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Connected and automated trucks (CATs) have the potential to transform the transportation system and logistics industry. Their unique features, such as operational strategies and truck driving behaviors, can affect transportation system performance. For successful development, testing and deployment of CATs, analysis, modeling, and simulation (AMS) plays an important role, especially in evaluating the impacts of CAT technologies on existing transportation systems. This paper presents a comprehensive review and assessment of up-to-date studies related to CAT AMS, focusing on three correlated elements: CAT applications, data, and tools. The research delves into CAT applications from individual CAT and CAT fleet to CAT-involved traffic. It explores available data sources relevant to CAT system use cases, assessing their potential issues and opportunities. The study also reviews existing AMS tools used to analyze CAT applications at both operational performance and network integration levels, emphasizing research needs in CAT-specific tools development. The findings identify the data needs and point out that existing AMS tools may not capture the complexity of CAT operation, which involves driving behaviors, vehicle-to-everything communications, autonomous capabilities, and response to truck-specific scenarios. The study will lay a solid foundation for further development of the AMS framework for CATs and provide guidance to future research of CAT applications. 

In projects centered around rare event case data, the challenge of data comprehension is greatly increased because of insufficient data for deriving insight and analysis. This is particularly the case with traffic crash occurrence, where positive events (crashes) are rare and, in most cases, no data set exists for negative events (non-crashes). One method to increase available data is negative sampling, which is the process of creating a negative event based on the absence of a positive event. In this work, four negative sampling techniques are presented with varying ratios of negative to positive data. These types of techniques are based on spatial data, temporal data, and a mixture of the two, with the data ratios acting as class balancing tools. The best performing model found was with a negative sampling technique that shifted temporal information and had an even 50/50 data split, with an F-1 score, a formulaic combination of precision and recall, of 93.68. These results are promising for Inteligent Transportation Systems (ITS) applications to inform of potential crash locations in an entire area for proactive measures to be put in place.