CSET Project Reports
http://hdl.handle.net/11122/7891
2024-03-23T09:39:19ZPromoting Positive Traffic Safety Culture in RITI Communities through Active Engagement: Implementation Guide and Outreach Activities
http://hdl.handle.net/11122/14668
Promoting Positive Traffic Safety Culture in RITI Communities through Active Engagement: Implementation Guide and Outreach Activities
Pehrson, Jacob; Prescott, Logan; Abdel-Rahim, Ahmed
RITI crash data analysis clearly highlights three major areas of concern: prevalence of excessive speed, impaired and distracted driving, and underage driving. Safety-focused educational programs and awareness campaigns have all contributed to a reduction in crashes in urban areas. However, in RITI communities, much more work is still needed. It is important that communities are provided with the proper resources and methods to deliver the appropriate training and educational tools that promote and cause a significant positive change in the traffic safety culture. Through reviewed literature and interviews with tribal community stakeholders, this research team came to understand that tribal youth are most impacted and engaged when educational material is made culturally relevant. We then developed an implementation guide to be used by tribes to create, develop, and enact a sustained educational program with the mission to positively impact traffic safety culture among youth in tribal and rural communities.
2023-09-01T00:00:00ZAssessing the Relative Risks of School Travel in Rural Communities
http://hdl.handle.net/11122/14667
Assessing the Relative Risks of School Travel in Rural Communities
Chang, Kevin; Souvenir, Brandt
This study examined school travel safety and risk and explored the potential differences between conditions that are present today with those that existed nearly two decades ago, when the Transportation Research Board published its landmark study on school travel safety. For this study, thirty transportation professionals were interviewed and a twenty-year crash data set from the Fatality Analysis Reporting System (FARS) was analyzed.
The responses from the interviews were separated into ten common themes. The three most mentioned themes were education programs, concerns of roadway environments, and school bus safety. Based on the responses, concerns about the roadway environment, poor driver behavior, and the role of parents on mode choice have not changed in the last twenty years; however, safety education programs, vehicle centric travel, community planning, and pick up/drop off safety have evolved over time.
With regard to the FARS data set, which was used as a benchmark to assess school transportation safety, the overall trends indicate that the trip to and from school remains a relatively safe activity, particularly along rural facilities where positive results were identified across four key metrics. Along urban facilities, slightly increasing trends were observed in the annual number of fatalities and in the number of non-motorists involved in a fatal crash, suggesting that opportunities remain to enhance and to improve the travel environment for school children.
2023-09-01T00:00:00ZTHE PERCEPTION OF AUTONOMOUS DRIVING IN RURAL COMMUNITIES
http://hdl.handle.net/11122/13432
THE PERCEPTION OF AUTONOMOUS DRIVING IN RURAL COMMUNITIES
Chang, Kevin; Williams, Jade
Autonomous, or self-driving, vehicles have the capability to either fully or partially replace a human driver in the navigation to a destination. To better understand how receptive society will be to these types of vehicles, this study focused on the perceived level of trust in autonomous vehicles (AVs) by rural drivers and passengers. An online survey that examined the behavioral and value-based perspectives of drivers was developed and distributed to respondents across the United States, and a total of 1,247 valid responses were collected and analyzed. Based on the results, rural (and non-rural) respondents had similar levels of trust when comparing self-driving vehicles with human-driven vehicles, though older people and those with less education tended to have less trust in self-driving vehicles. The outcomes from this study can be used to support targeted outreach efforts for those drivers who remain skeptical about the overall safety benefits of this evolving transportation technology area.
2023-07-01T00:00:00ZDrone-based Computer Vision-Enabled Vehicle Dynamic Mobility and Safety Performance Monitoring
http://hdl.handle.net/11122/13171
Drone-based Computer Vision-Enabled Vehicle Dynamic Mobility and Safety Performance Monitoring
Zhang, Guohui; Yuan, Runze; Prevedouros, Panos; Ma, Tianwei
This report documents the research activities to develop a drone-based computer vision-enabled vehicle dynamic safety performance monitoring in Rural, Isolated, Tribal, or Indigenous (RITI) communities. The acquisition of traffic system information, especially the vehicle speed and trajectory information, is of great significance to the study of the characteristics and management of the traffic system in RITI communities. The traditional method of relying on video analysis to obtain vehicle number and trajectory information has its application scenarios, but the common video source is often a camera fixed on a roadside device. In the videos obtained in this way, vehicles are likely to occlude each other, which seriously affects the accuracy of vehicle detection and the estimation of speed. Although there are methods to obtain high-view road video by means of aircraft and satellites, the corresponding cost will be high. Therefore, considering that drones can obtain high-definition video at a higher viewing angle, and the cost is relatively low, we decided to use drones to obtain road videos to complete vehicle detection. In order to overcome the shortcomings of traditional object detection methods when facing a large number of targets and complex scenes of RITI communities, our proposed method uses convolutional neural network (CNN) technology. We modified the YOLO v3 network structure and used a vehicle data set captured by drones for transfer learning, and finally trained a network that can detect and classify vehicles in videos captured by drones. A self-calibrated road boundary extraction method based on image sequences was used to extract road boundaries and filter vehicles to improve the detection accuracy of cars on the road. Using the results of neural network detection as input, we use video-based object tracking to complete the extraction of vehicle trajectory information for traffic safety improvements. Finally, the number of vehicles, speed and trajectory information of vehicles were calculated, and the average speed and density of the traffic flow were estimated on this basis. By analyzing the acquiesced data, we can estimate the traffic condition of the monitored area to predict possible crashes on the highways.
2023-01-30T00:00:00Z