Mask- and Coverage-Aware Traffic-State Assessment Under Sparse Probe-Vehicle Sensing

Probe-vehicle sensing can extend traffic observation beyond fixed detectors, but its sparse and uneven coverage complicates the interpretation of predicted traffic states. This study develops a mask- and coverage-aware framework for regional traffic-state assessment from sparse probe-vehicle speed observations. Segment-level data from Xuzhou, China, were aggregated into 32 regions at 15 min resolution; only 0.157% of segment-time cells were observed. A regional target was observable whenever at least one segment in its region was observed, and the observed-target ratio across the controlled test conditions ranged from 12.1% to 23.9%. Nine controlled-missingness conditions were used to evaluate one-step speed prediction, followed by deterministic evidence-fusion assessment using predicted speed, observation masks, coverage history, and neighboring-region evidence. Reference labels for 130 stratified region-time cases were independently assigned and adjudicated by two annotators. Among the evaluated deterministic variants on this cohort, the speed-plus-mask rule achieved the strongest overall classification performance, with 69.2% accuracy and 63.7% Macro-F1. Because 69.2% equals the majority-class share of the cohort by construction (90 of 130 cases are low severity), the rule's advantage is its cross-class discrimination (Macro-F1) rather than overall accuracy alone. Adding coverage reduced the false-severe rate from 5.6% to 2.8%, but also reduced overall classification performance, demonstrating an over-escalation-control-classification trade-off. On a limited 16-case protocol, a fixed, schema-constrained large language model achieved 97.5% modal-label repeatability across five repetitions but only 32.0% Macro-F1; its limited and imbalanced support precludes any full-cohort effectiveness claim. The findings show that sensing support should be retained as traffic-state assessment evidence and reported alongside prediction and traffic-state alert performance. On the same 130 cases, a balanced RBF-SVM reached 75.0% mean grouped out-of-fold accuracy and 64.6% Macro-F1, but all four headline-metric paired intervals versus speed plus mask crossed zero and its false-severe rate was higher (9.7% versus 5.6%); no method was uniformly superior.