The U.S. construction industry represents approximately 4% of the U.S. gross domestic product (BEA 2015) and currently involves over 6 million workers employed by an estimated 750,000 construction firms (BLS 2015). Within this industry, productivity is a key driver for economic growth and strongly affects prosperity for the country (Vogl and Abdel-Wahab 2014). More specifically, higher construction productivity and more reliable installation (quality) translates into higher wages and increased profits (Vogl and Abdel-Wahab 2014). On many construction projects, productivity is defined or greatly impacted by equipment cycle time. Furthermore, the U.S. construction industry continues to be one of the more dangerous work environments for employees (BLS 2015). Construction workers in the U.S. experience a disproportionate number of fatalities when compared other major industrial sectors in the U.S. (BLS 2013). Visibility has proven to be a major cause of accidents on construction sites (Hinze and Teizer 2011). This research seeks to prove the hypothesis that visibility and location-based data can be automatically collected and analyzed for construction equipment operators to assess a construction equipment cycle. As one of the more promising recent implementations in the construction industry, sensing and design technology provide unique opportunities to capture and analyze location-based information on construction sites. These technologies can enable productivity managers to identify, assess, and decrease the overall cycle time of a specific operation. This research implements Building Information Modeling (BIM), Global Positioning System (GPS) location identification, and laser scanning to enable automated data collection and analysis. The overall objective of the research is to automatically capture and analyze elements of a construction equipment cycle. The outcomes of this research addresses the following key components of an equipment cycle time: 1) automated cycle time path planning, 2) location-based data capture and analysis of real-time equipment cycles, and 3) equipment path environment visualization. The research framework was tested with active construction site data, and feedback from the workforce and management was assessed and integrated into the research approach. The research has the potential to improve productivity on construction sites and enhance construction employee safety performance. It will also assist in adding a link between productivity planning and management and existing project BIMs.