Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
Browse
2 results
Search Results
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 This is mostly because of higher speeds and outdated infrastructure designs.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 An investigation modeling risk of wildlife-vehicle collisions in Montana, USA(Montana State University - Bozeman, College of Engineering, 2019) Bell, Matthew Andrew; Chairperson, Graduate Committee: Yiyi Wang and Damon Fick (co-chair)Road ecologists and transportation engineers have been exploring new methods to adapt to the environmental and motorist safety concerns involving wildlife-vehicle collisions. There are over one-million crashes with large-bodied wildlife every year in the U.S. that result in substantial property damage and personal injuries. Recent studies modeling these collisions identify where they cluster, and the landscape, road, and driver characteristics that influence the likelihood of a collision along short road corridors and small geographic regions. This research expands on current knowledge and attempts to model the risk of wildlife-vehicle collisions on a large geographic scale. This research investigates different analysis methods and creates predictive models that will estimate the risk of a wildlife-vehicle collision as drivers travel across multiple ecosystems. Different analysis units were created to extract two similar datasets that are modeled against two different response variables -- reported collisions and roadkill locations. Regularization is used to help with feature selection. Negative binomial regression models are built to predict risk. Random forest machine learning helps better understand the percent of variance explained by the variables in each model. A range of statistical measurements were taken to compare the non-nested models. The best performing model is applied to the seasonal division of data. Yearlong and seasonal risk is mapped onto the road network and color-coded to show the differences in risk on Montana's road network. The maps capture the changes in risk throughout the year, they generally match where wildlife-vehicle collisions actually happen, and even coincides with published work on the locations of collision hotspots in Montana. This research is the basis for future complex real-time risk-mapping models that can be integrated into smart technology and developed into on-board driver alert systems. With the advancements of autonomous vehicle, it is possible to incorporate real-time driving data into models that will analyze wildlife-vehicle collision risk based on vehicle location, season, time of day and driving habits. This can increase driver safety by informing them when they are traveling in areas where wildlife-vehicle collisions are more likely to happen, and can be especially helpful while driving on unfamiliar roads.