{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:21:47Z","timestamp":1760242907678,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2016,10,3]],"date-time":"2016-10-03T00:00:00Z","timestamp":1475452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Road anomalies, such as cracks, pits and puddles, have generally been identi\ufb01ed by citizen reports made by e-mail or telephone; however, it is dif\ufb01cult for administrative entities to locate the anomaly for repair. An advanced smartphone-based solution that sends text and\/or image reports with location information is not a long-lasting solution, because it depends on people\u2019s active reporting. In this article, we show an opportunistic sensing-based system that uses a smartphone for road anomaly detection without any active user involvement. To detect road anomalies, we focus on pedestrians\u2019 avoidance behaviors, which are characterized by changing azimuth patterns. Three typical avoidance behaviors are de\ufb01ned, and random forest is chosen as the classi\ufb01er. Twenty-nine features are de\ufb01ned, in which features calculated by splitting a segment into the \ufb01rst half and the second half and considering the monotonicity of change were proven to be effective in recognition. Experiments were carried out under an ideal and controlled environment. Ten-fold cross-validation shows an average classi\ufb01cation performance with an F-measure of 0.89 for six activities. The proposed recognition method was proven to be robust against the size of obstacles, and the dependency on the storing position of a smartphone can be handled by an appropriate classi\ufb01er per storing position. Furthermore, an analysis implies that the classi\ufb01cation of data from an \u201cunknown\u201d person can be improved by taking into account the compatibility of a classi\ufb01er.<\/jats:p>","DOI":"10.3390\/ijgi5100182","type":"journal-article","created":{"date-parts":[[2016,10,3]],"date-time":"2016-10-03T10:17:01Z","timestamp":1475489821000},"page":"182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Smartphone-Based Pedestrian\u2019s Avoidance Behavior Recognition towards Opportunistic Road Anomaly Detection"],"prefix":"10.3390","volume":"5","author":[{"given":"Tsuyoshi","family":"Ishikawa","sequence":"first","affiliation":[{"name":"Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Naka-cho Koganei, 2-24-16 Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5294-2812","authenticated-orcid":false,"given":"Kaori","family":"Fujinami","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Naka-cho Koganei, 2-24-16 Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,10,3]]},"reference":[{"key":"ref_1","unstructured":"City of Chiba Chiba-Repo Field Trial: Review Report. 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