Analysis and Prediction of Spatiotemporal Impact of Traffic Incidents for Better Mobility and Safety in Transportation Systems, Research Project
Analysis and Prediction of Spatiotemporal Impact of Traffic Incidents for Better Mobility and Safety in Transportation Systems, Research Project
- Neue Suche nach: C. Shahabi
- Neue Suche nach: U. Demiryurek
- Neue Suche nach: C. Shahabi
- Neue Suche nach: U. Demiryurek
The goal of this research is to develop a machine learning framework to predict the spatiotemporal impact of traffic accidents on the upstream traffic and surrounding region. We propose a Latent Space Model for Road Networks (LSM-RN), which enables more accurate and scalable traffic prediction by utilizing both topology similarity and temporal correlations. Our framework further enables real-time traffic prediction by 1) exploiting real-time sensor readings to adjust/update the latent spaces, and 2) training as data arrives and predicting on-the-fly.
Dokumentinformationen
Analysis and Prediction of Spatiotemporal Impact of Traffic Incidents for Better Mobility and Safety in Transportation Systems, Research Project
2015
2 pages
Report
Keine Angabe
Englisch
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