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Sustainable road safety : developing community-based macro-level collision prediction models of increased bicycle use in the Regional District of Central Okanagan

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dc.contributor.author Wei, Feng
dc.date.accessioned 2012-11-01T23:00:24Z
dc.date.available 2012-11-01T23:00:24Z
dc.date.copyright 2012 en_US
dc.date.issued 2012-11-01
dc.identifier.uri http://hdl.handle.net/2429/43565
dc.description.abstract Since the energy crisis, global warming, transportation congestion, and road safety issues, governments and individuals are more willing to use sustainable transportation modes to support the development of compact neighbourhoods. Bicycling, as one of the most effective modes for short-distance trips, is widely encouraged. However, as vulnerable road users (VRUs), cyclists are more likely to be injured when they are involved in collisions. In order to prevent bicycle collisions from occurring, it is necessary to conduct proactive evaluation of cyclists’ road safety. Community-based, macro-level collision prediction models, as prospective empirical tools to evaluate and predict road safety, have been suggested in reactive road safety applications and road safety planning. This research reviews previous studies on road safety and bicycle use and summarizes different regression methods for collision prediction model (CPM) development. On the basis of insights gained in the literature review, community-based, macro-level CPMs related to bicycle use were developed using the generalized linear regression (GLM), zero-inflated count regression (ZIC), geographically weighted regression (GWR), and full Bayesian (FB) methods, based on data from the Regional District of Central Okanagan (RDCO) of British Columbia, Canada. The statistical associations of total/severe/bike-vehicle collisions and their neighbourhood traits, which were derived from these model results, are reasonable. In the reactive road safety application of macro-level black spot study, bike-vehicle collision prone zones (CPZs) were identified and ranked in the RDCO with the developed GLM and FB collision prediction models and preliminary diagnoses and remedies for these CPZs are suggested. Finally, data gaps for macro-level CPM development were identified. In order to improve data quality and linkage, several suggestions about how to build an integrated data warehouse for vulnerable road user collisions are proposed. en_US
dc.language.iso eng en_US
dc.publisher University of British Columbia en
dc.title Sustainable road safety : developing community-based macro-level collision prediction models of increased bicycle use in the Regional District of Central Okanagan en_US
dc.type Electronic Thesis or Dissertation en
dc.degree.name Master of Applied Science - MASc en_US
dc.degree.discipline Civil Engineering en_US
dc.degree.grantor University of British Columbia en
dc.date.graduation 2013-05 en_US
dc.degree.campus UBCO en_US
dc.description.scholarlevel Graduate en


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