In junction traffic operations, vehicle delay is one of the most essential performance measures of effectiveness. It allows traffic engineers to assess the performance of a traffic system component or the efficacy of a system-wide control plan. Real-time applications such as adaptive signal control, congestion management, and dynamic traffic assignment often use this technology. Obtaining real-time data on intersection performance, such as control delay, may be time-consuming and labor-intensive. This study presents a new approach for estimating network-level real-time delay from passive traffic counting. Total Travel Delay Estimation Technique (TTD) is proposed for signalized intersection delays that can be computed by examining real-time data from arrival and departure detectors upstream and downstream of a junction. The proposed estimation method mathematically manipulated equations that relate the input-output model and vehicle O–D data acquired from the Automatic Turning Movement Identification System (ATMIS). The developed methods utilize the obtained real-time traffic detection system as input data. The proposed methods are applied for three cases: simple, semi-generalized, and generalized networks, where any of them can be used as a building TTD estimation block for the whole actual network. Results from the TTD were compared to VISSIM output, and a statistical test was conducted under varying traffic conditions (low, medium, high, and saturated). The findings show that the proposed methodology can yield stable and reliable results in various traffic volumes and turning movement conditions. Future field implementation studies for the suggested methods are recommended to evaluate the model’s reliability and efficacy in real-time traffic scenarios.

Traffic control and management applications require the full realization of traffic flow data. Frequently, such data are acquired by traffic sensors with two issues: it is not practicable or even possible to place traffic sensors on every link in a network; sensors do not provide direct information about origin–destination (O–D) demand flows. Therefore, it is imperative to locate the best places to deploy traffic sensors and then augment the knowledge obtained from this link flow sample to predict the entire traffic flow of the network. This article provides a resilient deep learning (DL) architecture combined with a global sensitivity analysis tool to solve O–D estimation and sensor location problems simultaneously. The proposed DL architecture is based on the stacked sparse autoencoder (SAE) model for accurately estimating the entire O–D flows of the network using link flows, thus reversing the conventional traffic assignment problem. The SAE model extracts traffic flow characteristics and derives a meaningful relationship between traffic flow data and network topology. To train the proposed DL architecture, synthetic link flow data were created randomly from the historical demand data of the network. Finally, a global sensitivity analysis was implemented to prioritize the importance of each link in the O–D estimation step to solve the sensor location problem. Two networks of different sizes were used to validate the performance of the model. The efficiency of the proposed method for solving the combination of traffic flow estimation and sensor location problems was confirmed from a low root-mean-square error with a reduction in the number of link flows required.

In the context of critical energy infrastructures (e.g., hydrogen infrastructure) that extensively utilize power converters, the need for reliable and accurate monitoring is of paramount importance. Addressing this necessity, this paper presents a novel GPU-based multivariate approach to Insulated Gate Bipolar Transistor (IGBT) lifetime prediction. Despite the substantial technological advances in the field, accurately predicting the lifetime of IGBTs remains a significant challenge. Current methods often rely on single precursor variable models, which can lack the precision required in demanding power electronic applications. In contrast, this study utilizes multiple precursor variables (V _CE(ON) and case temperature) to achieve more accurate results. Initial results using NASA's open-source dataset, and Gaussian Process Regression (GPR) reveal that our multivariate model outperforms its single-variable counterparts in …