transportation networks present complex optimization challenges spanning calibration of high-fidelity simulators and real-time operational contro...
arXiv:2412.02520v4 Announce Type: replace-cross Abstract: Connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC) create new opportunities for highway congestion mitigation. T...
arXiv:2607. 03703v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control.
arXiv:2606. 07556v1 Announce Type: cross Abstract: Accurate measurement of traffic volumes and flows is vital for modern intelligent transportation.
RiskTraf introduces a risk-extrapolated residual learning approach for multi-variate traffic flow prediction, leveraging raw flow, speed, and occupancy data from the new PEMSB-3V benchmark. The method freezes a trained spatio-temporal backbone and adds a lightweight residual head that learns from historical speed and occupancy to correct flow predictions across different traffic regimes. Experiments show consistent improvements over various backbones and outperform existing debiasing and distribution-shift adaptation techniques.