Theses and Dissertations at Montana State University (MSU)
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/732
Browse
2 results
Search Results
Item A study on dynamic bicycle detection and warning device(Montana State University - Bozeman, College of Engineering, 2018) Relph, David Edward; Chairperson, Graduate Committee: Ahmed Al-KaisyThis research pertains to the effectiveness and reliability of a Dynamic Bicycle Detection and Warning sign. In rural areas there are fewer safety options due to financing and environmental design restrictions. Rural roads normally have higher speeds and reduced lane widths with limited or no shoulders. Thus, safety on roads in rural environments should be of great concern for designers. Data concerning interactions between bicycles and vehicles was collected to determine the effectiveness and reliability of the system. Before- and after-installation data was collected via radar traffic recorders and video cameras at two sites along Rim Rock Drive. The data collected before-installation was used to determine a baseline for the interactions between drivers and bicyclists. Two indicators of drivers' reaction were used: vehicle speed and lateral placement in the lane. Three other variables were tested for their effect on the two aforementioned variables: time to opposing vehicle arrival, time between a bicycle and a vehicle, and vehicle class. The variable, lights flashing was added to determine the effects of the system on speed and lateral placement. Results suggest vehicle speed and lateral placement are both affected by the presence of bicyclists. Before-installation affects were primarily seen when the vehicle was observed within 10 seconds or less of a bicycle, or within approximately 400 feet. After-installation the affects were seen up to four minutes after the bicycle passed the site. Driver reactions were similar before and after up to 20 seconds. After 20 seconds drivers moved over more and after 30 seconds drivers slowed down more after-installation. The system detected a bicycle 86% of the time and detected something else 11% of the time. In conclusion the system reduced speeds and encouraged drivers to move over. However, no change in driver behavior occurred under 20 seconds. Speeds averaged around 26 miles per hour which is over the posted speed limit. Most drivers were still utilized the right third of the lane. Therefore, the system was reliable and effective, enhancing bicyclist safety, but driver behavior still poses a danger to bicyclists on Rim Rock Drive.Item Mapping risky driver behavior and identifying their contributory factors : a spatial statistical approach(Montana State University - Bozeman, College of Engineering, 2016) Sharda, Shivam; Chairperson, Graduate Committee: Yiyi WangThe goal of this study is to develop risk maps that predict zones that have an increased risk of traffic crashes for utility service trucks of the electric power industry. This study employed a national dataset that contains video logs of driving events from 10,009 utility truck drivers from 2010 to 2014. The study consists of four steps. Step 1 focused on finding whether certain driver behaviors (e.g., traffic violation, distraction, etc.) cluster in the same location as crashes and, therefore, suggest behaviors that are predictive of crashes. The study used Getis-Ord Gi* hot-spot analysis to reveal the clustering pattern within a standard unit of area (at the grid cell level: 1,640 feet by 1,640 feet). The finding of this research indicated that four behaviors ('risky behaviors') consistently cluster with the collision outcomes: distraction, lack of awareness, following too close, and eating/drinking. In Step 2, negative binomial models were used to relate the occurrence of the risky behaviors to a host of geospatial variables (e.g., land use, traffic, and socio-economics) while controlling for the exposure at the grid level (200 feet by 200 feet, roughly the size of a street block). Step 2 was implemented on the three datasets that were assembled based on different levels of availability of the geospatial features. Results indicated that well-balanced land use, road network density, lane-mile density of secondary and primary roads (if urban areas), and high concentration of elderly people (65 years and above) contributed to the prevalence of risky behaviors. Residential neighborhood, local road (if rural area), and average household size were shown to dampen incidence of risky behaviors. Step 3 developed the scoring systems to estimate the overall risk of each risky behavior for a given location (grid). Finally, Step 4 developed risk maps on a 2-D scale to delineate locations into different levels of hazards. In sum, this study confirmed the linkage between driver behavior and collisions and proposed a new way to anticipate crashes. While the test dataset pertains to utility service trucks, the methods can be adapted for predicting locations where the risk of future crashes is higher.