Omidreza Shoghli

Omidreza Shoghli, Fatemeh Banani Ardecani, Amin Mohamadi Hezaveh

Generative AI tools are increasingly being piloted in public agencies, but limited evidence explains how employee acceptance changes after hands-on use. This study examines Microsoft 365 Copilot adoption during an eight-week pilot at a state Department of Transportation. A matched two-wave survey measured perceived usefulness, perceived ease of use, behavioral intention, and trust before and after participation. After matching and response-quality screening, the sample included 124 employees. Nonparametric tests assessed aggregate changes, k-means clustering identified baseline acceptance personas, and fixed-centroid assignment tracked migration. Open-ended responses were examined using keyword-based content mapping. Perceived usefulness declined significantly after use, suggesting recalibration of expectations, while perceived ease of use, behavioral intention, and trust showed only small, nonsignificant changes. Three baseline personas emerged: Skeptics, Cautiously Positive users, and Champions. Although persona counts changed modestly, individual movement was substantial: 40 percent of Skeptics moved to Cautiously Positive, while 68 percent of Champions moved to less enthusiastic personas. Upward movement was associated with gains in usefulness, behavioral intention, and trust; downward movement was associated with declines in usefulness and trust. Communication and summarization remained stable use cases, while data, chart, and presentation tasks declined. Accuracy and privacy concerns decreased, but job and skills concerns increased. Public-sector AI adoption should be monitored dynamically and supported through persona-specific training, workflow examples, verification routines, and trust-calibration safeguards. The study offers a framework for tracking workforce heterogeneity during enterprise generative AI implementation.

Sepehr Sabeti, Fatemeh Banani Ardecani, Omidreza Shoghli

Given the aging highway infrastructure requiring extensive rebuilding and enhancements, and the consequent rise in the number of work zones, there is an urgent need to develop advanced safety systems to protect workers. While Augmented Reality (AR) holds significant potential for delivering warnings to workers, its integration into roadway work zones remains relatively unexplored. The primary objective of this study is to improve safety measures within roadway work zones by conducting an extensive analysis of how different combinations of multimodal AR warnings influence the reaction times of workers. This paper addresses this gap through a series of experiments that aim to replicate the distinctive conditions of roadway work zones, both in real-world and virtual reality environments. Our approach comprises three key components: an advanced AR system prototype, a VR simulation of AR functionality within the work zone environment, and the Wizard of Oz technique to synchronize user experiences across experiments. To assess reaction times, we leverage both the simple reaction time (SRT) technique and an innovative vision-based metric that utilizes real-time pose estimation. By conducting five experiments in controlled outdoor work zones and indoor VR settings, our study provides valuable information on how various multimodal AR warnings impact workers reaction times. Furthermore, our findings reveal the disparities in reaction times between VR simulations and real-world scenarios, thereby gauging VR's capability to mirror the dynamics of roadway work zones. Furthermore, our results substantiate the potential and reliability of vision-based reaction time measurements. These insights resonate well with those derived using the SRT technique, underscoring the viability of this approach for tangible real-world uses.

Vinit Katariya, Mohammadreza Baharani, Nichole Morris et al.

Vehicle trajectory prediction is essential for enabling safety-critical intelligent transportation systems (ITS) applications used in management and operations. While there have been some promising advances in the field, there is a need for modern deep learning algorithms that allow real-time trajectory prediction on embedded IoT devices. This article presents DeepTrack, a novel deep learning algorithm customized for real-time vehicle trajectory prediction and monitoring applications in arterial management, freeway management, traffic incident management, and work zone management for high-speed incoming traffic. In contrast to previous methods, the vehicle dynamics are encoded using Temporal Convolutional Networks (TCNs) to provide more robust time prediction with less computation. DeepTrack also uses depthwise convolution, which reduces the complexity of models compared to existing approaches in terms of model size and operations. Overall, our experimental results demonstrate that DeepTrack achieves comparable accuracy to state-of-the-art trajectory prediction models but with smaller model sizes and lower computational complexity, making it more suitable for real-world deployment.