{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T12:07:52Z","timestamp":1790251672518,"version":"4.1.0"},"reference-count":26,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,11]],"date-time":"2020-01-11T00:00:00Z","timestamp":1578700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Transportation agencies cannot afford to scale existing methods of roadway and railway condition monitoring to more frequently detect, localize, and fix anomalies throughout networks. Consequently, anomalies such as potholes and cracks develop between maintenance cycles and cause severe vehicle damage and safety issues. The need for a lower-cost and more-scalable solution spurred the idea of using sensors on board vehicles for a continuous and network-wide monitoring approach. However, the timing of the full adoption of connected vehicles is uncertain. Therefore, researchers used smartphones to evaluate a variety of methods to implement the application using regular vehicles. However, the poor accuracy of standard positioning services with low-cost geospatial positioning system (GPS) receivers presents a significant challenge. The experiments conducted in this research found that the error spread can exceed 32 m, and the mean localization error can exceed 27 m at highway speeds. Such large errors can make the application impractical for widespread use. This work used statistical techniques to inform a model that can provide more accurate localization. The proposed method can achieve sub-meter accuracy from participatory vehicle sensors by knowing only the mean GPS update rate, the mean traversal speed, and the mean latency of tagging accelerometer samples with GPS coordinates.<\/jats:p>","DOI":"10.3390\/s20020409","type":"journal-article","created":{"date-parts":[[2020,1,13]],"date-time":"2020-01-13T04:05:51Z","timestamp":1578888351000},"page":"409","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Accuracy Enhancement of Anomaly Localization with Participatory Sensing Vehicles"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3743-6652","authenticated-orcid":false,"given":"Raj","family":"Bridgelall","sequence":"first","affiliation":[{"name":"Department of Transportation, Logistics, &amp; Finance, North Dakota State University, Department 2880 P.O. Box 6050, Fargo, ND 58101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Denver","family":"Tolliver","sequence":"additional","affiliation":[{"name":"Upper Great Plains Transportation Institute, North Dakota State University, Department 2880 P.O. Box 6050, Fargo, ND 58101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ragnoli, A., Blasiis, M.R.D., and Benedetto, A.D. (2018). Pavement distress detection methods: A review. Infrastructures, 3.","DOI":"10.20944\/preprints201809.0567.v1"},{"key":"ref_2","unstructured":"Pierce, L.M., and Weitzel, N.D. (2019). Automated Pavement Condition Surveys, Academies Press."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Karamihas, S.M., Gilbert, M.E., Barnes, M.A., and Perera, R.W. (2019). 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