This report details the activities conducted to assess the feasibility of using new technology tools for safety training. Utilizing established research studies, risk frameworks, and vendor quotations, we compared the different attributes of training methods such as Traditional Training (classroom/presentations), LMS (Learning Management System) based gamification, Computer Simulation, Virtual Reality (VR), and Augmented Reality (AR). The anticipated benefits include improved training program development, higher interactivity and long-term retention, and the chance to reduce work zone risk. The project was divided in three phases, and the following are our four key takeaways.
(1) Quality of Safety Training: Benchmarking training practices provided strong evidence that participative programs, such as role plays, demonstrations of safety devices, and risk mapping are some of the best practices. Additionally, training engineers on work zone design, auditing, and recording safe work zones can influence project attributes, such as the length and duration of work zone. Including all these aspects during the project planning phase has a greater chance of influencing work zone safety.
(2) Effectiveness of New Technology Tools: Our vendor outreach project phase allowed us to determine the different attributes in training course development and customer experience using new technology tools. Established research studies provided significant support to our hypothesis that new technology tools are more effective and interactive compared to traditional learning.
(3) Risk-Based Approach to Training: Analyzing the risk index for work zone attributes indicate the degree of risk that a worker faces while performing a task characterizing those attributes. We concluded that implementation of new technology tools should be planned based on this risk index and optimization model. This will ensure better worker performance and perception of the course content in alignment with the severity of that work attribute.
(4) Optimizing Selection of Training Tools for Tasks: We provide an optimization model to choose the optimal mix of training tools to attain the desired level of risk reduction. The tool is spreadsheet-based and shows the benefit of using a portfolio of modules across training tools, each targeted at attaining the desired risk reduction by attribute for a task. By using the risk reduction due to training tools from the literature, the cost data from vendors and task characteristics, this tool can enable INDOT managers to manage risk cost efficiently
