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This study audits ultra-short-term road-segment speed forecasting under sparse mobile sensing using a mask-consistent support-chain framework. A three-day GPS dataset is aggregated into 5 min speed observations over 1970 road segments and used as a controlled sparse-sensing case study rather than a general-purpose long-term forecasting benchmark. The evaluation protocol distinguishes the full test grid, the set of directly observed target speeds, model-valid prediction support, strict complete-history support, and common-support subsets for coverage-limited baselines. The directly observed target set is used as the primary relaxed support because it retains all verifiable ground-truth targets, while strict and common-support subsets are reported as sensitivity checks. Under this support-conditioned evaluation, the adaptive graph convolutional recurrent network (AGCRN) is associated with lower mean absolute error (MAE) among full-coverage models, the historical mean (HIST_MEAN) baseline is associated with lower root mean squared error (RMSE), and congestion recall remains below 0.24 for all full-coverage deep models. These complementary results indicate conditional and metric-dependent strengths rather than universal model superiority. Because the dataset covers only three consecutive days, weekday\/weekend variation, incident-specific fluctuations, seasonal effects, and spatial transferability cannot be fully examined and are treated as limitations. Overall, the findings show that evaluation support should be reported as a first-order experimental factor alongside model accuracy under sparse mobile probe sensing.<\/jats:p>","DOI":"10.3390\/s26134320","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:16:18Z","timestamp":1783527378000},"page":"4320","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Auditing Road-Segment Speed Forecasting Under Sparse Mobile Probe Sensing: A Mask-Consistent Support-Chain Analysis"],"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":"School of Instrument Science and Engineering, State Key Laboratory of Comprehensive PNT Network and Equipment Technology, 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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2211-3586","authenticated-orcid":false,"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"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4927","DOI":"10.1109\/TITS.2021.3054840","article-title":"Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions","volume":"23","author":"Yin","year":"2022","journal-title":"IEEE Trans. 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