Traffic estimation in unobserved network locations using data-driven macroscopic models. (arXiv:2401.17095v1 [cs.LG])

This paper leverages macroscopic models and multi-source spatiotemporal data
collected from automatic traffic counters and probe vehicles to accurately
estimate traffic flow and travel time in links where these measurements are
unavailable. This problem is critical in transportation planning applications
where the sensor coverage is low and the planned interventions have
network-wide impacts. The proposed model, named the Macroscopic Traffic
Estimator (MaTE), can perform network-wide estimations of traffic flow and
travel time only using the set of observed measurements of …

applications arxiv coverage cs.lg data data-driven flow locations low network paper planning probe sensor traffic transportation travel vehicles