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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Enhancing Safety in Mixed-Autonomy Traffic via Prediction-Based Connected Autonomous Vehicle Control</title>
      <link>https://rip.trb.org/View/2703928</link>
      <description><![CDATA[This project proposes a new framework for prediction-based connected autonomous vehicle (CAV) control to enhance safety in mixed-autonomy traffic where CAVs and human-driven vehicles (HVs) coexist. Specifically, by predicting future traffic conditions behind a target CAV, the vehicle can be proactively controlled to improve the safety and efficiency of the overall traffic stream. This approach is motivated by the fact that a controlled CAV directly influences the behavior, safety, and performance of following HVs through car-following interactions. Accordingly, the proposed method jointly considers a CAV and its following HVs in the design of a safety-aware driving strategy. Although HVs do not communicate with CAVs, traffic states related to HVs can be estimated using partial traffic measurements collected by CAVs. Leveraging these predictions, the proposed control strategy will be formulated within a model predictive control (MPC) framework to improve safety and traffic efficiency for HVs following a CAV. Extensive simulation studies will be conducted under a range of traffic scenarios and HV driving styles to demonstrate the effectiveness of the proposed approach. In addition, multiple CAV penetration rates will be evaluated to examine scalability and deployment potential. 
This project is highly aligned with the Mid-America Transportation Center's (MATC’s)  mission to advance transportation safety through technology development, technology transfer, and deployment. It addresses a timely safety challenge: near-term traffic will be mixed-autonomy, where early-generation autonomous vehicles (e.g., ACC-equipped vehicles and emerging CAVs) operate alongside the majority of HVs. In this environment, safety risks arise not only from individual vehicle performance, but also from interactions between automated and human drivers—an issue that is often overlooked in existing CAV control design.
While this project will leverage an existing dataset collected in Minnesota and high-fidelity simulation data generated in Simulation of Urban MObility (SUMO) for numerical investigation and validation, the team anticipates extending the proposed methodology in future work using connected-vehicle and SPaT data to be collected by Dr. Li Zhao’s team at UNL, in collaboration with the Nebraska DOT. 
]]></description>
      <pubDate>Thu, 21 May 2026 22:41:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703928</guid>
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      <title>RES2020-25: Development of a New Attenuation Model for West Tennessee</title>
      <link>https://rip.trb.org/View/1717012</link>
      <description><![CDATA[The main objective of this study was to determine seismological parameters in Central and Eastern North America
(CENA), including constraints on the geometrical spreading, anelastic attenuation, stress parameters, and site
attenuation parameters (kappa). To determine the seismological parameters, the recently developed and published
Ground Motion Models (GMMs) for the NGA-East were used. In addition to the main objective, as part of this
study, three new ground-motion models (GMMs) were developed: (1) a new model for vertical to horizontal
response spectral ratios for central and eastern North America; (2) a ground-motion prediction model for smallto-moderate induced earthquakes for central and eastern United States; and (3) a ground motion model for the Gulf
Coast region of the United States, which includes part of West Tennessee. These three GMMs are presented as
supplementary material to this report
We used a genetic algorithm (GA) to invert weighted geometric mean estimates of horizontal response-spectral
acceleration from the empirical NGA-East ground-motion models to successfully estimate a consistent set of
seismological parameters that can be used along with an equivalent point-source stochastic model to mimic the
general scaling characteristics of these ground-motion models. The inversion is performed for events of M 4 – 8.0,
RRUP = 1 to 300 km, T = 0.01 – 10 sec (f = 0.1 – 10 Hz).
This study is the first to perform a formal inversion using the extensive and peer-reviewed CENA GMMs
developed for the NGA-East project and using a formal GA approach. The approach was validated by using
simulated small-to-moderate magnitude and large-magnitude data derived from the NGA-West2 GMMs (Zandieh
et al., 2016, 2018; Pezeshk et al., 2015).]]></description>
      <pubDate>Mon, 29 Jun 2020 18:12:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/1717012</guid>
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