Matching Items (2)
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- All Subjects: Bicycle
- Genre: Masters Thesis
- Creators: Bush, Jonathan Ernest
- Creators: Kuby, Mike
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.
ContributorsMcIntyre, Andrew (Author) / Salon, Deborah (Thesis advisor) / Kuby, Mike (Committee member) / Chester, Mikhail (Committee member) / Arizona State University (Publisher)
Created2016
Description
Riding a bicycle requires accurately performing several tasks, such as balancing and navigation, which may be difficult or even impossible for persons with disabilities. These difficulties may be partly alleviated by providing active balance and steering assistance to the rider. In order to provide this assistance while maintaining free maneuverability, it is necessary to measure the position of the rider on the bicycle and to understand the rider's intent. Applying autonomy to bicycles also has the potential to address some of the challenges posed by traditional automobiles, including CO2 emissions, land use for roads and parking, pedestrian safety, high ownership cost, and difficulty traversing narrow or partially obstructed paths.
The Smart Bike research platform provides a set of sensors and actuators designed to aid in understanding human-bicycle interaction and to provide active balance control to the bicycle. The platform consists of two specially outfitted bicycles, one with force and inertial measurement sensors and the other with robotic steering and a control moment gyroscope, along with the associated software for collecting useful data and running controlled experiments. Each bicycle operates as a self-contained embedded system, which can be used for untethered field testing or can be linked to a remote user interface for real-time monitoring and configuration. Testing with both systems reveals promising capability for applications in human-bicycle interaction and robotics research.
The Smart Bike research platform provides a set of sensors and actuators designed to aid in understanding human-bicycle interaction and to provide active balance control to the bicycle. The platform consists of two specially outfitted bicycles, one with force and inertial measurement sensors and the other with robotic steering and a control moment gyroscope, along with the associated software for collecting useful data and running controlled experiments. Each bicycle operates as a self-contained embedded system, which can be used for untethered field testing or can be linked to a remote user interface for real-time monitoring and configuration. Testing with both systems reveals promising capability for applications in human-bicycle interaction and robotics research.
ContributorsBush, Jonathan Ernest (Author) / Zhang, Wenlong (Thesis advisor) / Heinrichs, Robert (Thesis advisor) / Sandy, Douglas (Committee member) / Arizona State University (Publisher)
Created2020