Exploring Crowdsourced Monitoring Data for Safety

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dc.contributor.authorTurner, Shawn M.en
dc.contributor.authorMartin, Michael W.en
dc.contributor.authorGriffin, Greg P.en
dc.contributor.authorLe, Minhen
dc.contributor.authorDas, Subasishen
dc.contributor.authorWang, Ruihongen
dc.contributor.authorDadashova, Baharen
dc.contributor.authorLi, Xiaoen
dc.date.accessioned2020-06-09T14:36:23Zen
dc.date.available2020-06-09T14:36:23Zen
dc.date.issued2020-03en
dc.description.abstractThis project included four distinct but related exploratory studies of data sources that could improve roadway safety analysis. The first effort evaluated passively gathered crowdsourced bicyclist activity data from StreetLight Data and found promising correlations (R2 of 62% and 69% for monthly weekday and weekend daily averages) when the StreetLight data were compared to bicyclist counts from 32 locations in eight Texas cities, and even better correlation (R2 of 94%) when compared with countywide Strava data expanded to represent total bicycling activity. The second effort evaluated the pedestrian counting accuracy of the Miovision system and found 15% error for daytime and 24% error for nighttime conditions. The third effort used INRIX trip trace data to determine origin-destination patterns and developed 40 decision rules to define the origin-destination patterns. The fourth effort analyzed crowdsourced Waze data (i.e., traffic incidents) and found it to be a reliable alternative to observed and predicted crashes, with the ability to identify high-risk locations: 77% of high-risk locations identified from police-reported crashes were also identified as high-risk in Waze data. The researchers propose a method to treat the redundant Waze reports and to match the unique Waze incidents with police crash reports.en
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/10919/98795en
dc.language.isoenen
dc.publisherSAFE-D: Safety Through Disruption National University Transportation Centeren
dc.relation.ispartofseriesSAFE-D;TTI-Student-05en
dc.rightsCC0 1.0 Universalen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.subjectbicyclist countsen
dc.subjectpassive crowdsourceden
dc.subjectpedestrian crossingen
dc.subjectmassive GPS dataen
dc.subjectorigin-destinationen
dc.subjectcrowdsourced incident dataen
dc.titleExploring Crowdsourced Monitoring Data for Safetyen
dc.typeReporten
dc.type.dcmitypeTexten

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