Likelihood Parameterization of Bicycle Crash Injury Severities

Abstract

This paper evaluates different factors and parameters contributing to likelihood of bicycle crash injury severity levels. Multinomial Logit (MNL) model was used to analyze impact of different roadway features, traffic characteristics and environmental conditions associated with bicycle crash injury severities. The multinomial model was used due to its flexibility in quantifying the effect of the independent variables for each injury severity categories. Model results showed that, severity of bicycle crashes increases with increase in vehicles per lane, number of lanes, bicyclist alcohol or drug use, routes with 35 -45 mph posted speed limits, riding along curved or sloped road sections, when bicyclists approach or cross a signalized intersection, and at driveways. In addition, routes with a high percentage of trucks, roadway sections with curb and gutter, cloudy or foggy weather and obstructed vision were found to have high probability of severe injury. Segments with wider lanes, wide median and wide shoulders were found to have low likelihood of severe bicycle injury severities. Limited lighting locations was found to be associated with incapacitating injury and fatal crashes, indicating that insufficient visibility can potentially lead to severe crashes. Other findings are also presented in the paper.

Spatial characteristics of bicycle–motor vehicle crashes in Christchurch, New Zealand: A case-control approach

“…All four models identified that intersections and driveways contribute to an increase in crash odds reflecting the findings of previous studies undertaken by Isaksson-Hellman, (2012) for intersections and Chimba et al, (2012) for driveways. Regarding intersections, the findings of this research reflects the findings of previous studies which are of European context (Reynolds et al, 2009).…”

“…With regards to infrastructure type, crash risk increases with the presence of driveways (Chimba, Emaasit, & Kutela, 2012), high traffic speeds (Kim, Kim, Ulfarsson, & Porrello, 2007), intersections (Isaksson-Hellman, 2012) and specific road classifications (Li, Zhu, & Sui, 2007) while the presence of bicycle-only infrastructure (e.g., separated cycling facilities, cycle routes and bike lanes) reduces the crash risk compared to mixing with traffic (Reynolds et al, 2009). Of particular interest, in New Zealand and North America, the presence of marked on road cycle lanes has been associated with a crash risk reduction (Parsons & Koorey, 2013;Reynolds et al, 2009) while on a world-wide scale Stewart and McHale (2014) suggest that there is much contradictory evidence surrounding the benefits of cycle lanes.…”

Leveraging Social Media Data to Understand Spatial and Severity of Roadway Crashes in Tanzania

“…The application of the MNL requires the selection of the base category (Chimba, Emaasit, and Kutela 2012;Liu et al 2015;Salum et al 2019). In this case, the category "no injuries" was used as the base category.…”