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.

This paper explores the usability and user experience challenges of ARROWS, a novel Augmented Reality (AR) and Wearable Technology (WT) safety system for roadway work zones, an area with limited existing usability research. We utilized a mixed-method approach with two complementary experiments in indoor and outdoor settings, using the Wizard of Oz methodology and a high-fidelity prototype. We focused on identifying usability challenges, factors contributing to user experience, and the distinct needs of highway workers, documenting results using the System Usability Scale (SUS), Rating Scale Mental Effort (RSME), and a trust score. Participants rated the usability of ARROWS above average in both settings, while making a reasonable level of mental effort. The findings also indicate a significant correlation between perceived trust and usability, highlighting the importance of trust in user experience.

Establishing performance targets for marine maintenance and repair operations can be challenging for management due to the multitude of factors that can potentially influence productivity, efficiency, and manpower requirements. The aim of this study was to measure and evaluate the efficiencies of various maintenance and repair shipyard operations using data envelopment analysis (DEA). The use of DEA allows for multiple factors affecting maintenance productivity to be analyzed and allows the sources of inefficiencies in maintenance operations to be identified through examination of efficient or best practice decision making units, or DMUs. Results presented in this study were used to develop an overall strategic plan for enhanced decision-making with regard to labor and resource requirements and management strategies for the administration of the shipyards. Inefficient maintenance operations were identified through DEA evaluation and included the use of identified quantitative and qualitative factors. The use of quantified factors in assessing manpower needs was a beneficial approach for what is typically a subjective judgment in the determination of staffing and scheduling needs, organizational structure, and performance targets.

Transportation agencies constantly strive to tackle the challenge of limited budgets and continuously deteriorating highway infrastructure. They look for optimal solutions to make intelligent maintenance and repair investments. Condition prediction of highway assets and, in turn, prediction of their maintenance needs are key elements of effective maintenance optimization and prioritization. This paper proposes a novel risk-based framework that expands the potential of available data by considering the probabilistic susceptibility of assets in the prediction process. It combines a risk score generator with machine learning to forecast the hotspots of multiple defects while considering the interrelations between defects. With this, we developed a scalable algorithm, Multi-asset Defect Hotspot Predictor (MDHP), and then demonstrated its performance in a real-world case. In the case study, MDHP predicted the hotspots of three defects on paved ditches, considering the interrelation between paved ditches and five nearby assets. The results demonstrate an acceptable accuracy in predicting hotspots while highlighting the interrelation between adjacent assets and their contribution to future defects. Overall, this study offers a scalable approach with contribution in data-driven multi-asset maintenance planning with potential benefits to a broader range of linear infrastructures such as sewers, water networks, and railroads.