Where do bike lanes work best? A Bayesian spatial model of bicycle lanes and bicycle crashes

Abstract

US municipalities are increasingly introducing bicycle lanes to promote bicycle use, increase roadway safety and improve public health. The aim of this study was to identify specific locations where bicycle lanes, if created, could most effectively reduce crash rates. Previous research has found that bike lanes reduce crash incidence, but a lack of comprehensive bicycle traffic flow data has limited researchers' ability to assess relationships at high spatial resolution. We used Bayesian conditional autoregressive logit models to relate the odds that a bicycle injury crash occurred on a street segment in Philadelphia, PA (n = 37,673) between 2011 and 2014 to characteristics of the street and adjacent intersections. Statistical models included interaction terms to address the problem of unknown bicycle traffic flows, and found bicycle lanes were associated with reduced crash odds of 48% in streets segments adjacent to 4-exit intersections, of 40% in streets with oneor two-way stop intersections, and of 43% in high traffic volume streets. Presence of bicycle lanes was not associated with change in crash odds at intersections with less or more than 4 exits, at 4way stop and signalized intersections, on one-way streets and streets with trolley tracks, and on streets with low-moderate traffic volume. The effectiveness of bicycle lanes appears to depend most on the configuration of the adjacent intersections and on the volume of vehicular traffic. Our approach can be used to predict specific street segments on which the greatest absolute reduction in bicycle crash odds could occur by installing new bicycle lanes.

On-road bicycle lane types, roadway characteristics, and risks for bicycle crashes

“…Our findings are consistent with existing research describing relationships between bicycle lanes of all types and bicycle crashes on street segments. Similar to our previous study in Philadelphia (Kondo et al, 2018) and other published research (Kim and Kim, 2015; Thomas and DeRobertis, 2013), we found bicycle lanes to be most effective on streets with higher speed limits, which also have greater volumes of vehicular traffic. Likewise, the finding that bicycle lanes are most effective where vehicular traffic lanes are narrower reinforces Schepers et al's (2017) assertion that greater distance between the cyclists and motor vehicles reduces crash risk.…”

“…An essential methodological consideration for studies of bicycle lanes and bicycle crashes is that the volume of bicycle traffic through roadway sections is often unknown (DiGioia et al, 2017), yet this exposure drives much of the observed variation in crash incidence. Bicycle volume data were not available for Melbourne; however, we addressed this denominator problem by using interaction terms in regression analyses, an approach we have previously implemented in Philadelphia, PA (Kondo et al, 2018).…”

Expanding the Scope of the Bicycle Level-of-Service Concept: A Review of the Literature

“…Furthermore, the traffic enforcement and control aspects of cycling (such as traffic signs and lane markings) have been covered neither in comfort-related nor BLOS studies. This tool could contribute to the regulation of cyclists in terms of both on-street and off-street facilities and could potentially reduce the conflict between agents [193]. Traffic signs and signals (as traffic enforcement) can directly affect cyclists' comfort through imposing unnecessary stops and the process of stop-and-go.…”

Велосипед И Безопасность В Городе: Развитие Транспортной Инфраструктуры

“…Отметим, что официальная статистика ДТП может не отражать реальной ситуации, так как многие ДТП с велосипедистами не фиксируются [Kondo, 2017], и официальные данные дорожных служб разнятся с данными медицинских служб, фиксирующих полученные травмы и их причины [Pucher, Buehler, 2008].…”