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dc.rights.licenseCC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.
dc.contributorWang, Xiaokun (Cara)
dc.contributor.authorMarquis, Robyn
dc.date.accessioned2021-11-03T07:57:49Z
dc.date.available2021-11-03T07:57:49Z
dc.date.created2013-09-09T14:11:09Z
dc.date.issued2013-05
dc.identifier.urihttps://hdl.handle.net/20.500.13015/830
dc.descriptionMay 2013
dc.descriptionSchool of Engineering
dc.description.abstractThis report is a preliminary analysis into, through separate modeling approaches, the accident severity level and crash occurrences per Census tract, temporally divided into the four time blocks in the Best Practice Model: AM (6:00-10:00 AM), MD (10:00 AM-3:00 PM), PM (3:00-7:00 PM) and NT (7:00 PM-6:00 AM). An ordered probit model was used to predict the severity level of 2,630 unique, geo-located truck accidents, and it was found that a dry roadway surface, dark roads with street lights, and the times of 5:00-6:00 AM and 1:00-3:00 PM increased the odds of the crash being more severe. The hour 5:00-6:00 AM and increases in either the number of vehicles involved or the traffic flow predicted a lower severity. The crash occurrence models compared standard with zero-inflated negative binomial, and the latter was preferred for all time blocks except PM. An increase in population, vacancy rate, or hourly truck volume would all increase the expected number of crashes for all models, while certain industrial sectors and commuters who walked led to increases in only some of the models. The commuters who used transit was only significant in the MD zero-inflated, and decreased the expected occurrences. The next steps are to update the vehicle flows from an anticipated new data release, and then model occurrences by severity.
dc.description.abstractEach year in the United States, traffic accidents are one of the leading causes of fatalities, and also have injury costs in the billions of dollars. Research into these occurrences and the severity levels mostly considers passenger vehicles and highway segments. This research focuses on accidents in which at least one truck as involved, for a three-year period in Manhattan, New York. The author is part of a research team looking at congestion management techniques in this area, namely the adoption of an off-hour delivery program. This approach shifts truck traffic to the overnight hours from 7:00 PM-6:00 AM, and safety impacts are one of the major metrics in assessing this program's effectiveness.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectTransportation engineering
dc.titleInvestigating temporal effects on truck accident occurrences and severity levels in Manhattan
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid167000
dc.digitool.pid167001
dc.digitool.pid167002
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreeMS
dc.relation.departmentDept. of Civil and Environmental Engineering


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CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.
Except where otherwise noted, this item's license is described as CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.