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Developing macro-level collision prediction models to enhance traditional road safety improvement programs and evaluate bicycle safety in the City of Vancouver

University of British Columbia

To encourage greener cities while reducing transportation impacts such as climate change, traffic congestion, and road safety issues, governments have been investing in sustainable transportation modes such as cycling. A safe and comfortable cycling environment is critical to encourage bicycle trips, since cyclists are subject to greater safety risks and represent the highest share of severe and fatal road collisions. Traditionally, engineering approaches have addressed road safety in reaction to existing collision histories. For bicycle collisions, which are rare events, a proactive approach is more appropriate. This study described the development of bicycle related macro-level (i.e. neighbourhood or traffic analysis zone level) Collision Prediction Models (CPMs) and tested the models as empirical tools for bicycle road safety evaluation and planning. This study was unique in its usage of the bicycle exposure variable represented by Bicycle Kilometers Travelled (BKT) as a lead exposure variable in the models. The macro-level CPMs that were developed for bicycle-vehicle collisions were applied to a case study of the City of Vancouver at the zonal level. The objectives of the study were to: (1) identify bicycle data safety indicators, (2) develop bicycle macro-level CPMs using generalized linear regression modeling (GLM), (3) demonstrate model use by applying them to a case study of the City of Vancouver through a macro-reactive road safety application, and (4) identify potential safety countermeasures for the highest ranked Collision Prone Zones (CPZs). The models were effective in enhancing traditional road safety initiatives and identifying and ranking dangerous CPZs in the City of Vancouver. The top three collision prone areas were then brought forward for diagnosis and remedy analysis. This case study effectively demonstrated the use of the models to proactively enhance bicycle safety.

Text

2016-07-08

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58403

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/