Research map / Probabilistic and scientific modeling / Dynamical systems
Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| parallelize LDS learning and anomaly criteria on Apache Spark instead of a single machine | RSLDS pipeline · 2,534 links, one year data | processing time: 1,689 hours -> less than a day | Helped |
Closest papers by meaning
- TraffiDent: A Dataset for Understanding the Interplay Between Traffic Dynamics and Incidents (NeurIPS 2025 · similarity 0.86)
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