The paper introduces a spatio-temporally complementary feature propagation framework for estimating Annual Average Daily Traffic (AADT) across an entire urban network. It combines spatially sparse but temporally dense loop detector data with spatially complete but temporally sparse macroscopic transportation models, using a Poisson energy minimization algorithm on directed graphs with flow ratio matrices. Tested in Zurich, the method converges quickly and achieves a normalized mean absolute error below 10%, demonstrating its effectiveness and scalability.
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:2607. 24885v1 Announce Type: cross Abstract: Predicting traffic flow is crucial to optimizing transportation systems and improving urban mobility.
arXiv:2606. 09872v1 Announce Type: cross Abstract: Traffic forecasting is a fundamental component of intelligent transportation systems, yet remains challenging in real-world settings due to irregular sensor distributions and the high computational cost of modeling large-scale spatiotemporal dependencies.
arXiv:2606. 07695v1 Announce Type: cross Abstract: Multi-Modality Spatio-Temporal Forecasting (MoSTF) extends traditional spatio-temporal forecasting by incorporating diverse traffic modalities.
arXiv:2603.05581v2 Announce Type: replace-cross Abstract: Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobili...
arXiv:2608. 13993v1 Announce Type: new Abstract: Urban traffic management relies on sensor networks whose spatial coverage is limited by deployment costs and privacy regulations.
The paper introduces a spatio‑temporal traffic forecasting framework that fuses Graph Neural Networks with embeddings derived from a general‑purpose knowledge graph such as Wikidata. By creating semantic subgraphs around traffic sensors, the approach captures relationships like nearby points of interest, administrative hierarchies, and functional roles of locations, which are then integrated as additional adjacency matrices for the GNN. Experiments demonstrate that this external knowledge improves prediction accuracy beyond what street‑network data alone can achieve, while also offering a path toward better interpretability.
The paper introduces a spatio‑temporal traffic forecasting framework that fuses Graph Neural Networks with semantic knowledge from general-purpose knowledge graphs such as Wikidata. By generating embeddings that capture relationships like nearby points of interest, administrative hierarchies, and functional roles of locations, the framework creates additional adjacency matrices that enrich the sensor graph beyond physical connectivity. Experiments with established forecasting methods demonstrate that this external knowledge improves prediction accuracy and offers a path toward better interpretability.
The paper introduces Mobility Stream-Structure Synergy (MoSS), a method that fuses two complementary views of mobility data—an hourly inflow/outflow Sequence view and a Structure view derived from zigzag persistence diagrams—to capture temporal dynamics and evolving regional connectivity. MoSS employs a synergy module that extracts higher‑order representations from the co‑occurrence of these views, moving beyond additive fusion. Experiments on New York City and Chicago demonstrate that MoSS outperforms existing baselines on three downstream tasks using only mobility data.
arXiv:2507. 02961v2 Announce Type: replace-cross Abstract: Modern transportation network modeling increasingly involves the integration of diverse methodologies including sensor-based forecasting, reinforcement learning, classical flow optimization, and demand modeling that have traditionally been developed in isolation.
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:2607. 06614v1 Announce Type: cross Abstract: Accurate station-level demand forecasting is essential for the efficient operation of bike-sharing systems, yet it remains challenging due to complex spatio-temporal dependencies and the large scale of urban networks.