On-road bicycle facilities and bicycle crashes in Iowa, 2007–2010

Hamann, Cara; Peek‐Asa, Corinne

doi:10.1016/j.aap.2012.12.031

Cited by 80 publications

(50 citation statements)

References 10 publications

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“…As for the effects of roadway density, more drive lanes and bike lanes are positively associated with the number of bicycle crashes (Wei and Lovegrove, 2012). Of different types of bicycle facilities, off-road bike lanes are safer than on-road bike lanes (Hamann and Peek-Asa, 2013;Reynolds et al, 2009;Teschke et al, 2012), and the installation of bicycle lanes does not lead to additional crashes, but a possible increase in the number of cyclists (Chen et al, 2012). Sakshaug et al (2010) found that adding roundabouts produced more bicycle conflicts as the yielding rules were ambiguous in roundabout areas, contributing to a lower yielding rate and less trust among road users.…”

Section: Relationships Between Built Environment Factors and Bicycle mentioning

confidence: 98%

“…Among the travel demand variables, vehicle volume (Hamann and Peek-Asa, 2013;Schepers et al, 2011), bicycle volume (Hamann and Peek-Asa, 2013;Miranda-Moreno et al, 2011b;Schepers et al, 2011;Strauss et al, 2013) and large vehicle volume (Vandenbulcke et al, 2014) have been included for modeling, and all of them suggest positive associations with bicycle crash frequency. As for traffic control variables, a higher density of low-speed streets (<15 mph) is negatively associated with the number of bicycle crashes (Siddiqui et al, 2012), while more roads with high-speed limits (>35 mph) have an increased number of bicycle crashes (Chen and Fuller, 2014;Siddiqui et al, 2012).…”

Section: Relationships Between Built Environment Factors and Bicycle mentioning

confidence: 99%

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Built environment factors in explaining the automobile-involved bicycle crash frequencies: A spatial statistic approach

Chen

2015

Safety Science

121

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Section: Relationships Between Built Environment Factors and Bicycle mentioning

confidence: 98%

Section: Relationships Between Built Environment Factors and Bicycle mentioning

confidence: 99%

Built environment factors in explaining the automobile-involved bicycle crash frequencies: A spatial statistic approach

Chen

2015

Safety Science

121

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“…Results of urban safety analyses (Hamann and Peek-Asa, 2013;Klassen et al, 2014;Osberg et al, 1998) cannot be extrapolated to overtaking manoeuvres of motor vehicles and bicycles on rural roads, because of the higher speeds of motor vehicles and the type of manoeuvres that take place on them.…”

Section: Introductionmentioning

confidence: 97%

Motor vehicles overtaking cyclists on two-lane rural roads: Analysis on speed and lateral clearance

et al. 2017

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a b s t r a c tTwo-lane rural roads in Spain accommodate significant bicycle traffic volumes, mainly associated to sport and leisure activities. Motor vehicles' higher speed, weight and volume, compared to cyclists, represent a serious safety concern when overtaking a bicycle. Spanish traffic rules determine a minimum 1.5 m lateral distance.This research characterised 2928 overtaking manoeuvres in the overtaking lateral clearance between motor vehicle and bicycle, as well as in the motor vehicle speed, in contrast with previous research. Two instrumented bicycles were equipped with laser rangefinders, a GPS tracker and three video cameras. They rode along seven rural road segments at a speed between 15 and 25 km/h, centred on the paved shoulder, or as close as possible to the outer edge. Besides, this methodology allowed the characterisation of the overtaken vehicle type, its left lane occupation as well as its interaction with opposing traffic flow. For each session, rider's general risk perception was also registered.The analysis suggested that lateral clearance is not the only factor that influenced rider's risk perception, although current standards are only related to it. On the contrary, a combined factor of lateral clearance, vehicle type and vehicle speed had a more significant correlation with the perceived risk. This agreed with literature models of transient aerodynamic forces between overtaking and overtaken vehicles. Results showed that effect of heavy vehicles on bicyclists was also strong. In addition to this, the combined factor of clearance and speed was higher on tangent sections where overtaking was permitted.

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“…The Poisson regression approach has frequently been used in bicycle crash analysis by [10] and [35] as well as for exposure measurement of accidents by [50]. A probabilistic joint analysis approach has been used for correcting sampling bias in species distribution models by [51].…”

Section: Discussionmentioning

confidence: 99%

Correcting Bias in Crowdsourced Data to Map Bicycle Ridership of All Bicyclists

et al. 2019

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Traditional methods of counting bicyclists are resource-intensive and generate data with sparse spatial and temporal detail. Previous research suggests big data from crowdsourced fitness apps offer a new source of bicycling data with high spatial and temporal resolution. However, crowdsourced bicycling data are biased as they oversample recreational riders. Our goals are to quantify geographical variables, which can help in correcting bias in crowdsourced, data and to develop a generalized method to correct bias in big crowdsourced data on bicycle ridership in different settings in order to generate maps for cities representative of all bicyclists at a street-level spatial resolution. We used street-level ridership data for 2016 from a crowdsourced fitness app (Strava), geographical covariate data, and official counts from 44 locations across Maricopa County, Arizona, USA (training data); and 60 locations from the city of Tempe, within Maricopa (test data). First, we quantified the relationship between Strava and official ridership data volumes. Second, we used a multi-step approach with variable selection using LASSO followed by Poisson regression to integrate geographical covariates, Strava, and training data to correct bias. Finally, we predicted bias-corrected average annual daily bicyclist counts for Tempe and evaluated the model's accuracy using the test data. We found a correlation between the annual ridership data from Strava and official counts (R 2 = 0.76) in Maricopa County for 2016. The significant variables for correcting bias were: The proportion of white population, median household income, traffic speed, distance to residential areas, and distance to green spaces. The model could correct bias in crowdsourced data from Strava in Tempe with 86% of road segments being predicted within a margin of ±100 average annual bicyclists. Our results indicate that it is possible to map ridership for cities at the street-level by correcting bias in crowdsourced bicycle ridership data, with access to adequate data from official count programs and geographical covariates at a comparable spatial and temporal resolution.

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On-road bicycle facilities and bicycle crashes in Iowa, 2007–2010

Cited by 80 publications

References 10 publications

Built environment factors in explaining the automobile-involved bicycle crash frequencies: A spatial statistic approach

Built environment factors in explaining the automobile-involved bicycle crash frequencies: A spatial statistic approach

Motor vehicles overtaking cyclists on two-lane rural roads: Analysis on speed and lateral clearance

Correcting Bias in Crowdsourced Data to Map Bicycle Ridership of All Bicyclists

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