Results for “traffic congestion” — Page 6

DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2004

Improving VHF Spectrum Utilization

Limited VHF communications system capacity and increasing air traffic results in congestion of the aviation VHF spectrum. The voice communications errors and delayed channel access create system congestion and air traffic delays. Regulatory subdivision of bands for specific functions limits flexibility in the frequency usage. The objective of this viewgraph presentation is to identify near/mid/far term technologies to improve the performance and spectrum efficiency of current and emerging VHF communications systems. Select technologies with the highest potential, perform research and development to bring them to implementation stage.

Andro, Monty↗

NASA NTRSDate not supplied

Initial Validation of a Simulation System for Studying Interoperability in Future Air Traffic Management Systems

Future air traffic management systems will need to accommodate large numbers of increasingly diverse air vehicles with different operating paradigms. To support this trend, they will digitally share copious amounts of information via a common communication architecture. Operators will deploy programs that create and negotiate flight plans via the architecture’s communication protocols. These programs will autonomously make decisions that must be arbitrated by the architecture and robust to uncertainty. To study interoperability in air traffic management, a new airspace simulation system was composed by integrating a legacy airspace simulation, an air traffic control model, and a new research communication architecture. It was used to evaluate air traffic management concepts by adapting it to handle congested arrival traffic at Newark Liberty International Airport and executing simulations. Results demonstrated the ability of the simulation system to model in detail strategic traffic flow management, predeparture flight planning, and air traffic control working in concert. Subsequent studies can use the simulation system to study interoperability, autonomy, digital communication, and uncertainty in future air traffic management systems.

aircraft scheduling,traffic flow management,autono↗

NASA NTRSDate not supplied

Initial Validation of a Simulation System for Studying Interoperability in Future Air Traffic Management Systems

Future air traffic management systems will need to accommodate large numbers of increasingly diverse air vehicles with different operating paradigms. To support this trend, they will digitally share copious amounts of information via a common communication architecture. Operators will deploy programs that create and negotiate flight plans via the architecture’s communication protocols. These programs will autonomously make decisions that must be arbitrated by the architecture and robust to uncertainty. To study interoperability in air traffic management, a new airspace simulation system was composed by integrating a legacy airspace simulation, an air traffic control model, and a new research communication architecture. It was used to evaluate air traffic management concepts by adapting it to handle congested arrival traffic at Newark Liberty International Airport and executing simulations. Results demonstrated the ability of the simulation system to model in detail strategic traffic flow management, predeparture flight planning, and air traffic control working in concert. Subsequent studies can use the simulation system to study interoperability, autonomy, digital communication, and uncertainty in future air traffic management systems.

air traffic control↗

NASA NTRS2021

Initial Validation of a Simulation System for Studying Interoperability in Future Air Traffic Management Systems

Future air traffic management systems will need to accommodate large numbers of increasingly diverse air vehicles with different operating paradigms. To support this trend, they will digitally share copious amounts of information via a common communication architecture. Operators will deploy programs that create and negotiate flight plans via the architecture’s communication protocols. These programs will autonomously make decisions that must be arbitrated by the architecture and robust to uncertainty. To study interoperability in air traffic management, a new airspace simulation system was composed by integrating a legacy airspace simulation, an air traffic control model, and a new research communication architecture. It was used to evaluate air traffic management concepts by adapting it to handle congested arrival traffic at Newark Liberty International Airport and executing simulations. Results demonstrated the ability of the simulation system to model in detail strategic traffic flow management, predeparture flight planning, and air traffic control working in concert. Subsequent studies can use the simulation system to study interoperability, autonomy, digital communication, and uncertainty in future air traffic management systems.

air traffic control↗

DOE OSTI2021

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement. Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Capacitated p -hub approach for park-and-ride facility location problem under nested logit demand function: polyhedral approaches

By generalizing the unconstrained p-hub approach for the park-and-ride (P&R) facility location problem under the multinomial logit demand function, the capacitated p-hub approach for the problem under the nested logit demand function captures a broader range of real-world cases. To solve this problem optimally, we introduce a mixed-integer linear program and accelerate its solution by enhancing the branch-and-cut procedure. To address the problem at a large scale, we introduce two other polyhedral approaches: variable neighborhood search (VNS) and adaptive randomized rounding (ARR). Downtown areas in Seoul have a high modal share of public transportation and congested road traffic, yet P&R has not been widely implemented. Therefore, we apply the ARR procedure to solve a real-world problem using traffic and geographic data from the Seoul metropolitan area. ARR performs better than VNS and addresses real-world cases. The solutions obtained by ARR present a phased expansion plan that encourages policymakers to start installing a small number of P&Rs immediately.

Capacitated p-hub approach↗

NASA NTRS1971

Economic utilization of general aviation airport runways

The urban general aviation airport economics is studied in detail. The demand for airport services is discussed, and the different types of users are identified. The direct cost characteristics of the airport are summarized; costs to the airport owner are largely fixed, and, except at certain large airports, weight is not a significant factor in airport costs. The efficient use of an existing airport facility is explored, with the focus on the social cost of runway congestion as traffic density at the airport build up and queues form. The tradeoff between aircraft operating costs and airport costs is analyzed in terms of runway length. The transition from theory to practice is treated, and the policy of charging prices only on aircraft storage and fuel is felt likely to continue. Implications of the study from the standpoint of public policy include pricing that spreads traffic peaks to improve runway utilization, and pricing that discriminates against aircraft requiring long runways and causes owners to adopt V/STOL equipment.

Piper, R. R.↗

NASA NTRS1982

Quiet Short-Haul Research Airplane (QSRA) model select panel functional description

The QSRA, when equipped with programmable color cathode ray tube displays, a head up display, a general purpose digital computer and a microwave landing system receiver, will provide a capability to do handling qualities studies and terminal area operating systems experiments as well as to enhance an experimenter's ability to obtain repeatable aircraft performance data. The operating systems experiments include the capability to generate minimum fuel approach and departure paths and to conduct precision approaches to a STOLport runway. The mode select panel is designed to provide both the flexibility needed for a variety of flight test experiments and the minimum workload operation required by pilots flying into congested terminal traffic areas.

Watson, D. M.↗

NASA NTRS2003

Cost Effective Measures to Reduce CO2 Emissions in the Air Freight Sector

This paper presents cost effective measures to reduce CO2 emissions in the air freight sector. One door-to-door transport chain is studied in detail from a Scandinavian city to a city in southern Europe. The transport chain was selected by a group of representatives from the air freight sector in order to encompass general characteristics within the sector. Three different ways of shipping air cargo are studied, i.e., by air freighter, as belly freight (in passenger aircrafts) and trucking. CO2 emissions are calculated for each part of the transport chain and its relative importance towards the total amount CO2 emitted during the whole transport chain is shown. It is confirmed that the most CO2 emitting part of the transport chain is the actual flight and that it is in the take-off and climbing phases that most fuel are burned. It is also known that the technical development of aircraft implies a reduction in fuel consumption for each new generation of aircraft. Thus, the aircraft manufacturers have an important role in this development. Having confirmed these observations, this paper focuses on other factors that significantly affects the fuel consumption. Analyzed factors are, e.g., optimization of speed and altitude, traffic management, congestion on and around the airfields, tankering, "latest acceptance time" for goods and improving the load factor. The different factors relative contribution to the total emission levels for the transport chain has been estimated.

Blinge, Magnus↗

NASA NTRSDate not supplied

Weather Guidance for UAS Urban Medical Transport Missions

Unmanned aircraft will revolutionize healthcare services by providing efficient and expeditious delivery of life-saving transplant organs and supplies to hospitals in urban environments, where road traffic and congestion can slow delivery times and endanger lives. Bell has developed the Autonomous Pod Transport (APT) vehicle to serve this market, with entry into service in mid-2020s. Adverse weather conditions can introduce risks and inefficiencies in urban environments leading to flight delays and cancellations. In 2018, National Aeronautics and Space Administration (NASA) and Bell entered into a cooperative agreement, under the Systems Integration and Operationalization (SIO) program to tackle key challenges to enable future commercial unmanned aircraft operations. The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) at the University of Massachusetts, Amherst, joined this team to demonstrate weather avoidance technologies for remotely piloted and autonomous vehicles. CASA has developed the ‘City Warn’ hazard alerting platform to gather weather information from various weather sensors and models, and based on user (or mission) preferences for alerting and on user (or unmanned aircraft) locations, the platform shares timely weather intelligence with users and the systems used by them for remote operations. This presentation discusses the weather avoidance solution developed during this project, leading up to the demonstration of the end-to-end system in Fall 2020. The presentation will cover the following topics: 1) goals related to weather avoidance 2) the design requirements process, including the results of pilot interviews, 3) weather observation and avoidance needs 4) selection of regional and national weather data sets 5) design of the weather graphical interface and 6) considerations for real-time weather alerting. The end-to-end system that was developed will be discussed, along with the results from the demonstration flight. The presentation will conclude with insights from the project team on lessons learned and best practices on weather avoidance technologies for the industry going ahead.

weather avoidance↗

NASA NTRS2021

Weather Guidance for UAS Urban Medical Transport Missions

Unmanned aircraft will revolutionize healthcare services by providing efficient and expeditious delivery of life-saving transplant organs and supplies to hospitals in urban environments, where road traffic and congestion can slow delivery times and endanger lives. Bell has developed the Autonomous Pod Transport (APT) vehicle to serve this market, with entry into service in mid-2020s. Adverse weather conditions can introduce risks and inefficiencies in urban environments leading to flight delays and cancellations. In 2018, National Aeronautics and Space Administration (NASA) and Bell entered into a cooperative agreement, under the Systems Integration and Operationalization (SIO) program to tackle key challenges to enable future commercial unmanned aircraft operations. The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) at the University of Massachusetts, Amherst, joined this team to demonstrate weather avoidance technologies for remotely piloted and autonomous vehicles. CASA has developed the ‘City Warn’ hazard alerting platform to gather weather information from various weather sensors and models, and based on user (or mission) preferences for alerting and on user (or unmanned aircraft) locations, the platform shares timely weather intelligence with users and the systems used by them for remote operations. This presentation discusses the weather avoidance solution developed during this project, leading up to the demonstration of the end-to-end system in Fall 2020. The presentation will cover the following topics: 1) goals related to weather avoidance 2) the design requirements process, including the results of pilot interviews, 3) weather observation and avoidance needs 4) selection of regional and national weather data sets 5) design of the weather graphical interface and 6) considerations for real-time weather alerting. The end-to-end system that was developed will be discussed, along with the results from the demonstration flight. The presentation will conclude with insights from the project team on lessons learned and best practices on weather avoidance technologies for the industry going ahead.

weather avoidance↗

NASA NTRSDate not supplied

Effect of Airspace Characteristics on Urban Air Mobility Airspace Capacity

As interest in urban air mobility grows, increasingly high density traffic poses a challenge for the transition to safe, efficient, and timely operations. Hundreds of simultaneously demanded flights will need to share the same fleet, infrastructure, and constrained airspace. This paper investigates the feasibility of effectively managing high density traffic in congested urban environments by optimizing the design of airspace route structures. Demand estimations for the Dallas-Fort Worth and Los Angeles areas are used to evaluate potential improvements to airspace route design with the goal to accommodate the projected level of demand.

Urban Air Mobility↗

NASA NTRSDate not supplied

Effect of Airspace Characteristics on Urban Air Mobility Airspace Capacity

As interest in urban air mobility grows, increasingly high density traffic poses a challenge for the transition to safe, efficient, and timely operations. Hundreds of simultaneously demanded flights will need to share the same fleet, infrastructure, and constrained airspace. This paper investigates the feasibility of effectively managing high density traffic in congested urban environments by optimizing the design of airspace route structures. Demand estimations for the Dallas-Fort Worth and Los Angeles areas are used to evaluate potential improvements to airspace route design with the goal to accommodate the projected level of demand.

Urban Air Mobility↗

NASA NTRS2024

Presound: UAV Diagnostic System Enabled by Vibration-Based Machine Learning

A low-weight, inexpensive small unmanned aerial system (sUAS) that takes off, performs a mission, lands, and safely stows and recharges itself has myriad future applications ranging from agricultural imaging to last-mile package delivery. Likewise, Urban Air Mobility (UAM) systems will enable people to take air taxis from point to point in cities, rapidly moving commuters long distances without concern for road traffic and congestion. Fully electric aviation systems will be cleaner and quieter than ground transport. Cities could eliminate cars and buses, and convert roads to higher capacity bike and pedestrian throughways. Yet, for sUAS as well as UAM, system reliability and assurance is a limiting factor to deploying affordable autonomous flight systems. For this bright future of aviation to be realized, aircraft must be able to autonomously and accurately self-diagnose health issues both before takeoff and during flight. The GreenSight PreSound system is designed to identify defects on aircraft through intelligent analysis of vibration. It accomplishes this by measuring structural vibrations induced by the vehicle’s own propellers, and analyzing that data using a machine learning model that determines whether a defect is present. The PreSound system is designed to require no human oversight, and to operate across a wide array of vehicles through re-training of the model for each target aircraft. PreSound has been developed and seen limited early success using data collected from the GreenSight Dreamer sUAS, a 5lb quadrotor vehicle designed for aerial imaging applications. The final detection model, trained on data with props spinning at 50% throttle, achieves excellent performance with over 99% average accuracy in detecting blade damage using a single FFT vector input. It demonstrates the ability to generalize to new types of blade damage, correctly classifying a different type of blade damage with 98% accuracy. Full test pulses were classified with 100% accuracy, and in live testing, all sets of data during blade movement were classified accurately with over 95% confidence. When trained on in-flight data, the same model achieves an average accuracy of 85% in distinguishing between undamaged and blade-damaged states in flight. The authors believe that these accuracies show significant potential of this approach to expand unmanned flight safety, with significant potential benefits in accelerating Advanced Aerial Mobility (AAM) and UAM aviation applications.

UAS↗

DOE OSTI2025

Design, Preparation, and Execution of the 100-AV Field Test for the CIRCLES Consortium: Methodology and Implementation of the Largest Mobile Traffic Control Experiment to Date

This article presents the comprehensive design, setup, execution, and evaluation of the MegaVanderTest (MVT) experiment conducted by the Congestion Impacts Reduction via CAV-in-the-Loop Lagrangian Energy Smoothing (CIRCLES) Consortium, which aimed to mitigate traffic congestion using partially autonomous vehicles (AVs) (see “Summary”). The experiment involved 100 vehicles on Nashville’s Interstate 24 (I-24) highway, utilizing various control algorithms to smooth stop-and-go traffic waves. The execution of the MVT experiment required a coordinated effort from multiple teams. This article details the meticulous planning process, the coordinated efforts of multiple teams, and the innovative use of a dynamic agent-based simulation framework for traffic evaluation. Here, the contributions of this work include demonstrating and providing a detailed roadmap for large-scale live traffic experiments, illustrating the lessons learned from the MVT experiment, and introducing the other articles in this issue and their complementary relationship in the MVT experiment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

"Designing, simulating, and performing the 100-AV field test for the CIRCLES consortium: Methodology and Implementation of the Largest mobile traffic control experiment to date"

Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) Consortium conducted MegaVanderTest (MVT), a live traffic control experiment involving 100 vehicles near Nashville, TN, USA. This article is a tutorial for developing analytical and simulation-based tools essential for designing and executing a live traffic control experiment like the MVT. It presents an overview of the proposed roadmap and various procedures used in designing, monitoring, and conducting the MVT, which is the largest mobile traffic control experiment at the time. The design process is aimed at evaluating the impact of the CIRCLES AVs on surrounding traffic. The article discusses the agent-based traffic simulation framework created for this evaluation. A novel methodological framework is introduced to calibrate this microsimulation, aiming to accurately capture traffic dynamics and assess the impact of adding 100 vehicles to existing traffic. The calibration model's effectiveness is verified using data from a six-mile section of Nashville's I-24 highway. The results indicate that the proposed model establishes an effective feedback loop between the optimizer and the simulator, thereby calibrating flow and speed with different spatiotemporal characteristics to minimize the error between simulated and real-world data. Finally, We simulate AVs in multiple scenarios to assess their effect on traffic congestion. This evaluation validates the AV routes, thereby contributing to the execution of a safe and successful live traffic control experiment via AVs.

Ameli, Mostafa↗

NASA NTRS2005

Analysis of Aircraft Clusters to Measure Sector-Independent Airspace Congestion

The Distributed Air/Ground Traffic Management (DAG-TM) concept of operations* permits appropriately equipped aircraft to conduct Free Maneuvering operations. These independent aircraft have the freedom to optimize their trajectories in real time according to user preferences; however, they also take on the responsibility to separate themselves from other aircraft while conforming to any local Traffic Flow Management (TFM) constraints imposed by the air traffic service provider (ATSP). Examples of local-TFM constraints include temporal constraints such as a required time of arrival (RTA), as well as spatial constraints such as regions of convective weather, special use airspace, and congested airspace. Under current operations, congested airspace typically refers to a sector(s) that cannot accept additional aircraft due to controller workload limitations; hence Dynamic Density (a metric that is indicative of controller workload) can be used to quantify airspace congestion. However, for Free Maneuvering operations under DAG-TM, an additional metric is needed to quantify the airspace congestion problem from the perspective of independent aircraft. Such a metric would enable the ATSP to prevent independent aircraft from entering any local areas of congestion in which the flight deck based systems and procedures may not be able to ensure separation. This new metric, called Gaggle Density, offers the ATSP a mode of control to regulate normal operations and to ensure safety and stability during rare-normal or off-normal situations (e.g., system failures). It may be difficult to certify Free Maneuvering systems for unrestricted operations, but it may be easier to certify systems and procedures for specified levels of Gaggle Density that could be monitored by the ATSP, and maintained through relatively minor flow-rate (RTA type) restrictions. Since flight deck based separation assurance is airspace independent, the challenge is to measure congestion independent of sector boundaries. Figure 1 , reproduced from Ref. 1, depicts an example traffic situation. When the situation is analyzed by sector boundaries (left side of figure), a Dynamic Density metric would identify excessive congestion in the central sector. When the same traffic situation is analyzed independent of sector boundaries (right side of figure), a Gaggle Density metric would identify congestion in two dynamically defined areas covering portions of several sectors. The first step towards measuring airspace-independent congestion is to identify aircraft clusters, i.e., groups of closely spaced aircraft. The objective of this work is to develop techniques to detect and classify clusters of aircraft.

Bilimoria, Karl D.↗