Explore effects of bicycle facilities and exposure on bicycle safety at intersections

Cai, Qing; Abdel-Aty, Mohamed; Castro, Scott

doi:10.1080/15568318.2020.1772415

Cited by 22 publications

(4 citation statements)

References 48 publications

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“…Mobile phone apps allowing collection of crowdsourced data are seen as a new and efficient way of collecting bicycle-related information that would not be possible otherwise. Fitness apps (e.g., STRAVA) can be used to collect bicycle trip characteristic data, such as routes and trip duration, and have also been used in bicycle safety studies as a means of identifying bicycle demand ( 23 , 25 , 39 ). In addition to STRAVA data, additional sources can be used to extract bicycle demand data, as discussed by Kothuri et al ( 40 ).…”

Section: Datamentioning

confidence: 99%

Investigating the Safety Impact of Segment- and Intersection-Level Bicycle Treatments on Bicycle–Motorized Vehicle Crashes

Deliali

Fournier

Christofa

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Bicycle treatments are installed to elevate motorists’ awareness of the presence of bicyclists and to enhance bicycle safety and mobility. To date, no studies have compared the safety benefits of sharrows and protected- and conventional bike lanes, or intersection-level treatments like bike boxes and intersection-crossing pavement markings. One factor that limits bicycle safety research is the lack of adequate bicycle exposure data. For this study, a crowdsource app was used for estimating networkwide bicycle demand data for Portland, OR. Crash prediction models were developed for road segments and signalized intersections to associate bicycle treatment presence and type with crash frequency. Compared with the “no treatment” case, protected bike lanes (odds ratio [OR] = 0.032), sharrows (OR = 0.211), and conventional bike lanes (OR = 0.552) were safer for road segments. Signalized intersections where segment-level bicycle treatments exist at more than one of the intersecting roads were associated with higher crash frequency. Specifically, for signalized intersections with one conventional bike lane, with two conventional bike lanes, or with a conventional- and a protected bike lane, the respective CMFs were 1.94, 2.07, and 3.38. Signalized intersections with at least one bike box or intersection-crossing pavement markings experienced higher crash frequency than intersections with no treatments, however not necessarily in the approach where the treatment was located. The respective Crash Modification Factors (CMFs) were 1.39 and 1.76. The findings could guide practitioners in selecting bicycle treatments for segments, whereas the models for signalized intersections could identify intersections with high crash frequency that need safety improvements.

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Section: Datamentioning

confidence: 99%

Investigating the Safety Impact of Segment- and Intersection-Level Bicycle Treatments on Bicycle–Motorized Vehicle Crashes

Deliali

Fournier

Christofa

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…Many studies have identified different types of road characteristics that have continuously been related to bicycle crashes (3)(4)(5)(6)(7)(8). Siddiqui et al (7) utilized Bayesian models to estimate bicycle crashes according to the presence of roadway characteristics in the U.S. State of Florida.…”

Section: Bicycle Safetymentioning

confidence: 99%

Effect of Various Speed Management Strategies on Bicycle Crashes for Urban Roads in Central Florida

Ugan

Abdel-Aty

Cai

et al. 2021

Transportation Research Record

Self Cite

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In recent years, cycling has become an increasingly popular transportation mode around the world. In comparison with other popular modes of transportation, cycling is economical and energy efficient. While many studies have been conducted for the analysis of bicycle safety, most were limited in bicycle exposure data and on-street data. This study tries to improve the current safety performance functions for bicycle crashes at urban corridors by utilizing crowdsource data from STRAVA and on-street speed management strategies data. Speed management strategies are any roadway alterations that cause a change in motorists’ driving behavior. In Florida, these speed management strategies are defined by the Florida Department of Transportation design manual. Considering the disproportionate representation of cyclists from the STRAVA data, adjustments were made to represent more accurately the cyclists based on the video detection data by developing a Tobit model. The adjusted STRAVA data was used for bicyclist exposure to analyze bicycle crashes on urban arterials. A Bayesian joint model was developed to identify the relations between the bicycle crash frequency and factors relating to speed management strategies. Other factors, such as vehicle traffic data, roadway information, socio-demographic characteristics, and land use data, were also considered in the model. The results suggest that the adjusted STRAVA data could be used as the exposure for bicycle crash analysis. The results also highlight the significant effects of speed management strategies, such as parking lots and surface pavement. It is expected that these findings could help engineers develop effective strategies to enhance safety for bicyclists.

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“…Accidents involving two-wheeled vehicles at intersections are closely related to factors such as population density, and the numbers of vehicles, bicycles, and lanes [13]. Razzaghi and others proposed that population density and GDP have positive and negative impacts, respectively, on the number of road traffic fatalities [14].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Different Population Density Scenarios on Two-Wheeler Accident Characteristics at Intersections

Wang,

Hu,

Wang

et al. 2024

Sustainability

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Examining 1192 intersection car and two-wheeled vehicle collision accidents from the China In-Depth Accident Study (CIDAS) database, this study employs population density heat maps for precise assessment of surrounding population densities at accident sites. The K-Medoid clustering algorithm and silhouette coefficient were used to classify accidents into two distinct groups based on population density. Subsequent application of the random parameter logit model revealed key contributing factors to these accidents in varying population densities. The results show notable differences in factors such as collision direction of two-wheeled vehicles, types of accident conflict, road conditions, and traffic flow, depending on the population density. Based on these conclusions, the research can inform differentiated risk prediction for two-wheeled vehicle accidents at intersections and provide insights for intersection design in various population density scenarios.

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Explore effects of bicycle facilities and exposure on bicycle safety at intersections

Cited by 22 publications

References 48 publications

Investigating the Safety Impact of Segment- and Intersection-Level Bicycle Treatments on Bicycle–Motorized Vehicle Crashes

Investigating the Safety Impact of Segment- and Intersection-Level Bicycle Treatments on Bicycle–Motorized Vehicle Crashes

Effect of Various Speed Management Strategies on Bicycle Crashes for Urban Roads in Central Florida

Assessing the Impact of Different Population Density Scenarios on Two-Wheeler Accident Characteristics at Intersections

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