Full metadata
Title
Determinants of bicycle and pedestrian crash severity in San Francisco, CA
Description
Bicyclist and pedestrian safety is a growing concern in San Francisco, CA,
especially given the increasing numbers of residents choosing to bike and walk. Sharing
the roads with automobiles, these alternative road users are particularly vulnerable to
sustain serious injuries. With this in mind, it is important to identify the factors that
influence the severity of bicyclist and pedestrian injuries in automobile collisions. This
study uses traffic collision data gathered from California Highway Patrol’s Statewide
Integrated Traffic Records System (SWITRS) to predict the most important
determinants of injury severity, given that a collision has occurred. Multivariate binomial
logistic regression models were created for both pedestrian and bicyclist collisions, with
bicyclist/pedestrian/driver characteristics and built environment characteristics used as
the independent variables. Results suggest that bicycle infrastructure is not an important
predictor of bicyclist injury severity, but instead bicyclist age, race, sobriety, and speed
played significant roles. Pedestrian injuries were influenced by pedestrian and driver age
and sobriety, crosswalk use, speed limit, and the type of vehicle at fault in the collision.
Understanding these key determinants that lead to severe and fatal injuries can help
local communities implement appropriate safety measures for their most susceptible
road users.
especially given the increasing numbers of residents choosing to bike and walk. Sharing
the roads with automobiles, these alternative road users are particularly vulnerable to
sustain serious injuries. With this in mind, it is important to identify the factors that
influence the severity of bicyclist and pedestrian injuries in automobile collisions. This
study uses traffic collision data gathered from California Highway Patrol’s Statewide
Integrated Traffic Records System (SWITRS) to predict the most important
determinants of injury severity, given that a collision has occurred. Multivariate binomial
logistic regression models were created for both pedestrian and bicyclist collisions, with
bicyclist/pedestrian/driver characteristics and built environment characteristics used as
the independent variables. Results suggest that bicycle infrastructure is not an important
predictor of bicyclist injury severity, but instead bicyclist age, race, sobriety, and speed
played significant roles. Pedestrian injuries were influenced by pedestrian and driver age
and sobriety, crosswalk use, speed limit, and the type of vehicle at fault in the collision.
Understanding these key determinants that lead to severe and fatal injuries can help
local communities implement appropriate safety measures for their most susceptible
road users.
Date Created
2016
Contributors
- McIntyre, Andrew (Author)
- Salon, Deborah (Thesis advisor)
- Kuby, Mike (Committee member)
- Chester, Mikhail (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
v, 67 pages : color illustrations, color maps
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.40789
Statement of Responsibility
by Andrew McIntyre
Description Source
Viewed on January 25, 2017
Level of coding
full
Note
thesis
Partial requirement for: M.S., Arizona State University, 2016
bibliography
Includes bibliographical references (pages 41-43)
Field of study: Sustainability
System Created
- 2016-12-01 07:04:56
System Modified
- 2021-08-30 01:20:26
- 3 years 2 months ago
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