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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>IoT Sensor Fusion for Low-Cost Cloud Based Monitoring for Resilient Levees and Embankments</title>
      <link>https://rip.trb.org/View/2536170</link>
      <description><![CDATA[The performance and longevity of geo-infrastructure assets such as levees and highway embankments depend on geotechnical (embankment, foundations, slopes) components, both influenced by soil conditions, hydraulic loads, and disruptions due to weather. Continuous, data-driven monitoring is essential for reliable water resource management and disaster resilience. This research advances Geotechnical Asset Management (GAM) using advanced Internet of Things (IoT)-based inertial measurement unit (IMU) sensors installed onsite combined with periodic aerial LiDAR point-cloud data collection techniques. IoT-based IMU sensors will track multi-directional displacements, while accelerometers and vibration sensors will capture performance data under various conditions. An earth dam and highway embankment site in Jackson, Mississippi, and a Levee section owned by the United States Army Corps of Engineers (USACE) will serve as test locations. A 3D geospatial model combining drone-mounted LiDAR will track structural stability and environmental impacts. Periodic assessments will detect instability, settlement, and deformation, enabling proactive maintenance to prevent failures and minimize disruptions. Enhanced monitoring will ensure reliable, connected, and risk-mitigated infrastructure to support national economic competitiveness. Collected data will be transmitted to the Amazon Web Services (AWS) cloud for remote monitoring of the embankment, dam and levee system. In addition, the analytical tools in the cloud platform will be used to analyze the data and identify threshold points based on the performance criteria to create an early detection of failure under extreme conditions. This project will develop a data-driven, scalable solution to enhance safety, efficiency, and resilience in water management infrastructure while strengthening investments, thus enabling US economic strength and global competitiveness.]]></description>
      <pubDate>Wed, 09 Apr 2025 18:28:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2536170</guid>
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      <title>Exploring Hybrid Architecture for Data Processing/Storage Needs of FDOT</title>
      <link>https://rip.trb.org/View/2384742</link>
      <description><![CDATA[Objective 1: The first step towards moving to a hybrid cloud architecture is to understand the basics of the cloud technology, encompassing operational mechanisms, key characteristics, and diverse service models. The research team will study and document different cloud services, explore mixed storage architectures, data localization per Cloud Procurement and Contractual Elements, Rule 60GG-4.002(6), Florida Administrative Code, and analyze the potential benefits in terms of efficiency, security, and feasibility. The goal is to provide useful insights and knowledge to help Florida Department of Transportation (FDOT) Transportation Systems Management and Operations (TSM&O) choose the best cloud services and storage setups for its needs. With this detailed documentation, FDOT will be better equipped to make informed decisions and use cloud technologies to improve its operations. Objective 2: The second objective is to thoroughly examine and analyze other DOTs’ practices regarding migration to cloud-based services. This will result in valuable insights that can guide FDOT TSM&O’s move to cloud-based services. By closely studying the experiences, best practices, challenges, and lessons learned from these agencies, the research team aims to enhance the efficiency, security, and effectiveness of FDOT TSM&O’s cloud-based initiatives. The primary aim is to provide a comprehensive overview of cloud adoption strategies, service models, deployment scales, security considerations, and associated challenges, ultimately informing and enriching FDOT’s cloud migration endeavor. Objective 3: This objective aims to develop a holistic framework that facilitates the successful migration of applications to cloud-based environments. The informed decision-making in transitioning legacy and new applications to the cloud environment should consider various aspects, including cost analysis for both importing and exporting for each cloud provider, technology compatibility, functionality, latency, recoverability, exit strategies, and security. This objective encompasses the creation of a hybrid cloud migration decision framework tailored to evaluate applications for cloud migration suitability. Furthermore, this involves designing a cloud migration and change management plan, outlining phased processes, from data migration to testing and validation. The training guidelines will empower existing personnel, bridging the knowledge gap while transitioning from legacy platforms to cloud technology and ensuring efficient and effective utilization of the newly adopted cloud-based services.]]></description>
      <pubDate>Mon, 03 Jun 2024 14:13:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2384742</guid>
    </item>
    <item>
      <title>Real-time Safety Diagnosis System for Connected Vehicles with Parallel Computing Architecture (Project O6)</title>
      <link>https://rip.trb.org/View/2004707</link>
      <description><![CDATA[The ongoing STRIDE F4 project – Automatic Safety Diagnosis in Connected Vehicle Environment – is to construct a computational pipeline of a near-crash diagnosis system to identify near-crash events by processing the Basic Safety Messages (BSMs) generated in the Connected Vehicle (CV) environment on the individual level. The in-vehicle system identifies outliers by analyzing BSMs from nearby vehicles and comparing with each individual driver’s past normal driving pattern provided by the Traffic Management Center (or a cloud server). The speed of data processing and transmission at both the cloud system and the in-vehicle system can be quite demanding. For the near-crash warning signal to be generated promptly in real-time environment, parallel computing is indispensable. The parallel computing technology can be incorporated into both the cloud system and the in-vehicle system.
First, the amount of BSMs received by the cloud server from the CVs could be massive up to several hundreds of GBs/sec. The data collection, data updating and warning massage broadcasting at the cloud server and the in-vehicle system can be carried out in a parallel fashion by using parallel computing. Second, vehicles are equipped with small computers to analyze the BSMs from all nearby vehicles. The in-vehicle data processing can also be accelerated by parallel computing.
The research team proposes to continue their current research using the parallel computing technology to accelerate the data processing and analysis in both the cloud system and the in-vehicle system. The group has extensive experience in parallel computing in solving large-scale fluid flow problems using the Message Passing Interface (MPI) library and the OpenCL technology. These technologies make the most out of today’s heterogeneous computing systems equipped with multi-core CPUs and GPUs. The team would like to leverage their existing parallel computing practice and adapt it to the traffic safety message processing and analysis.]]></description>
      <pubDate>Wed, 10 Aug 2022 15:07:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2004707</guid>
    </item>
    <item>
      <title>A Cloud-based Quantum Artificial Intelligence-supported Truck Platooning Strategy for Safety and Operational Performance</title>
      <link>https://rip.trb.org/View/1923107</link>
      <description><![CDATA[Description: This research aims to develop a new Q-AI driven technology that will address the existing knowledge gap identified by existing literature of fully quantifying the impact of freight operation by integrating freight pathways, powertrain technologies, the total cost of ownership, infrastructure, and platooning technology. To achieve this goal, the research objectives are to, (1) develop cloud-assisted truck platooning models considering different truck powertrain technologies (Obj.-1), (2) develop cloud computing strategies to satisfy real-time computing requirements of structured and unstructured data from heterogeneous data sources (Obj.-2); 3) study the impacts of cloud-supported truck platoons, in terms of safety, operational efficiency and energy consumption, along freight corridors in a simulation tool (Obj.-3); (4) validate the improved predictive analytics including Q-AI strategies in actual fleet trials for safety and operational impacts(Obj.-4), and (5) evaluate the cost-effectiveness and other impacts of proposed technology with the baseline technology with roadside units-supported cloud servers.

Intellectual Merit: To maximize the expected safety and operational benefits of truck platooning, this proposal intends to develop a cloud infrastructure supported platooning algorithm to assist truck platooning to minimize delay, reduce energy consumption and improve safety and demonstrate the application in the real world. Cloud infrastructure will provide a seamless, on-demand data storing, application hosting, and execution platform for truck platooning application. 

Broader Impacts: Reliance on in-vehicle computational devices for truck platooning, as considered in the existing studies, will increase the computational burden for each vehicle. To reduce the overreliance on the in-vehicle computing nodes and enable a predictive analytics-based truck platooning for a corridor to ensure safety and operational improvement, a cloud-based truck platooning framework will be developed in this research. Both connected trucks and automated trucks will be considered in this study. This research focuses on predictive analytics using quantum artificial intelligence (Q-AI). An earlier study discussed using Q-AI to enhance learning efficiency, learning capacity, and run-time improvements. The focus of this study is to develop predictive Q-AI algorithms for cloud-based, safe, and efficient truck platooning using high volume and heterogeneous data from multiple diverse sources with the capability of scaling up. This study will also evaluate the efficacy of platooning for safety and operational performance in the real world in a test track in Greenville, South Carolina.]]></description>
      <pubDate>Sun, 06 Mar 2022 15:24:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/1923107</guid>
    </item>
    <item>
      <title>Multimodal-AI based Roadway Hazard Identification and Warning using Onboard Smartphones with Cloud-based Fusion</title>
      <link>https://rip.trb.org/View/1923105</link>
      <description><![CDATA[Road hazard is one of the significant causes of fatality in road accidents. Accurate estimation of road hazards can ensure safety and enhance the driving experience. Existing methods of road condition monitoring are time-consuming, expensive, inefficient, require much human effort, and need to be regularly updated. There is a requirement for a flexible, cost-effective, and efficient process to detect road conditions, especially road hazards. In this study, we present a new method to deal with road hazards using smartphones. Since most of the population drives cars with smartphones onboard, we aim to leverage this
to detect road hazards in a more flexible, cost-effective, and efficient way. This study proposes a cloud based deep-learning road hazard detection model based on a Long-Short Term Memory network (LSTM) to detect different types of road hazards from motion data. To address the issue of large data requests for deep learning, this study proposes to fuse both simulation data and experimental data for the learning. The proposed approaches are validated by experimental tests, and the results demonstrate the accuracy of road hazard detection based on cloud-based fusion]]></description>
      <pubDate>Sun, 06 Mar 2022 15:14:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/1923105</guid>
    </item>
    <item>
      <title>Building Smarter Cities via Intelligent Asset Management: South Carolina Case Study using IBM Maximo Application</title>
      <link>https://rip.trb.org/View/1923100</link>
      <description><![CDATA[Description: Bridges serve as vital hubs in the national economy, facilitating the movement
of goods and vehicles. South Carolina has nearly 9,455 bridges, and 8.7 percent are currently classified
as structurally deficient [ARTBA, 2021]. A total of 89.4 percent of structurally deficient bridges are
located on interstate highways and other critical roadways that connect major airports, ports, railroads,
and truck terminals [ARTBA, 2021], posing a threat to the transportation of people and goods.

Intellectual Merit: The research goal is to develop a cost-effective approach to monitor the road conditions by cloud-based collaborative monitoring using in-vehicle smartphones which could be from any general public vehicle users.

Broader Impacts: This project aims to reduce the cost of road condition monitoring by providing a very cost-effective way with a minimum investment of equipment and labor, significantly improve the safety of transportation systems, especially the multimodal connected and automated transportation systems, by providing timely needed road condition monitoring, and create a smartphone-based road condition dataset to benefit the research society.]]></description>
      <pubDate>Sun, 06 Mar 2022 15:01:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/1923100</guid>
    </item>
    <item>
      <title>Cloud-based Collaborative Road Condition Monitoring using In-Vehicle Smartphone Data and Deep Learning</title>
      <link>https://rip.trb.org/View/1922918</link>
      <description><![CDATA[Ensuring the safety of transportation systems requires monitoring the conditions of roads. Traditional monitoring and inspection of road conditions require surveyors to walk or drive along the roads to search for defects manually. Such processes require a lot of
human and equipment efforts, which however can still hardly provide real-time information on road conditions. Existing automated road condition monitoring approaches usually require special vehicles equipped with specific sensors and corresponding processing and computing devices. In addition, these approaches only use one single vehicle to perform the detection on its own and the vehicle
usually still needs to be driven by a surveyor. Therefore, in this project, we developed a much more cost-effective approach to monitoring road conditions by cloud-based collaborative monitoring using in-vehicle smartphones which could come from any public
vehicle user. When a vehicle drives over a certain type of road defect, the acceleration signal, especially the vertical acceleration, will have a unique pattern in the trajectory. The type of road defect can be identified from the general shape of the acceleration wave. Meanwhile, the amplitude of the wave reflects the vehicle speed and the severity of the defect. In this project, we trained a Long Short-Term Memory (LSTM) based deep learning network to complete the identification of defect types using acceleration data. Sometimes LSTM could have difficulty in deciding the defect type solely based on accelerations since the smartphone would be placed in the passenger cabin, and the motion it measures will be filtered by the vehicle suspension. Thus, we trained YOLO (You Only Look Once) deep learning network to detect and identify defects from the live video taken by the smartphone’s camera. We then fused the road condition detection results of multiple deep learning approaches from smartphones of multiple vehicles in order to get holistic monitoring of the road condition. The data including the smartphone motion and vision-based road condition detection results and the GPS locations of the vehicles would be sent to a cloud server through cellular networks. All detection results were then fused with the k-means clustering method based on their GPS locations, and the top three most occurred types of damage within a cluster were found to represent the road condition of that location.
We developed a data collection app to collect acceleration and vision data from smartphones mounted on the windshield of multiple cars. The data used for this experiment was collected over various roads of Greenville, Spartanburg, Clemson, and Columbia area in South Carolina, USA. Eventually, we were able to get an accuracy of 94% from the trained LSTM model, and 87.5% from the trained YOLO in classifying potholes, cracks, and normal road surfaces. We also created a web page that displays the fusion results of detected road damage on a map. The web page enables concerned authorities to view the road damages reported by the users
with the help of our developed mobile application]]></description>
      <pubDate>Sun, 06 Mar 2022 13:37:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/1922918</guid>
    </item>
    <item>
      <title>Bigdata Analytics and Artificial Intelligence for Smart Intersections</title>
      <link>https://rip.trb.org/View/1631565</link>
      <description><![CDATA[This proposal seeks to use edge-based video-stream processing, infrared cameras, and LIDAR at intersections and in public vehicles to convert video data into space-time trajectories of individual vehicles and pedestrians that are transmitted and synthesized on a cloud-based system.]]></description>
      <pubDate>Mon, 17 Jun 2019 16:11:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/1631565</guid>
    </item>
    <item>
      <title>Security of Connected Vehicles via Sandboxing against False Data Injection Attack</title>
      <link>https://rip.trb.org/View/1578502</link>
      <description><![CDATA[Description: This project will develop a resilient control framework for managing information flows for CAVs. More specifically, it will create a cloud-based sandboxing technique that will allow CAVs to safely operate even in corrupted conditions when malicious data is injected into the communication network. The technique will also account for communication delays, real-time computational constraints, and opportunistic behavior and uncertainties in the localization of non-connected vehicles (non-CVs). The project will focus on both urban and extra-urban driving scenarios, and on heterogeneous traffic conditions (60% or higher CAV technology penetration). 
Intellectual Merit: The research team will develop a cloud-based control-oriented technology solution for CAVs that utilizes infrastructure information, along with a traffic model to detect cyber-attacks consisting of false information injected into the CAV communication system.
Broader Impacts: The technology that will be developed may play an important role in integrating smart cities’ and regions’ infrastructure services and components by accelerating the introduction of CAVs and vehicle-sharing services.
Technology Transfer Plan: Both the software and the algorithm the team develops will be licensed to the private and public sectors.]]></description>
      <pubDate>Fri, 11 Jan 2019 15:51:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/1578502</guid>
    </item>
    <item>
      <title>SPR-4205: Connected Vehicle Corridor Deployment and Performance Measures for Assessment</title>
      <link>https://rip.trb.org/View/1486728</link>
      <description><![CDATA[The project team and SAC will engage with a variety of public and private sector stakeholders to identify use cases for connected vehicle deployments and implement them on signalized Indiana corridors. Two likely deployment architectures will be considered: 1) Dedicated Short Range Communication (DSRC) and 2) Cloud based vehicle communication through factory installed telematics. Performance measures will be developed and used to assess the uses cases and deployment architectures. These performance measures will build on past traffic performance measures and extended to incorporate the new and emerging connected vehicle data.]]></description>
      <pubDate>Thu, 26 Oct 2017 15:38:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/1486728</guid>
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