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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\u2013time 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\u2013time 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\u2019s 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\u2013classification 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.<\/jats:p>","DOI":"10.3390\/s26196280","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T10:57:46Z","timestamp":1791197866000},"page":"6280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mask- and Coverage-Aware Traffic-State Assessment Under Sparse Probe-Vehicle Sensing"],"prefix":"10.3390","volume":"26","author":[{"given":"Dingxin","family":"Wu","sequence":"first","affiliation":[{"name":"Faculty of Transportation Engineering, Huai\u2019an University, Huai\u2019an 223001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Faculty of Computer & Software Engineering, Huai\u2019an University, Huai\u2019an 223001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Transportation Engineering, Huai\u2019an University, Huai\u2019an 223001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Comprehensive PNT Network and Equipment Technology, School of Instrument Science and Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong Ki","family":"An","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal 14300, Pulau Pinang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dewen","family":"Kong","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhicheng","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,3]]},"reference":[{"key":"ref_1","unstructured":"National Academies of Sciences, Engineering, and Medicine (2021). 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