arXiv Machine Learning July 20, 2026 By Pengnan Chi, Xiaoliang Ma, Magnus Jansson, Magnus Nordenvaad

A Semiparametric Framework for Stochastic Fundamental Diagram Modeling

Read the original on arXiv Machine Learning →

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.

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Unveiling Stochasticity: Universal Multi-modal Probabilistic Modeling for Traffic Forecasting

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.

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An Empirical Markov Chain Car-Following (MC-CF) Model

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Structure-Guided Spatiotemporal Attention Graph Neural Network for Traffic Flow Prediction

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A Multi-Resolution Multi-Domain Pre-Training Framework for Universal Traffic Forecasting

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...

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Large-Scale OD Matrix Estimation with A Deep Learning Method

arXiv:2310. 05753v2 Announce Type: replace Abstract: The estimation of origin-destination (OD) matrices is a crucial aspect of Intelligent Transport Systems (ITS).

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Capacity-Aware Deep Learning for Generalizable Traffic Volume Estimation Across Links and Cities

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.

By L\'eo Hein, Giovanni De Nunzio, Aur\'elie Pirayre, Laurent Najman

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Unveiling Stochasticity: Universal Multi-modal Probabilistic Modeling for Traffic Forecasting

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