arXiv:2607. 15907v1 Announce Type: new Abstract: The stochastic fundamental diagram (SFD) provides a probabilistic description of the relationship between traffic density and flow or speed, enabling uncertainty-aware traffic modeling.
arXiv:2604. 16084v2 Announce Type: replace-cross Abstract: Traffic forecasting is a challenging spatio-temporal modeling task and a critical component of urban transportation management.
The paper introduces the Markov Chain Car‑Following (MC‑CF) model, an empirical probabilistic approach that represents car‑following as a Markov process and samples accelerations from empirical distributions within discretized state bins. Evaluation on the Waymo Open Motion Dataset shows that MC‑CF variants outperform physics‑based baselines and compete with modern data‑driven methods in both one‑step and open‑loop trajectory prediction. Zero‑shot transfer to the Naturalistic Phoenix dataset and microscopic ring‑road simulations demonstrate cross‑domain generalization and scalability, with the model reducing collisions and reproducing naturalistic shockwave propagation.
arXiv:2608. 14177v1 Announce Type: cross Abstract: Deep spatiotemporal models integrating graph convolutions and attention mechanisms have demonstrated excellent performance in network-level traffic flow prediction, owing to their exceptional ability to capture complex spatiotemporal dependencies.
arXiv:2609.13878v1 Announce Type: new Abstract: Spatio-temporal traffic data are central to intelligent transportation systems, yet their heterogeneity poses significant challenges for large-scale mo...
arXiv:2310. 05753v2 Announce Type: replace Abstract: The estimation of origin-destination (OD) matrices is a crucial aspect of Intelligent Transport Systems (ITS).
arXiv:2607. 24056v1 Announce Type: cross Abstract: Network-wide traffic volume estimation typically relies on propagating measurements from fixed sensors, making performance highly dependent on sensor density and limiting deployment in sparsely instrumented networks.
arXiv:2602.14049v2 Announce Type: replace-cross Abstract: Spatio-temporal traffic forecasting is a core component of intelligent transportation systems, supporting various downstream tasks such as si...
The paper introduces COFM, a framework for consistent optimal transport flow matching that uses partially input convex neural networks (PICNN) to parameterize the transport potential. By adding a Hamilton‑Jacobi residual to the training objective, COFM enforces dynamical consistency and supports both one‑step transport and multi‑step ODE sampling without costly inner optimization. Experiments on benchmark datasets show that COFM achieves competitive performance while reducing L^2‑UVP by over 2× and cutting computational time by about 9× compared to state‑of‑the‑art models.
arXiv:2603. 11475v2 Announce Type: replace Abstract: Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions.
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.
arXiv:2606. 06272v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) are a framework for sampling structured objects via stochastic trajectories in a directed graph.
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.