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    <title>Research in Progress (RIP)</title>
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    <language>en-us</language>
    <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>
    <image>
      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving with Year 2 Focus on Bicyclist Safety</title>
      <link>https://rip.trb.org/View/2440009</link>
      <description><![CDATA[Motivated by the shortcomings of using public road development of autonomous driving functions, this project focuses on the Vehicle-in-Virtual-Environment (VVE) method of safe, efficient, and low-cost connected and autonomous driving function development, evaluation, and demonstration. The VVE method places the actual vehicle inside a highly realistic virtual environment with realistic virtual sensor feeds while the vehicle is physically moving in a large and empty testpad. This is as if the vehicle is using a virtual reality headset. It is possible to easily change the virtual development environment and inject rare and difficult events which can be tested very safely. This is the second-year proposal for a two-year project that focuses on the use of the VVE method for development and evaluation of Vulnerable Road User (VRU) safety functions. Pedestrian safety was the focus of the first-year project. The current proposal focuses on bicyclist safety and the use of the VVE method for its development and evaluation. 

Five FARS (NHTSA’s Fatality Analysis Reporting System) pedestrian crash scenario use cases are being considered in the first-year project. The research team has demonstrated FARS 750 (Crossing Roadway – Vehicle Not Turning) with their research vehicle and real pedestrian as part of the year 1 project and will present their results in the 2024 SAE World Congress and Experience (WCX). The team also started developing and testing a deep reinforcement learning based pedestrian collision avoidance system, the early results of which will also be presented in the 2024 SAE WCX conference. The proposed year 2 project will focus on the bicyclist crash scenario use cases of: Motorist Overtaking Bicyclist (FARS 230), Bicyclists Failed to Yield at Midblock (FARS 310), Bicyclist Failed to Yield at Sign Controlled Intersection (FARS 145), Bicyclist Left Turn / Merge (FARS 220), Motorist Left Turn / Merge (FARS 210).

Vehicle-to-VRU communication-based bicyclist detection which also works for non-line-of-sight cases will be combined with perception-based detection within the VVE method in the year 2 project. The Deep Reinforcement Learning (DRL) method the team is developing in the first-year project for pedestrian protection will be further developed for bicyclist protection. The team will be using hierarchical DRL to improve the training times by making DRL generate a collision free trajectory modification of the vehicle while the trajectory tracking control will be treated separately. Vehicle trajectory modification to avoid a possible future collision will be developed and evaluated safely using the VVE approach with the vehicle and bicyclist at separate locations physically but on a collision risk path in the virtual environment which will enable very realistic evaluation of the designed VRU safety function. Robust and delay tolerant trajectory control will be developed and evaluated using the VVE method also for executing the calculated collision free modified vehicle trajectory which may involve slowing down, braking, or braking and steering. Virtual environments and collision risk scenarios will be developed and evaluated first in MIL and HIL, followed by development and evaluation using the VVE method. The results will also be applicable and extendable to the safety of scooterists.]]></description>
      <pubDate>Sat, 12 Oct 2024 11:45:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440009</guid>
    </item>
    <item>
      <title>Enhancing Traffic Safety and Connectivity: A Data-Driven Multi-Step-Ahead Vehicle Headway Prediction Leveraging High-Resolution Vehicular Trajectories</title>
      <link>https://rip.trb.org/View/2292664</link>
      <description><![CDATA[Vehicle headway, defined as the time elapsed between two successive vehicles passing a roadway point, is a key mesoscopic-scale measure in traffic flow theory with safety-critical transportation applications, such as preemptive collision avoidance warning systems as well as connected and autonomous vehicle (CAV) platoon control. Hence, it is crucial to accurately predict vehicle headway over sufficiently long future horizons (i.e., multi-step-ahead prediction) to be applicable for downstream safety-critical applications. This is a challenging task due to several random factors influencing headway, including inter- and intra-driver heterogeneity, asymmetric car-following driving behavior, and vehicle heterogeneity under mixed traffic of different vehicle classes. This becomes even more complicated under traffic congestion, which results in tangible inter-vehicle interactions and, thus, speed-dependent headways. The complex effects of the above factors on headway, along with the unprecedented amount of high time-resolution vehicle trajectory big data (e.g., datapoints recorded every 0.1 second), call for advanced data-driven headway prediction models. Deep learning architectures, particularly variants of Recurrent Neural Network (RNN), are promising candidates as they can “learn” highly nonlinear relationships from headway time-series data. However, recurrent networks are notorious for the vanishing gradient problem, which precludes learning long-term dependencies in time series data. To tackle, this proposed project will employ a state-of-the-art interpretable deep learning model for multi-step-ahead time series forecasting (e.g., next 5 seconds), which can accommodate reasonably long prediction horizons that can capture human/vehicle reaction time. Leveraging the vehicle trajectory big data from the USDOT’s Next Generation Simulation (NGSIM) dataset, the model will be trained and tested to investigate the effects on headway of microscopic traffic measures, macroscopic traffic flow, vehicle class, and lane position.]]></description>
      <pubDate>Tue, 21 Nov 2023 18:37:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292664</guid>
    </item>
    <item>
      <title>Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving</title>
      <link>https://rip.trb.org/View/2292642</link>
      <description><![CDATA[The current approach to connected and autonomous driving function development and evaluation uses model-in-the-loop (MIL) simulation, hardware-in-the-loop (HIL) simulation and limited proving ground use, followed by public road deployment of the beta version of software and technology. The rest of the road users are involuntarily forced into taking part in the development and evaluation of these beta level connected and autonomous driving functions. This is an unsafe, costly and inefficient method and has resulted in many problems in the deployment of autonomous vehicles with an associated loss of trust. Motivated by these shortcomings, this project focuses on the Vehicle-in-Virtual-Environment (VVE) method of safe, efficient and low-cost connected and autonomous driving function development, evaluation and demonstration. The VVE method places the vehicle inside a highly realistic virtual environment with realistic virtual sensor feeds while the actual vehicle is physically running inside a large and empty test area. This is as if the vehicle is using a virtual reality headset. It is possible to easily change the virtual development environment and also inject rare and difficult events which can be tested very safely. This is a two-year project and will focus on the use of the VVE method for development and application of Vulnerable Road User (VRU) safety functions.   Pedestrian safety will be treated in the first year of the project and bicyclist safety will be treated in its second year. The research will start by considering the following five pedestrian crash scenario use cases of: Crossing Roadway – Vehicle Not Turning (FARS 750), Walking/Running Along Roadway (FARS 400), Dash / Dart-Out (FARS 740), Crossing Roadway – Vehicle Turning (FARS 790), Crossing Expressway (FARS 910) during year 1 and the five bicyclist crash scenario use cases of: Motorist Overtaking Bicyclist (FARS 230), Bicyclists Failed to Yield – Midblock (FARS 310), Bicyclist Failed to Yield – Sign – Controlled Intersection (FARS 145), Bicyclist Left Turn / Merge (FARS 220), Motorist Left Turn / Merge (FARS 210) in year 2 where FARS is short for NHTSA’s Fatality Analysis Reporting System.  Vehicle-to-VRU communication-based pedestrian/bicyclist detection which also works for non-line-of-sight cases will be combined with camera and lidar based detection within the VVE method. A data-driven approach will be used to predict the vulnerable road user trajectory which will be compared with the trajectory of the vehicle to predict a future collision possibility. Vehicle trajectory modification to avoid a possible future collision will be developed and evaluated safely using the VVE approach with the vehicle and VRUs at separate locations physically but on a collision risk path in the virtual environment which will enable very realistic evaluation of the designed VRU safety function. Robust and delay tolerant trajectory control will be developed and evaluated using the VVE method also, for executing the calculated collision free modified vehicle trajectory which may involve slowing down, braking or braking and steering. Virtual environments and collision risk scenarios will be developed and evaluated first in MIL and HIL, followed by development and evaluation using the VVE method.]]></description>
      <pubDate>Mon, 20 Nov 2023 19:43:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292642</guid>
    </item>
    <item>
      <title>Control of Connected and Autonomous Vehicles for Congestion Reduction in Mixed Traffic: A Learning-Based Approach
</title>
      <link>https://rip.trb.org/View/2283490</link>
      <description><![CDATA[Building on prior work on lane keeping and lane changing for connected and autonomous vehicles, this collaborative research project aims at taking a significant step forward to develop innovative learning-based, real-time control algorithms for connected and autonomous vehicles to reduce traffic congestion. This project aims at achieving four major objectives: (1) developing a traffic light prediction method by utilizing advanced deep learning techniques; (2) developing a trajectory optimization framework for a stream of vehicles to efficiently reduce the traffic congestion, attenuate the stop-and-go waves, and increase the throughput of the traffic; (3) integrating reinforcement learning techniques with (control) barrier functions to address the safety-oriented learning-based trajectory tracking control of autonomous vehicles; (4) validating the proposed congestion-reducing scheme with real-world vehicle trajectory data and SUMO testing under different environments in the presence of different vehicle mixes and driver uncertainties.]]></description>
      <pubDate>Mon, 30 Oct 2023 22:33:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2283490</guid>
    </item>
    <item>
      <title>Dynamic Origin-Destination estimation (DODE) under incidents using individual trajectories data</title>
      <link>https://rip.trb.org/View/2247598</link>
      <description><![CDATA[Travel behavior in route choices under incidents will be modeled based on a disutility function for individuals and the calibrated regional network model. The research team will design a methodology to simulate the traffic and estimate dynamic origin-destination (O-D) demand on the real time basis. The simulation adopts the historical traffic demand as an initial (base) demand and their pre-scribed route choices from the dynamic network model in the existing meso-scopic simulation tool (MAC-POSTS). ]]></description>
      <pubDate>Fri, 15 Sep 2023 15:31:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2247598</guid>
    </item>
    <item>
      <title>Multiple-vehicle Trajectory Planning Framework Considering Vulnerable Road Users</title>
      <link>https://rip.trb.org/View/2229371</link>
      <description><![CDATA[This study aims to address the challenge of real-time trajectory planning for connected and automated vehicles (CAVs) while considering vulnerable road users (VRUs) in the environment. The problem lies in efficiently planning trajectories for CAVs in the presence of unpredictable VRUs, ensuring safety and avoiding crashes. The solution involves modeling the decision-making processes of CAVs and VRUs through game theory, incorporating the uncertainty of VRUs' motion using confidence intervals, and designing efficient heuristic algorithms for real-time problem solving. The project's expected outcomes include a technical paper describing the developed trajectory planning framework, along with simulation videos showcasing its effectiveness. This proposal seeks to fill a research gap by exploring novel solutions for this complex problem and contribute to the field of CAV operations. The proposed framework's concepts and validation methods are intended for educational purposes and potential practical implementation in future CAV operations.]]></description>
      <pubDate>Thu, 17 Aug 2023 08:40:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2229371</guid>
    </item>
    <item>
      <title>Eco-Driving of Connected and Autonomous Vehicles Approaching and Departing Signalized Intersections</title>
      <link>https://rip.trb.org/View/1948637</link>
      <description><![CDATA[Autonomous vehicles (AVs) commonly known as self-driving vehicles have captured the attention
of the public for decades and continue to be the center of attention of academic and industrial
research activities worldwide. Their proliferation has rapidly grown, largely as a result of Vehicleto-
X (or V2X) technology which refers to an intelligent transportation system where all vehicles and
infrastructure components are interconnected with each other. Therefore, the term “CAV”, which is
short for connected and autonomous vehicles, was coined. The connected here not only refers to
the connections to infrastructures, such as traffic signals and GPS information, but also includes
the communication among vehicles in the same vicinity. Connected and autonomous vehicles
(CAVs) will have a profound impact on various aspects of urban mobility, such as safety, energy
usage, and environmental sustainability, which are considered as the driving change for smart
cities. The CAV technology provides an intriguing opportunity to better monitor transportation network
conditions, which in turn helps optimize traffic flows, enhance safety, reduce congestion, and
minimize emissions. Recent developments in artificial intelligence would make this once science
fiction-sounding idea into reality.
This project is going to address the safety and energy efficiency issues of CAVs approaching
and departing multiple signalized intersections. The alarming state of existing transportation
systems has been well documented from various aspects. From the safety perspective, an estimated
165000 accidents occur annually in intersections caused by red light runners, where about
800-1000 cases are fatal. From the energy perspective, for instance, in 2014, congestion caused
vehicles in urban areas to spend 6.9 billion additional hours on the road at a cost of an extra 3.1
billion gallons of fuel, resulting in a total cost estimated at $160 billion. The novelty of the proposal
lies in establishing a framework by combining emerging Artificial Intelligent (AI) technologies
and traditional control and optimization approaches to deal with existing challenges of trajectory
planning of CAVs approaching and departing signalized intersections. The first question that this
project addresses is the traffic signal phase detection. Traffic signal phase detection and recognition
is an important application for AVs aiding and providing information about decision making
on intersections. Second, we will develop an energy efficient and safe algorithm for CAVs to approach
and depart signalized intersections based on identified traffic phase information. Next, we
will extend this framework to the mixed traffic case of CAVs and human-driven vehicles (HDVs).
This analysis will be carried out using machine learning and reinforcement learning approaches
based on collected data in a simulation environment. Developed algorithms and results will be
extensively tested through software simulation, such as MATLAB, and SUMO. In addition, robotic
cars will be used for the hardware testing.
The research results developed in this project will be disseminated in conferences to academia
and industry. They will also be incorporated into existing courses (EE 3530 Introduction to Control
Engineering; EE 7500 Distributed Control of Multi-Agent System; EE 7560 Optimal Control
and Reinforcement Learning) offered by the Division of Electrical and Computer Engineering,
Louisiana State University. Graduate students and undergraduate students will be involved with
project all the time. Opportunities will be created especially for underrepresented students to
work the project. We will organize seminars to introduce the new technology to local community,
including high school teachers and local industrial companies for possible commercialization.]]></description>
      <pubDate>Fri, 06 May 2022 15:40:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/1948637</guid>
    </item>
    <item>
      <title>Preparation of Pavement Infrastructure for Connected and Autonomous Vehicle Deployment –Phase I</title>
      <link>https://rip.trb.org/View/1856579</link>
      <description><![CDATA[While Connected and Autonomous Vehicle (CAV) might be ten or fifteen years away from large-scale deployment in reality, considering the long lifespan of transportation infrastructure, it might be beneficial for departments of transportation (DOT) to start to prepare transportation infrastructure to support future CAV testing and deployment. This project will firstly quantify the CAV trajectory pattern with the help of an autonomous driving simulation software, and explore and identify available datasets such as the Long-Term Pavement Performance (LTPP) database to develop and train an Artificial Intelligence (AI)-based pavement performance predictive model. Such model, once trained and validated, will be applied onto the collected CAV trajectory datasets, to study the impact of CAV movements to the pavement infrastructure.]]></description>
      <pubDate>Sat, 05 Jun 2021 16:58:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1856579</guid>
    </item>
    <item>
      <title>Effect of Pedestrian and Crowds on Vehicle Motion and Traffic Flow</title>
      <link>https://rip.trb.org/View/1758052</link>
      <description><![CDATA[Using data collected and motion modelling developed in a previous project in this UTC, "Understanding and Guiding Pedestrian and Crowd Motion", as well as additional data to be collected, the project team will: (1) Develop pedestrian intention and motion modelling and prediction, with experimental validation, (2) Refine the sensor package and data analysis techniques of the project team's pedestrian motion data collection system and datasets, (3) Develop vehicle’s motion planning and control algorithm for navigating, dodging, or stopping in pedestrian interaction scenarios.]]></description>
      <pubDate>Wed, 16 Dec 2020 14:21:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/1758052</guid>
    </item>
    <item>
      <title>Predicting Paths of Controlled Pedestrians at Intersections Using Deep Learning Models</title>
      <link>https://rip.trb.org/View/1745743</link>
      <description><![CDATA[Traffic safety is a critical issue for heterogeneous, multimodal transportation settings such as traffic intersections. In particular, safety of pedestrians is a very challenging problem, since pedestrians are particularly vulnerable to small accidents. With increasing numbers of autonomous and partially autonomous vehicles, predicting where pedestrians will be in the future is critical, since these vehicles need to plan safe trajectories ahead of time. It is also conceivable that these autonomous vehicles will broadcast their planned trajectories to surrounding pedestrians, to help coordination, giving the pedestrians safe corridors to cross roads, for example.

In earlier work, the research team has investigated the Social Spatio-Temporal Graph Convolutional Neural Network (Social-STGCNN), which substitutes the need of aggregation methods by modeling pedestrians and vehicle interactions as a graph. This algorithm results in an improvement over the state of the art  prediction algorithms by 20% on the Final Displacement Error (FDE), with 8.5 times less parameters and up to 48 times faster inference speed than previously reported methods. In addition, this model is data efficient, and exceeds previous state of the art on the ADE metric with only 20% of the training data. The present proposal builds on this earlier work.

The objective of this project is to better understand how to model human trajectory tracking performance. Humans that receive guidance information are supposed to follow their assigned trajectories, though they may not exactly follow the assigned path. Their deviation from the assigned path is very important for collision avoidance purposes, and the goal of this project is to accurately capture how much deviation one can reasonably expect from a given human, and how do other vehicles around the pedestrian affect trajectory tracking.
]]></description>
      <pubDate>Sat, 17 Oct 2020 14:13:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/1745743</guid>
    </item>
    <item>
      <title>Nondestructive Data Driven Motion Planning for Inspection Robots (AS-5)</title>
      <link>https://rip.trb.org/View/1740053</link>
      <description><![CDATA[During the first five years, the team led by Dr. Hung La of the Advanced Robotics and 
Automation (ARA) lab at the University of Nevada, Reno (UNR), has developed five
different climbing robotic prototypes (wheeled mobile robot, tank-liked robot, roller-chain liked robot, inch-worm-like robot, and bicycle robot) to perform data collection on complex 
steel structures and steel bridges. Additionally, the ARA team developed a control
framework to allow the user to manually control these robots to climb on various steel 
structures (both flat and curving surfaces). The team also partially developed a navigation 
algorithm based on point-cloud data extracted from 3D stereo cameras and a LiDAR to 
allow these robots to safely traverse on bridge’s steel members.
In the final year of this project, the ARA team will focus on the development of 
navigation and motion planning algorithms to provide these climbing robotic systems a
fully autonomous navigation function so that they can safely traverse on and visit all steel 
members of the bridge for efficient inspection. To achieve this goal, we plan to (1) develop 
a method to segment the workspace into multiple clusters and represents them by a set 
of boundary points, then the Expectation Maximization-Gaussian Mixture Model (EM GMM) method will be utilized to classify these boundary points into irregular dimensional 
clusters for efficient motion planning, (2) develop a software with Graphic User Interface 
for the user operation, and (3) test and validate the proposed autonomous navigation 
and motion planning algorithms on the ARA robots in both robotics-based simulation 
environment, mocked steel structures and real bridges. We will finalize these robotic 
developments and select at least one to two prototypes for commercialization. In the fifth 
year of this project, we has founded a start-up company (spin-out from UNR) called 
Automated Inspection Robots (AIR) Corporation to allow the team to translate the 
developed technology to the market]]></description>
      <pubDate>Thu, 17 Sep 2020 18:34:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/1740053</guid>
    </item>
    <item>
      <title>Development of a Data-Driven Optimal Controller Based on Adaptive Dynamic Programming</title>
      <link>https://rip.trb.org/View/1641101</link>
      <description><![CDATA[Through vehicle-to-vehicle (V2V) communication, both human-driven and autonomous vehicles can actively exchange data, such as velocities and bumper-to-bumper distances. By employing the shared data, control laws with improved performance can be designed for connected and autonomous vehicles (CAVs). The research proposes an adaptive optimal control design method for a platoon mixed with multiple preceding human-driven vehicles and one CAV at the tail, while taking into account human-vehicle interaction and heterogeneous driver behavior. It is shown that, by using reinforcement-based learning and adaptive dynamic programming techniques, a near-optimal controller can be learned from real-time data for CAV with V2V communications, and do so without precise knowledge of car-following parameters of any driver in the platoon. The proposed method allows the CAV controller to adapt to different platoon dynamics caused by unknown and heterogeneous driver-dependent parameters. To improve safety performance during the learning process, our off-policy learning algorithm can leverage both historical data and data collected in real-time, which leads to considerably reduced learning time duration. The effectiveness and efficiency of our proposed method is demonstrated by rigorous proofs and microscopic traffic simulations.]]></description>
      <pubDate>Tue, 30 Jul 2019 08:15:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1641101</guid>
    </item>
    <item>
      <title>Dynamic Trajectory Control and Signal Coordination for a Signalized Arterial with Significant Freight Traffic</title>
      <link>https://rip.trb.org/View/1552816</link>
      <description><![CDATA[Freight traffic affects the performance of a road network in a more sensitive and significant way compared to other traffic with respect to mobility, environment, and safety. This is due to the complexity of the characteristics of the mixed-class traffic. For example, heavy trucks need extra distance and time for deceleration and acceleration, and their interactions with conventional vehicles can cause more uncertainties to the traffic flow due to their different lengths, speeds, and acceleration performances. As a result, a traffic bottleneck may appear on road segment where freight traffic is significant even though the overall volume is not high enough to cause congestion if the traffic composition is not truck heavy. What is more, for a signalized arterial, the coordination often fails when the traffic is composed of a large portion of trucks. This has been shown in the research of Freight Mobility Research Institute (FMRI) first-year project. In year II this proposed project tries to look into the area of freight signal priority control, which is related to control and information technology.
Given the existing infrastructure, the improvement of freight traffic operation can be conducted at the tactical and operational level. In the research of FMRI first-year project, the connected vehicle techniques are assumed so that signal information and estimated queuing information are treated as known inputs for the optimization of individual truck speed profile. Besides, in FMRI first-year project, the optimization is conducted for individual trucks given signal information and estimated queuing information as inputs. This second-year FMRI research focuses on the vehicle dynamics of trucks. In this proposed research, multiple trucks dynamic trajectories and their interactions with the conventional cars will be investigated, and an analytical tool of traffic flow performance will be developed. Based on the analytical models, control strategies are developed to schedule the trajectories of trucks/cars dynamically to improve the mobility of a corridor, assisted by the new coordination strategies of signals.
Two levels of the strategies are defined in the scope of this research: At the first level, the strategy takes into account the vehicle dynamics and optimizes the trajectories of trucks/cars given signal timing plans, signal states and traffic conditions. Real-time decisions and behaviors of vehicle motions and their interactions with other vehicles and with the infrastructure are analyzed. At the second level, the strategies will model the interactions among trucks and conventional vehicles while a new coordination strategy of signals is established, in which the timing variables are used as decision variables. While developing these strategies, the factors of robustness, optimality, predictability will be considered if necessary, and realistic factors such as truck market penetration rate, truck characteristics, speed variability, and signal types will be considered.]]></description>
      <pubDate>Wed, 03 Oct 2018 15:28:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/1552816</guid>
    </item>
    <item>
      <title>Unified 4D Trajectory Approach for Integrated Management</title>
      <link>https://rip.trb.org/View/1537166</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Wed, 22 Aug 2018 13:02:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/1537166</guid>
    </item>
    <item>
      <title>Task 185: Unified 4D Trajectory Approach for Integrated Management of Commercial Air and Space Traffic</title>
      <link>https://rip.trb.org/View/1532771</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Fri, 17 Aug 2018 11:41:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1532771</guid>
    </item>
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