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The purpose of this study was to analyze the effectiveness of simulation learning techniques within both face-to-face and online courses. The specific objective for this study was to answer two questions: (1) What are the specific benefits the simulation learning component adds to the course(s)? (2) How do students perceive the usefulness of the simulation learning component to their prepared readiness to enter the industry?

An open-ended survey was administered at the end of the course to conduct a content analysis of student perspectives of the incorporation of cloud-based, educational simulation learning into educational courses. A discussion of the students' perspective of the SIM labs benefits, ease of use and perceived usefulness of this trending learning component has been reviewed, along with the comparison of the online and face-to-face viewpoints.

Some of the identified successes were the ability to collaborate between online and face-to-face classes. Another was the ability to incorporate the application and decision-making components of the textbook into their virtual position of the simulation (SIM) learning lab from an owner's/general manager's perspective. Finally, the SIM labs provided the instructor with a measurable tool to have students compete in a healthy environment.

Valuable insights were gained into the student's perspective and helped in needed adjustments to better utilize this type of active learning. By studying a specific simulation learning component of this type of electronic learning (e-learning,) valuable contextual explanations to support the other types of active learning techniques mentioned above can be gained.

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns.

Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions.

Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others.

Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.