An empirical tool to evaluate the safety of cyclists: Community based, macro-level collision prediction models using negative binomial regression

► This paper has hypothesized a global model on the relationship between bicycle use and road safety levels. ► We conduct a literature review on micro-level and macro-level bicycle collision prediction models (CPMs). ► We use negative binomial regression to develop macro-level bicycle CPMs using dat...

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Veröffentlicht in:Accident analysis and prevention 2013-12, Vol.61, p.129-137
Hauptverfasser: Wei, Feng, Lovegrove, Gordon
Format: Artikel
Sprache:eng
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Zusammenfassung:► This paper has hypothesized a global model on the relationship between bicycle use and road safety levels. ► We conduct a literature review on micro-level and macro-level bicycle collision prediction models (CPMs). ► We use negative binomial regression to develop macro-level bicycle CPMs using data from the Central Okanagan Regional District (CORD), Canada. ► We suggested statistical relationships between current levels of cycling and road safety in North America. ► We recommend data issues in this model development for future research. Today, North American governments are more willing to consider compact neighborhoods with increased use of sustainable transportation modes. Bicycling, one of the most effective modes for short trips with distances less than 5km is being encouraged. However, as vulnerable road users (VRUs), cyclists are more likely to be injured when involved in collisions. In order to create a safe road environment for them, evaluating cyclists’ road safety at a macro level in a proactive way is necessary. In this paper, different generalized linear regression methods for collision prediction model (CPM) development are reviewed and previous studies on micro-level and macro-level bicycle-related CPMs are summarized. On the basis of insights gained in the exploration stage, this paper also reports on efforts to develop negative binomial models for bicycle–auto collisions at a community-based, macro-level. Data came from the Central Okanagan Regional District (CORD), of British Columbia, Canada. The model results revealed two types of statistical associations between collisions and each explanatory variable: (1) An increase in bicycle–auto collisions is associated with an increase in total lane kilometers (TLKM), bicycle lane kilometers (BLKM), bus stops (BS), traffic signals (SIG), intersection density (INTD), and arterial–local intersection percentage (IALP). (2) A decrease in bicycle collisions was found to be associated with an increase in the number of drive commuters (DRIVE), and in the percentage of drive commuters (DRP). These results support our hypothesis that in North America, with its current low levels of bicycle use (
ISSN:0001-4575
1879-2057
DOI:10.1016/j.aap.2012.05.018