Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.

As urban populations grow and freeway congestion worsens, the deployment of Autonomous Vehicles (AVs) and Connected Autonomous Vehicles (CAVs) presents new opportunities for improving mobility, roadway efficiency, and safety. This dissertation proposes an object-driven, discrete-event simulation framework to investigate the effectiveness of centrally coordinated AV platooning strategies in complex, mixed traffic environments. The research is motivated by the need to alleviate traffic congestion through intelligent control, reduce environmental and economic costs, and provide actionable insights for infrastructure planning.The study develops a novel Object-Driven Cellular Automata (ODCA) model that supports fine-grained simulation of AV and Human-Driven Vehicle (HDV) interactions at the cell level. The model captures merging, diverging, lane-changing, and platooning behaviors across multi-lane freeway segments with onramps and offramps. A central contribution lies in the implementation of destination-based platooning strategies, which group AVs by destination proximity to improve cohesion and reduce exit-related disruptions. The model supports varying AV penetration rates, platoon formations, and infrastructure configurations such as dedicated AV lanes and dedicated platoon segments.
Simulation results demonstrate that while naive or partially coordinated platooning yields limited benefit at low AV penetration, destination-based coordination under high AV presence significantly reduces travel time, delay, and lane-changing turbulence. Additionally, infrastructure support, particularly short, dedicated platoon zones upstream of offramps, offers a scalable alternative to full-lane reservation. These findings highlight the threshold effects of AV market share on freeway performance and emphasize the need for smart integration between vehicle coordination and infrastructure design.
This work advances traffic flow theory by extending the cellular automata paradigm to object-driven logic, incorporating both microscopic vehicle dynamics and macroscopic system performance. The results offer practical guidance for policymakers and transportation agencies planning for the gradual transition to mixed and fully autonomous traffic systems.