@inproceedings {pub6334,
	title = {Understanding day-to-day traffic patterns during disruption: an incremental nonnegative matrix factorization approach},
	author = {Jingyan Li AND Sebastian Brulin AND Nithin Santhanam AND Sean Qian},
	year = {2026},
	month = {January},
	abstract = {This research investigates daily traffic pattern changes resulting from significant network disruptions using data-driven incremental Non-negative Matrix Factorization (NMF). Employing high-resolution traffic speed data collected from the I-95 corridor in Pittsburg, the study analyzes how spatial-temporal traffic features evolve during normal and disrupted states. By integrating multi-stage incremental learning with route-based and topological regularization methods, this work identifies stable and adaptive spatial features reflecting dynamic traffic behaviors under disruptions. Preliminary results demonstrate distinct temporal shifts in traffic peaks following disruptions, revealing key routes{\textquoteright} resilience and vulnerability. },
	publisher = {Transportation Research Board (TRB)},
	booktitle = {Transportation Research Board Annual Meeting},
	city = {Washington}
}