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Inspection Technologies for Construction and Maintenance of Highway Infrastructure – Review and Analysis
Get PDFThe Federal Highway Administration (FHWA) and state Departments of Transportation (DOTs) seek to provide high quality infrastructure that meets or exceeds the desired quality standards crucial for ensuring safety and efficiency in our nation\u27s transportation systems. Efficient inspection practices—which enable conformance with plans, specifications, and quality standards—are an essential part of this effort. In this digital age, state DOTs are relying on emerging technologies to improve inspection practices. However, there is limited knowledge available regarding various emerging technologies used for highway infrastructure construction and maintenance inspection. This study identifies various emerging technologies that are implemented at the state DOTs and their uses/applications for various inspection purposes. The emerging technologies studied in this project include Remote Sensing and Monitoring Technologies—specifically, Unmanned Aerial Systems (UASs) and Light Detection and Ranging (LiDAR)—as well as Building Information Modelling (BIM), and Augmented Reality and Virtual Reality (AR/VR). Based on a comprehensive literature review and content analysis of journal articles, technical reports, state DOT documents/reports, templates, and guidelines on the identified technologies, the research findings indicate different usage levels among state DOTs and found that DOTs use these technologies for applications such as structural inspection, verifying quantities, bridge and visual inspection, safety inspection, inspection workforce training, inspection documentation, and measurements of pay quantities, among others. This study serves as a valuable resource for state DOTs seeking to maximize the benefits of their investments and embrace innovative inspection technologies to ensure safety and efficiency in the nation\u27s transportation infrastructure
Survey of Building Information Modeling for Infrastructure (BIM4I)
Get PDFThe rapid development of information technologies is transforming how data and information are produced, shared, exchanged, and managed. This transformation is accelerating in state departments of transportation (DOTs) across the country due to the pressing need for efficient means of delivering transportation projects and an enhanced need for internal and external collaboration. A key driver for this transformation is the implementation of Building Information Modeling for Infrastructures (BIM4I). The primary objective of this research was to develop actionable recommendations for DOTs to facilitate the effective adoption of BIM4I, based on national and international lessons learned and best practices. A four-step methodology was employed including: (1) a literature review identified key stakeholders and best practices; (2) data collection targeting transportation agencies included a survey of 94 participants and 18 follow-up interviews; (3) data analysis utilized statistical and content analysis to extract themes and insights; and (4) tailored recommendations were formulated based on findings. Main recommendations include: • Strategic Planning: Establish a clear definition of Building Information Modeling (BIM), create an implementation plan including a roadmap with defined objectives, and assess organizational readiness for BIM adoption. • Standardization and Training: Develop clear standards and guidelines for BIM usage, prioritize data quality, and invest in training. • Technology Integration: Ensure that BIM tools and software are compatible with existing systems and establish a user-friendly Common Data Environment (CDE) for effective data sharing. • Collaboration and Communication: Foster interdepartmental and cross-stages collaboration and engage stakeholders early in the design process to enhance understanding of project impacts. Recommendations from this research will help DOTs transitioning to digital delivery to enhance efficiency, collaboration, and project outcomes, providing a framework for effective BIM integration
Fences on the Epistemological Prairie: A Settler Colonial Approach to “Religion and Science”
Get PDFBuilding on the idea of religion and science as conceptual maps of intellectual territory, I use a settler colonial analysis as a framework for thinking about decolonizing religion and science in a way that moves away from abstraction and towards action; addressing not just the ideas, but the tools of control—the fences—that impose ideas on the territory itself. Comparing the Wyoming prairie with the epistemological prairie, I describe the maps, fences and other tools and technologies of settler colonialism used to appropriate Indigenous Land and knowledge, eventually turning it into private property. It is in this last step—the creation of private property—that fences are most important, because they are tools of ownership that do not merely restrict access to parts of the prairie (land and knowledge), but restrict movement on the prairie itself. I describe patents and intellectual property as examples of fences on the epistemological prairie. Because they are legally and historically connected to technologies of settler colonial appropriation of land—including terra nullius and land patents—they are an excellent example of the connection between land and epistemological territory, and show what epistemological decolonization can look like in practice
The potential for fuel reduction to reduce wildfire intensity in a warming California
Get PDFIncreasing fuel aridity due to climate warming has and will continue to increase wildfire danger in California. In addition to reducing global greenhouse gas emissions, one of the primary proposals for counteracting this increase in wildfire danger is a widespread expansion of hazardous fuel reductions. Here, we quantify the potential for fuel reduction to reduce wildfire intensity using empirical relationships derived from historical observations with a novel combination of spatiotemporal resolution (0.375 km, instantaneous) and extent (48 million acres, 9 years). We use machine learning to quantify relationships between sixteen environmental conditions (including ten fuel characteristics and four temperature-affected aridity characteristics) and satellite-observed fire radiative power. We use the derived relationships to create fire intensity potential (FIP) maps for sixty historical weather snapshots at a 2 km and hourly resolution. We then place these weather snapshots in differing background climatological temperature and fuel characteristic conditions to quantify their independent and combined influence on FIP. We find that in order to offset the effect of climate warming under the SSP2-4.5 emissions scenario, fuel reduction would need to be maintained perpetually on ∼3 million acres (or 600 000 acres per year, 1% of our domain, at a 5 year return frequency) by 2050 and ∼8 million acres (or 1.6 million acres per year, 3% of our domain, at a 5 year return frequency) by 2090. Overall, we find substantial potential for fuel reduction to negate the effects of climate warming on FIP
Insights for the Future of Car Rental and Ridesharing: Driving Behavior Across Different Levels of Automation
Get PDFAutonomous vehicles are reshaping the car rental and ridesharing industries, potentially leading to a unified model of on-demand transportation suitable for both uncommon (e.g., business trips) and daily commuting. An exploratory study of human behavior towards autonomous vehicles can uncover the challenges and opportunities inherent in different levels of vehicle automation. This study aims to (a) identify behavioral differences in drivers operating vehicles at various levels of automation and (b) explore how these behaviors vary with different assistance feature styles, specifically between risky and conservative modes. Human-subject experiments were conducted among twelve participants (aged 21 to 29, including four women) to complete simulated driving trials under different levels of automation (Levels 0, 3, and 5), assistance features (risky and conservative modes), and driving activities (lane keeping and lane changing). Measures of driving performance, body posture, and eye movement were recorded during each trial. The data implied that: (1) driving performance: drivers exhibited stable speed and steering control at Levels 0 and 5, while speed decreased and steering variability increased obviously at Level 3; (2) driving posture: a tense posture was noted at Level 0, with potential posture preparation needed for takeover actions at Level 3; (3) eye movement: active scanning and continuous control were maintained at Level 0, with notable shifts in attention at Levels 3 and 5. Further research could focus on conducting on-road tests, using equipment designed for on-road tests and broadening the demographic range of participants
Does the Transit Industry Understand the Risks of Cybersecurity and are the Risks Being Appropriately Prioritized?
Get PDFThe intent of this study is to assess the readiness, resourcing, and capabilities of public transit agencies to detect, identify, be protected from, respond to, and recover from cybersecurity vulnerabilities and threats. This study is an update of the 2020 Mineta Transportation Institute (MTI) study, “Is the Transit Industry Prepared for the Cyber Revolution? Policy Recommendations to Enhance Surface Transit Cyber Preparedness.” In the previous study, the authors found that the transit industry was ill-prepared for cybersecurity attacks. Unfortunately, after four years and the development of new, and often free, resources, the situation has not markedly improved. In fact, this survey, which included a larger number of small rural transit agencies, shows that they lag far behind their larger peers. The increasing sophistication of cybercriminals, in combination with a greater reliance on technology within the transit industry, puts the industry at greater risk than in 2020. This study reviews and updates the state of best cybersecurity practices in public surface transit; outlines U.S. public surface transit operators’ cybersecurity operations and the resources available to them; reviews U.S. policy on cybersecurity in public surface transportation; and provides policy recommendations that address gaps or identify issues for Congress, the Executive Branch, public surface transit agencies, and their associations and other supporting organizations. Research methods include an online survey and oral interviews with public surface transit agencies in the United States as well as oral interviews with members of the Executive Branch (e.g., the U.S. Department of Transportation, the U.S. Department of Homeland Security), as well as research of literature published in periodicals. There is an exponentially expanding gap between the cybersecurity preparedness that should exist and the growing threats from increased reliance on technology and the opportunities by malicious actors. This research provides information that can be used to help close that gap
Spartan Daily, May 7, 2025
Get PDFVolume 164, Issue 42https://scholarworks.sjsu.edu/spartan_daily_2025/1041/thumbnail.jp
Book Review: Creating Inclusive Libraries By Applying Universal Design: A Guide by Carli Spina
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Traffic Forecasting with VSET-Nets: A VGAE Spatial Embedding for Temporal Networks Approach
No full textTraffic forecasting is important for improving transportation systems by enabling better traffic management, congestion reduction, and urban planning. However, predicting traffic accurately is challenging due to the strong spatial dependencies between different road segments and the temporal changes in traffic patterns over time. Traditional time-series and graph models often struggle to capture both of these aspects effectively. In response, recent research has focused on temporal graph representation learning methods that jointly consider spatial relationships and temporal features in networks. This project proposes a hybrid model called VSET-Nets (VGAE Spatial Embedding for Temporal Networks) that employs Variational Graph Autoencoders (VGAEs) for learning spatial embeddings and Convolutional Neural Networks (CNNs) for capturing temporal features at each node. The model was evaluated using a benchmark dataset, containing traffic speed data collected from sensors in California’s District 7. Experimental results show that almost all of the node-specific regressors trained using our approach outperformed the baseline GCN model, with around 95% of nodes achieving better forecasting accuracy. These findings suggest that modular spatio-temporal modeling can offer a promising direction for building more effective traffic forecasting systems