A statistical inference framework for day-to-day traffic dynamics models allows identifiability, consistency proofs, and parameter estimation from trajectory data, with extensions for heterogeneity and privacy, validated on simulations and Ann Arbor data.
Transportation Research Part B: Methodological , volume=
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.OC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Statistical Inference of Day-to-Day Traffic Dynamics
A statistical inference framework for day-to-day traffic dynamics models allows identifiability, consistency proofs, and parameter estimation from trajectory data, with extensions for heterogeneity and privacy, validated on simulations and Ann Arbor data.