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69A3551747119, National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. NTL staff last accessed this dataset at its repository URL on 2023-12-06. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time., The advent of Digital Twin (DT) technology signifies a significant stride towards the progressive transformation from physical to digital paradigms within the realm of civil engineering. Its predecessors, namely Computer-Aided Drafting (CAD) and Building Information Modeling (BIM), have revolutionized the industry by streamlining the documentation of designs, curtailing both time and cost investments. The primary objective of this research was to explore different ways to create Digital Twins for civil infrastructure and to explore the effectiveness of DT technology as a tool to create new visualizations and understand interactions between transportation and other related civil infrastructure systems. We used The University of Texas at El Paso (UTEP) campus as a living lab by creating DT models based on transportation network, structural modelling, and LiDAR scans of different parts of the campus. The transportation network and 3D model of the entire campus was combined for traffic simulation and real-time sensing at a roundabout, while the digital model of a pedestrian bridge was made for structural simulations with provisions for strain and tilt sensors. In addition, LiDAR scans of campus buildings were integrated into virtual reality (VR) applications for visualization in VR headsets., The total size of the zip file is 1.62 GB. Desktop Services Store files, or DS_STORE Files, are Mac OS files that stores desktop attributes such as icons and colors. They are only used by Mac OSs and don't require additional software (for more information on .ds_store files and software, please visit https://www.file-extensions.org/ds_store-file-extension). The .mp4 file extension is used mainly for the MPEG-4 multimedia file format. These .mp4 files are in a container format defined in the Systems Part of the MPEG-4 standard (ISO 14496-1). The .mp4 files support all kinds of multimedia content (multiple audio streams, video streams, subtitle streams, pictures) and advanced content (called "Rich Media" or "BIFS") like 2D and 3D graphics, user interactivity, DVD-like menus. The MP4 file format is also streamable (for more information on .mp4 files and software, please visit https://www.file-extensions.org/mp4-file-extension). The .txt file type is a common text file, which can be opened with a basic text editor. The most common software used to open .txt files are Microsoft Windows Notepad, Sublime Text, Atom, and TextEdit (for more information on .txt files and software, please visit https://www.file-extensions.org/txt-file-extension). Polygon graphics files, .ply, are simple objects that were designed to be an accessible format for polygon models. These files are typically created in Blender (for more information on .ply files and software, please visit https://www.file-extensions.org/ply-file-extension). DWG files are files created in AutoCAD, a CAD software for Microsoft Windows and Apple Mac developed by Autodesk Incorperated. Typcally used in CAD drawings, this file format stores two and three dimensional data (for more information on .dwg files or software, please visit https://www.file-extensions.org/dwg-file-extension). The .rtd file format is used by the software Autodesk Robot Structural Analysis Professional and typically stores designs in the program (fo

69A3551747124, Document describing sensors and sensor algorithms relating to the implementation process of the project’s detection technology. The purpose of this project is to place edge computing devices Jetson Xavier NX and cameras on the fourth and fifth floors of the Schuster Garage on the UTEP campus, then use Yolov5 [3] and DeepSORT [4] to complete object detection and tracking tasks at the roundabout near the UTEP campus and get real-time traffic flow information in all directions including the trajectory of each vehicle.

69A3551747119, The advent of Digital Twin (DT) technology signifies a significant stride towards the progressive transformation from physical to digital paradigms within the realm of civil engineering. Its predecessors, namely Computer-Aided Drafting (CAD) and Building Information Modeling (BIM), have revolutionized the industry by streamlining the documentation of designs, curtailing both time and cost investments. The primary objective of this research was to explore different ways to create Digital Twins for civil infrastructure and to explore the effectiveness of DT technology as a tool to create new visualizations and understand interactions between transportation and other related civil infrastructure systems. We used The University of Texas at El Paso (UTEP) campus as a living lab by creating DT models based on transportation network, structural modelling, and LiDAR scans of different parts of the campus. The transportation network and 3D model of the entire campus was combined for traffic simulation and real-time sensing at a roundabout, while the digital model of a pedestrian bridge was made for structural simulations with provisions for strain and tilt sensors. In addition, LiDAR scans of campus buildings were integrated into virtual reality (VR) applications for visualization in VR headsets.

Agreement T1461, Task 05, DRIVE Net is a region-wide, Web-based transportation decision support system that adopts digital roadway maps as, the base, and provides data layers for integrating and analyzing a variety of data sources (e.g., traffic sensors, incident, records). Moreover, DRIVE Net offers a platform for streamlining transportation analysis and decision making, and it, serves as a practical tool for visualizing historical observations spatially and temporally. In its current implementation, DRIVE Net demonstrates the potential to be used as a standard tool for incorporating multiple data sets from different, fields and as a platform for real-time decision making. In comparison with the previous version, the new DRIVE Net, system is now able to handle more complex computational tasks, perform large-scale spatial processing, and support, data sharing services to provide a stable and interoperable platform to process, analyze, visualize, and share, transportation data., DRIVE Net’s capabilities include generating statistics for WSDOT’s Gray Notebook (GNB), including travel times, throughput productivity, and traffic delay calculations for both general purpose and HOV lanes, each of which are, important performance indicators in the WSDOT congestion report. The DRIVE Net system includes robust loop, detector data processing and quality control methods to address the data quality issues impacting loop detectors, throughout the state. The capabilities of the DRIVE Net system have been expanded to include safety modeling, hotspot identification, and incident induced delay estimation. Specifically, the Safety Performance module includes, functions that can be used to obtain traffic incident frequency, apply predictive models to estimate the safety, performance of road segments, and visualize and compare observed incident counts and different predictive models., Additionally, a module providing multi-modal data analysis and visualization capabilities was developed as a pilot, experiment for integration of heterogeneous data. This module includes pedestrian and bicycle, public transit, park and, ride, Car2Go, and ferry data downloading and visualization. DRIVE Net now offers role-based access control, such, that access privileges to different functions and data resources can be assigned on a group or individual basis.