Bicycle Safety Analysis at Intersections from Crowdsourced Data

Abstract

Cycling is encouraged in countries around the world as an economic, energy efficient, and sustainable mode of transportation. Although there are many studies focusing on analyzing bicycle safety, they have limitations because of the shortage of bicycle exposure data. This study represents a major step forward in estimating safety performance functions for bicycle crashes at intersections by using crowdsourced data from STRAVA. Several adjustments in respect of the population distribution and field observations were made to overcome the disproportionate representation of the STRAVA data. The adjusted STRAVA data which include bicycle exposure information were used as input to develop safety performance functions. The functions are negative binomial models aimed at predicting frequencies of bicycle crashes at intersections. The developed model was compared with three counterparts: the model using the unadjusted STRAVA data, the model using the STRAVA data with field observation data adjustments only, and the model using the STRAVA data with adjusted population. The results revealed that the case of STRAVA data with both population and field observation data adjustments had the best performance in bicycle crash modeling. The results also addressed several key factors (e.g., signal control system, intersection size, bike lanes) which are associated with bicycle safety at intersections. Additionally, the safety-in-numbers effect was acknowledged when bicycle crash rates decreased as bicycle activities increased. The study concluded that crowdsourced data are a reliable source for exploring bicycle safety after the appropriate adjustments.

Risk-Perception Formation Considering Tangible and Non-Tangible Aspects of Cycling: A Flemish Case Study

“…This conclusion is not in accordance with the belief that geometric features such as curvature and width are important to prevent accidents/injuries [45]. Geometry cannot be overlooked when designing intersections [19], intersection size, and bicycle lanes are positively associated with safety [46]. Features such as curvature, radius, width, and surface are considered to be critical for designing a cycling network and for comfort levels [18,47].…”

Estimation of Average Annual Daily Bicycle Counts using Crowdsourced Strava Data

“…However, Strava Metro is the only service that provides a dataset in multiple temporal aggregations for practical analysis in GIS-ready data formats. Evidence from earlier research and practice show predominant representation in Strava by a fitness and recreation-oriented market, nonetheless it supports a range of practical uses, including understanding where bicyclists ride for health (19), relative collision exposures (5,20), and temporal variation (21). The Oregon Department of Transportation explored practical use of Strava Metro soon after the service became available, finding it useful to identify routes with high bicycling ridership, but also a need to ''expand this information up to total bike riders'' (22).…”

“…An extensive review of big data for bicycling research suggested a research agenda exploring combinations of crowdsourced and traditional information to develop new insights on travel and analysis methods that scale beyond current approaches (23). To date, published approaches for scaling crowdsourced data include a focus on the use of population and traffic counters (20), and multi-factor Poisson regression in Maricopa County, Arizona (24). Though some studies have combined crowdsourced data with traffic counts and environmental data to understand bicycling contexts better, we suggest that both practitioners and researchers could benefit from a clear approach to expand crowdsourced data to estimate meaningfully the total volume of bicycling trips.…”

Exposure-Based Models of Trail User Crashes at Roadway Crossings

“…After controlling for bicycle and motor vehicle volumes, models have shown bicyclist crashes to be positively associated with signal-controlled intersections (29), more intersection legs (29), more motor vehicle turning movements (30), intersection size (29), total crosswalk length (30), higher speed limits (29), and bus stops (30), but negatively associated with bike lanes (29) and medians (29,30). These findings are generally consistent with broad literature reviews of pedestrian- (31,32) and bicycle safety studies (33).…”