arXiv (All)
open access
A Dynamic Fusion Large Language Model for Traffic Flow Prediction
Traffic flow prediction is a core supporting technology for intelligent transportation systems. It uses historical data to infer future traffic dynamics in specific areas, thereby helping to alleviate congestion and improve resource allocation efficiency. Traditional neural networks struggle to brea
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Dual-Channel Dynamic Gated Spatio-Temporal Graph for Traffic Flow Forecasting#20336
- Event-Driven Language Models with Sparse Neural Activity for Neuromorphic Hardware#799641
- Time-LLM: Time Series Forecasting by Reprogramming Large Language Models#16584
- A Multimodal Label Forecasting Method for Aperiodic Visuo-Motor Time Series#985337
- SpikeOPD: Stable On-Policy Distillation for Autoregressive Spiking Language Models#781142
Record · ID 998851
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive