Theses and Dissertations at Montana State University (MSU)
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Item Developing a network screening method for low volume roads(Montana State University - Bozeman, College of Engineering, 2020) Huda, Kazi Tahsin; Chairperson, Graduate Committee: Ahmed Al-KaisyCrash occurrences on rural low-volume roads (LVRs) are usually more severe in nature. This is mostly because of higher speeds and outdated infrastructure designs. Therefore, safety management programs for these roads are equally as important as their urban and high-volume counterparts. Network screening is an important aspect of safety management programs. However, traditional network screening methods based on historical crash data may not provide accurate results for LVRs. This is because of the sporadic nature of crash occurrence and the lower volumes. Therefore, the purpose of this research is to develop a suitable network screening method for LVRs. The literature review of this research identified a few existing network screening methods. A state-of-practice survey was also carried out in order to understand the LVR safety management practices across the United States. Then the identified methods were assessed for their suitability for LVRs. The method using a combination of crash frequency, severity and rate, and the Empirical Bayes (EB) method scored the highest. However, the EB method was selected for further analysis as it is not entirely dependent on historical crash experience and it incorporates risk factors. Actual LVR data from Oregon was used to analyze the EB method. This analysis indicated that the safety performance functions (SPFs) of the EB method overestimates the predicted crash numbers. This overestimation is mostly due to the high accident modification factors (AMF) for sharp horizontal curves. Finally, an alternative method was proposed. Two multiple linear regression models for estimating expected crashes mostly using risk factor categories were developed. The risk factor data were categorized using Classification and Regression Tree (CART) analysis. Both models have R square values of more than 0.90.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.Item Risk factors associated with high potential for crashes on low-volume roads(Montana State University - Bozeman, College of Engineering, 2016) Hossain, Fahmid; Chairperson, Graduate Committee: Ahmed Al-KaisyA significant portion of the roadway mileage in the U.S. is comprised of the low volume roads. As these roads experience very low crash frequencies, the identification of hazardous locations based on crash history alone is difficult. However, these low-volume roads may be associated with higher level of risks and consequently higher crash rates due to substandard geometry on these roads. Therefore, an approach to identify hazardous locations on low volume roads which accounts for geometric and roadside features as well as crash history seemed to be necessary. For this purpose, roadway data from Oregon's low volume roads and 10-years of crash data on the selected sample were collected and analyzed to identify the roadway geometric and roadside features that contribute to the crash occurrence. Length of the horizontal and vertical curves under 100 feet, degree of curvature over 30 degrees, vertical grade over 5 percent, lane width narrower than 11 feet, shoulder width of 0 feet, and driveway density of 5 driveways/mile were found as the most restrictive features contributing to higher crash rate. Based on these analyses a quantitative tool was developed for assessing the level of risk on low volume roads. The developed risk index, which is a function of roadway geometry, roadside features, traffic exposure, and crash history, is proactive in nature, as it does not rely heavily on crash occurrence in assessing crash risks. Application of the crash risk index on the three corridors of Oregon showed that, the use of risk index provides new information about the level of hazard along highway segments compared to using crash history alone. Economic feasibility of some potential low-cost safety countermeasures was analyzed to identify which countermeasures would ensure the maximum return on investments. Installation of the rumble strips, object markers, safety edge, centerline and edge-line markings were found to be most cost effective with benefit/cost ratio over 8. The same procedure can be followed by other states, with similar road and traffic conditions, to identify the contributing factors of crashes and identify the most-effective countermeasures to improve the safety of the road.Item Characterizing commercial vehicle safety in rural Montana(Montana State University - Bozeman, College of Engineering, 2001) Burke, Patricia WalshItem Identifying weather-related factors affecting crash severity and consequent response actions : a comparative analysis of the multinomial logit and ordered probit model forms(Montana State University - Bozeman, College of Engineering, 2001) Blomquist, Daniel Thomas