Abstract

In recent years, cycling has become an increasingly popular transportation mode around the world. In contrast to other popular modes of transportation, cycling is more economic and energy efficient. While many studies have been conducted for the bicycle safety analysis, most of them were limited in terms of 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 causes a change in motorists' driving behavior. In Florida, these speed management strategies were defined by the Florida Department of Transportation (FDOT) Design Manual. Considering the disproportion in the representation of cyclists from the STRAVA data, adjustments were done to more accurately represent the cyclists based on the video detection data by developing a Tobit model. The adjusted STRAVA data was used 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 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 suggested that the adjusted STRAVA data could be used as the exposure for bicycle crash analysis. Also, the results highlighted the significant effects of speed management strategies such as parking lots and surface pavement. It is expected that the results could help engineers develop effective strategies to enhance safety for bicyclists.

Notes

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Graduation Date

2021

Semester

Fall

Advisor

Abdel-Aty, Mohamed

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Civil, Environmental and Construction Engineering

Degree Program

Civil Engineering; Transportation System Engineering

Format

application/pdf

Identifier

CFE0008911; DP0026190

URL

https://purls.library.ucf.edu/go/DP0026190

Language

English

Release Date

December 2021

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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