Traffic Volume Estimation
Traffic-volume estimation framework combining floating car data, traffic cameras, and network-flow analysis.
This project estimates network-wide traffic volumes by combining floating car data, municipal traffic-camera observations, and traffic-flow modeling.
The public preprint frames the problem as a hybrid urban-sensing system: Cellular-transmission-model features, graph neural networks, topology-informed propagation, and ensemble square-root filtering are combined to estimate and forecast traffic volumes across a Manhattan road network.
- Role: Co-author
- Artifacts: Preprint on arXiv
- Keywords: Floating car data, traffic cameras, graph neural networks, network flow, data assimilation, Manhattan traffic, urban sensing
Link: arXiv.
