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    <title>Research in Progress (RIP)</title>
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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>Research in Progress (RIP)</title>
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      <title>Physics-Informed Learning and Control of Connected and Autonomous Vehicles for Congestion Reduction</title>
      <link>https://rip.trb.org/View/2458989</link>
      <description><![CDATA[Building upon previous work in lane changing using physics-informed machine learning for autonomous vehicles, the goal of this project is to develop physics-informed machine learning and data-driven control-based tools for the combined longitudinal and lateral planning and control of connected and autonomous vehicles (CAVs). This research initiative holds the promise to have the following advantages: 1) Utilizing physics-informed machine learning as a tool can significantly enhance computational efficiency, which is beneficial for real-time control in complex scenarios; 2) Combining neural networks with physical models can greatly reduce over-reliance on data; 3) During the training phase of neural networks, any differentiable objective function and various constraints can be considered, allowing it to solve constrained multi-objective model predictive control problems without affecting computational speed. In addition, this project will design a lane-change decision-making module based on deep reinforcement learning and validate the congestion-reducing scheme using NGSIM data and SUMO simulations.]]></description>
      <pubDate>Thu, 21 Nov 2024 17:29:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2458989</guid>
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    <item>
      <title>Improving Congestion Impact Estimation</title>
      <link>https://rip.trb.org/View/2459057</link>
      <description><![CDATA[The purpose of this project is to develop an accurate estimation of different congestion reduction schemes by creating more accurate digital twins. Current schemes are often benchmarked in micro-simulators but inaccurate micromodels of driver and pedestrian behavior can lead to the impacts being drastically over or under-estimated. Our goal is to improve these micro-models using new techniques in imitation learning. These new micro-models will be ported into SUMO, a standard micro-simulator, and used to recompute the expected benefits of popular schemes for tackling congestion. They will also be tested on the Waymo Sim Agents Challenge to see if they are competitive with existing state-of-the-art ML-based models. The proposed work does not directly tackle congestion but focuses on enabling researchers and policy-makers to more effectively tackle congestion. Micro-simulator are used to investigate the impact of an intervention and consequently overestimates in their impact can lead to improper estimation of the impact of an intervention per dollar.]]></description>
      <pubDate>Thu, 21 Nov 2024 16:47:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2459057</guid>
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      <title>Sensor-enabled Calibration of VR-Integrated Co-Simulation Platforms for Enhanced Accuracy in Multi-modal Mobility Models</title>
      <link>https://rip.trb.org/View/2283481</link>
      <description><![CDATA[Traffic simulations provide valuable insights into traffic control measures, infrastructure design, vehicle-to-vehicle communication, route selection behavior, emissions modeling, and more. SUMO (Simulation of Urban MObility), an open-source, microscopic, and multimodal traffic flow simulation platform, facilitates the creation of realistic traffic flow simulations by incorporating road networks, vehicles, pedestrians, and interactions with other applications such as virtual reality platforms and driver simulators. Calibration aims to bridge the gap between simulation outcomes and real-world observations; however, the effectiveness of calibration relies heavily on the realism of interactive behavior models for various agents, such as cars, drivers, bicyclists, pedestrians (including those with accessibility needs), and workers. Integrating multiple interactive simulations into a coherent representation of real-world transportation settings poses significant challenges due to the complexities of microsimulation, the diverse range of metrics, and varying traffic control systems. Calibration metrics, data sources, temporal scales, and spatial versus perceptual accuracies vary among platforms, leading to complexities when synchronizing and calibrating them collectively. To address these challenges, this research endeavors to meticulously calibrate a virtual ecosystem comprising diverse agents, including cars, pedestrians, workers, and agents with disabilities. By integrating data from multiple sensing sources like camera streams, drone data, connected vehicle information, and worker behaviors captured in virtual reality, the research seeks to tackle the intricacies of multi-simulation calibration. The proposed research project aims to embark on an innovative endeavor focused on the calibration of interactive and multi-simulation environments. Starting with SUMO and virtual reality environments, the project intends to create a vocabulary of calibration metrics for simulators, devise methods to expand data sources, and identify challenges on the way to establish a flexible framework for integrated calibration. Additionally, an existing testbed (e.g., Flatbush Avenue testbed) will be expanded to cover a disadvantaged community defined by U.S. DOT Climate and Economic Justice Screening Tool in order to identify the challenges on the way to the proposed calibration platform and defining ways to measure its efficiency, accuracy, and robustness. This research strives to contribute significantly to the field of multi-modal mobility solutions, ultimately enhancing congestion reduction strategies and advancing the capabilities of simulation-driven planning.]]></description>
      <pubDate>Mon, 30 Oct 2023 23:06:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2283481</guid>
    </item>
    <item>
      <title>An AI-reinforced Traffic Digital Twin for Testing Emergency Vehicle Interventions</title>
      <link>https://rip.trb.org/View/2278553</link>
      <description><![CDATA[Emergency vehicle (EMV) response times have degraded due to increasing urbanization and resulting congestion. Evaluating interventions to mitigate this degradation is too costly to be done in the field. This project will build a traffic digital twin (TDT) to be developed in collaboration with FDNY as a virtual test bed to evaluate interventions and support decision-making and planning in a safe simulation environment. The TDT will be built on the open source Simulation of Urban Mobility (SUMO) microscopic continuous traffic simulation. Key challenges are incorporating AI to learn non-EMV driver responses to EMV signals (sirens, V2X technologies) and to train the TDT to different traffic states using historical traffic data and dispatch data from FDNY. The scope of work can be summarized as: (1) development and calibration of a baseline SUMO simulation for FDNY district M6 in Harlem, NYC; (2) combining traffic data and camera data at the same time to develop an AI model for traffic state prediction in the digital twin; (3) combining EMV global positioning system (GPS) data and the traffic state data to statistically learn non-EMV behavioral responses (response reaction time, etc.); and (4) developing simulation-based intervention optimization and test using out-of-sample observations
]]></description>
      <pubDate>Sat, 28 Oct 2023 19:49:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2278553</guid>
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    <item>
      <title>Development of an open source multi-agent virtual simulation test bed for evaluating emerging transportation technologies and policies</title>
      <link>https://rip.trb.org/View/1508473</link>
      <description><![CDATA[The research team will develop, calibrate, and test a virtual test bed for New York City (NYC) wide Large-scale transportation simulation model. The test bed will be run on two simulation platforms: MATSIM for passenger travel simulation and SUMO for traffic simulation. The project will design a functional architecture to integrate both platforms to seek consistent sub-area analysis, and data transferability with databases and other models of NYC Open data and city and state governments. The team will design and illustrate a series of test cases that can be conducted using either tool and by combining them together. In addition to the development of the integrated and open simulation platform, several algorithms and novel approaches for on-line calibration, real-time computation etc. will be developed using this new simulation tool.

The second phase of the research will include further expanding the capabilities of the virtual test bed, applying the models to scenarios of interest to NYC DOT and the MTA, and focusing on tech transfer activities to make the ecosystem accessible to policymakers and  consortium partners. Applications include on-demand robotic taxi, traffic flow modifications to allow for connected vehicles, and dockless bikeshare. The research will provide resources and a virtual framework for supporting and helping public sector’s decision making to fill in the gap between basic research and field deployment.
]]></description>
      <pubDate>Thu, 12 Apr 2018 00:24:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/1508473</guid>
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