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dc.contributor.advisorChairperson, Graduate Committee: Yiyi Wangen
dc.contributor.authorSharda, Shivamen
dc.date.accessioned2017-05-02T19:56:24Z
dc.date.available2017-05-02T19:56:24Z
dc.date.issued2016en
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/12383en
dc.description.abstractThe goal of this study is to develop risk maps that predict zones that have an increased risk of traffic crashes for utility service trucks of the electric power industry. This study employed a national dataset that contains video logs of driving events from 10,009 utility truck drivers from 2010 to 2014. The study consists of four steps. Step 1 focused on finding whether certain driver behaviors (e.g., traffic violation, distraction, etc.) cluster in the same location as crashes and, therefore, suggest behaviors that are predictive of crashes. The study used Getis-Ord Gi* hot-spot analysis to reveal the clustering pattern within a standard unit of area (at the grid cell level: 1,640 feet by 1,640 feet). The finding of this research indicated that four behaviors ('risky behaviors') consistently cluster with the collision outcomes: distraction, lack of awareness, following too close, and eating/drinking. In Step 2, negative binomial models were used to relate the occurrence of the risky behaviors to a host of geospatial variables (e.g., land use, traffic, and socio-economics) while controlling for the exposure at the grid level (200 feet by 200 feet, roughly the size of a street block). Step 2 was implemented on the three datasets that were assembled based on different levels of availability of the geospatial features. Results indicated that well-balanced land use, road network density, lane-mile density of secondary and primary roads (if urban areas), and high concentration of elderly people (65 years and above) contributed to the prevalence of risky behaviors. Residential neighborhood, local road (if rural area), and average household size were shown to dampen incidence of risky behaviors. Step 3 developed the scoring systems to estimate the overall risk of each risky behavior for a given location (grid). Finally, Step 4 developed risk maps on a 2-D scale to delineate locations into different levels of hazards. In sum, this study confirmed the linkage between driver behavior and collisions and proposed a new way to anticipate crashes. While the test dataset pertains to utility service trucks, the methods can be adapted for predicting locations where the risk of future crashes is higher.en
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Engineeringen
dc.subject.lcshAutomobile drivingen
dc.subject.lcshTraffic accidentsen
dc.subject.lcshRegression analysisen
dc.titleMapping risky driver behavior and identifying their contributory factors : a spatial statistical approachen
dc.typeThesisen
dc.rights.holderCopyright 2016 by Shivam Shardaen
thesis.degree.committeemembersMembers, Graduate Committee: Nicholas Ward; Ahmed Al-Kaisy.en
thesis.degree.departmentCivil Engineering.en
thesis.degree.genreThesisen
thesis.degree.nameMSen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage144en
mus.data.thumbpage110en


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