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In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes. However, few studies detected the factors unique to single and multiple crashes, and none have examined these underlying factors to severe truck crashes in conjunction with violation data. The current research assessed all of these factors using two approaches to improve truck safety. The first approach used ordinal logistic regression to investigate the contributory factors that increased the odds of severe single-truck and multiple-vehicle crashes, with involvement of at least one truck. The literature has indicated that past violations can be used to predict future violations and crashes. Therefore, the second approach used risky violations, related to truck crashes, to identify the contributory factors to the risky violations and truck crashes. Driver actions of failure to keep proper lane following too close and driving too fast for conditions accounted for about 40% of all the truck crashes. Therefore, the same violations as the aforementioned driver actions were included in the analysis. Based on ordinal logistic regression, the analysis for the first approach indicated that being under non-normal conditions at the time of crash, driving on dry-road condition and having a distraction in the cabin are some of the factors that increase the odds of severe single-truck crashes. On the other hand, speed compliance, alcohol involvement, and posted speed limits are some of the variables that impacted the severity of multiple-vehicle, truck-involved crashes. With the second approach, the violations related to risky driver actions, which were underlying causes of severe truck crashes, were identified and analysis was run to identify the groups at increased risk of truck-involved crashes. The results of violations indicated that being nonresident, driving offpeak hours, and driving on weekends could increase the risk of truck-involved crashes. This paper offers an insight into the capability of using violation data, in addition to crash data, in identification of possible countermeasures to reduce crash frequency.

Introduction:The number of truck-related injuries and deaths can be reduced by understanding the factors that contribute to the higher risk of truck-related crashes and violations. Truck drivers are at fault of more than 80% of all the truck crashes on Wyoming interstates, and the literature review indicated that in order to identify appropriate countermeasure to crashes, each crash type should be analyzed individually. The literature review also revealed that relationships exist between driving records and driver culpability in crashes.Method:This study employed two approaches to identify contributory factors to truck-at-fault fatal and injury crashes, and truck-related violations. Interstate 80, a Wyoming corridor in a mountainous area with one of the highest truck crash rates in the US, was selected as a case study. Only truck-at-fault crashes and specific types of truck-related violations were considered in this study. The analyses include two approaches. First, the logistic regression model was employed to explore vehicle, driver, crash, and environmental characteristics that contribute to truck-at-fault fatal and injury crashes. Second, truck violations were used as a proxy for truck crashes to examine the tendency to violate truck-related traffic laws in relation to driver and temporal characteristics. Based on the literature, only violations associated with higher risk of severe crashes were included in the analyses. The included violations accounted for more than 70% of all the violations.Result:This study considered more than 30 variables and found that only 10 variables impact truck-at-fault crashes. These factors included: gender, history of past violation, crashes involving multiple vehicles, exceeding the speed limit, occupant distraction, driver ejection, fatigued driving, non-seat belt usage, overturn, and head-on collision. Results of the second analysis indicated that both residency and time of crash are factors that impact truck-related violations. Results of the analysis also indicated that both residency and time of the crash are factors that impact truck-related violations.

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The usage of credit card has increased dramatically due to a rapid development of credit cards. Consequently, credit card fraud and the loss to the credit card owners and credit cards companies have been increased dramatically. Credit card Supervised learning has been widely used to detect anomaly in credit card transaction records based on the assumption that the pattern of a fraud would depend on the past transaction. However, unsupervised learning does not ignore the fact that the fraudsters could change their approaches based on customers' behaviors and patterns. In this study, three unsupervised methods were presented including autoencoder, one-class support vector machine, and robust Mahalanobis outlier detection. The dataset used in this study is based on real-life data of credit card transaction. Due to the availability of the response, fraud labels, after training the models the performance of each model was evaluated. The performance of these three methods is discussed extensively in the manuscript. For one-class SVM and auto encoder, the normal transaction labels were used for training. However, the advantages of robust Mahalanobis method over these methods is that it does not need any label for its training.