Search NASA⌕ Search

SEARCH · Search NASA

Results for “traffic complexity”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Traffic Management Advisor: Iterative Field Development and Assessment at Multiple Sites

Previous studies have demonstrated the necessity of involving users in the development of automation aids, especially for complex domains such as air traffic control (ATC). Traditional development only demanded a single field test to validate a completed system, but a more iterative combination of development and assessment ensures that the technology meets the requirements of its application domain. Exposure across an adequate spectrum of field users is also required during development, and the use of multiple development sites provides an opportunity to consider individual facility cultures as they relate to implementation strategies. The development of the Center/TRACON Automation System (CTAS) Traffic Management Advisor (TMA) at the Denver and Dallas ATC facilities demonstrates successful iterative development and assessment at multiple field sites. The use of field development changes the nature of assessment. As development progresses, periodic assessments are required to validate that system development is progressing along an appropriate track. In the development of the TMA, assessments were performed based on software in the field, and input from traffic managers was analyzed and incorporated into subsequent releases of the TMA, to be reassessed in the field. This has led to a tool with operational suitability and broad user acceptance. Assessment at multiple sites provides a more generalizable perspective that allows the production of a system that is both generic enough to be used at different sites and tailored enough to be of use at any site. In addition to providing a better understanding of specific facility requirements, the use of multiple assessment sites in the development of TMA has provided an opportunity to consider individual facility operations, procedures and cultures as they relate to development and implementation strategies.

Sanford, Beverly D.↗
DOE OSTI2025

Adaptive continuity-preserving simplification of street networks

Street network data is widely used to study human-based activities and urban structure. Often, these data are geared towards transportation applications, which require highly granular, directed graphs that capture the complex relationships of potential traffic patterns. While this level of network detail is critical for certain fine-grained mobility models, it represents a hindrance for studies concerned with the morphology of the street network. For the latter case, street network simplification — the process of converting a highly granular input network into its most simple morphological form — is a necessary, but highly tedious preprocessing step, especially when conducted manually. In this manuscript, we develop and present a novel adaptive algorithm for simplifying street networks that is both fully automated and able to mimic results obtained through a manual simplification routine. The algorithm — available in the neatnet Python package — outperforms current state-of-the-art procedures when comparing those methods to manually, human-simplified data, while preserving network continuity.

Python↗
NASA NTRS2011

Reducing Traffic Congestions by Introducing CACC-Vehicles on a Multi-Lane Highway Using Agent-Based Approach

Traffic congestion is an ongoing problem of great interest to researchers from different areas in academia. With the emerging technology for inter-vehicle communication, vehicles have the ability to exchange information with predecessors by wireless communication. In this paper, we present an agent-based model of traffic congestion and examine the impact of having CACC (Cooperative Adaptive Cruise Control) embedded vehicle(s) on a highway system consisting of 4 traffic lanes without overtaking. In our model, CACC vehicles adapt their acceleration/deceleration according to vehicle-to-vehicle inter-communication. We analyze the average speed of the cars, the shockwaves, and the evolution of traffic congestion throughout the lifecycle of the model. The study identifies how CACC vehicles affect the dynamics of traffic flow on a complex network and reduce the oscillatory behavior (stop and go) resulting from the acceleration/deceleration of the vehicles.

Arnaout, Georges M.↗
NASA NTRS2017

Applications of Anomaly Detection and Precursor Identification in Airspace Operations

As we continue to advance the U.S. National Airspace into the next generation of air traffic, we face challenges in both increase in complexity, as well as, a significant growth in traffic volume. Addressing these challenges, while maintaining the same level of safety is an important application of data mining. Because of these significant shifts in airspace design and usage there is a need to identify current and emergent safety risks along with their potential precursors. In recent years NASA has made advancements in developing scalable methods to address this effort in the Big Data paradigm. Multiple kernel anomaly detection approaches have been employed on both surveillance radar data and flight operational quality assurance data to identify operationally significant safety risks. Additionally, events have been explored with a recently developed precursor identification tool to discover states that reveal an increased probability of a safety event. These tools can be used to discover emerging safety risks that may not be currently monitored, which allows for mitigation tactics to be employed and ultimately make the overall airspace safer. This talk will discuss an overview of these methods and a discussion of the findings.

anomaly detection↗
NASA NTRS2020

Will We Control the Automation, or Will It Control Us?

The purpose of air traffic control (ATC) is to provide a safe and efficient service for all air traffic. Since its inception, ATC has evolved in response to user needs, achieving exceptionally high standards of safety in a context of shifting complexity and density of air traffic operations. But predicted changes in societal demands, technological advancements and airspace-user needs create new challenges as we look to the mid and far term. This portion of the white paper presents some of the mid and far term visions, out to 2050, for upcoming challenges and changes to air traffic control demands and how Human Factors can support the development of air traffic control to safely and efficiently meet the needs of airspace users.

air traffic control↗
DOE OSTI2024

From Sim to Real: A Pipeline for Training and Deploying Traffic Smoothing Cruise Controllers

Designing and validating controllers for connected and automated vehicles to enhance traffic flow presents significant challenges, from the complexity of replicating real-world stop-and-go traffic dynamics in simulation, to the intricacies involved in transitioning from simulation to actual deployment. In this work, we present a full pipeline from data collection to controller deployment. Specifically, we collect 772 km of driving data from the I-24 in Tennessee, and use it to build a one-lane simulator, placing simulated vehicles behind real-world trajectories. Using policy-gradient methods with an asymmetric critic, we improve fuel efficiency by over 10% when simulating congested scenarios. Our comprehensive approach includes reinforcement learning for controller training, software verification, hardware validation and setup, and navigating various sim-to-real challenges. Furthermore, we analyze the controller's behavior and wave-smoothing properties, and deploy it on four Toyota Rav4’s in a real-world validation experiment on the I-24. Lastly, we release the driving dataset, the simulator and the trained controller, to enable future benchmarking and controller design.

42 ENGINEERING↗
NASA NTRS2010

Functional Allocation for Ground-Based Automated Separation Assurance in NextGen

As part of an ongoing research effort into functional allocation in a NextGen environment, a controller-in-the-loop study on ground-based automated separation assurance was conducted at NASA Ames' Airspace Operations Laboratory in February 2010. Participants included six FAA front line managers, who are currently certified professional controllers and four recently retired controllers. Traffic scenarios were 15 and 30 minutes long where controllers interacted with advanced technologies for ground-based separation assurance, weather avoidance, and arrival metering. The automation managed the separation by resolving conflicts automatically and involved controllers only by exception, e.g., when the automated resolution would have been outside preset limits. Results from data analyses show that workload was low despite high levels of traffic, Operational Errors did occur but were closely tied to local complexity, and safety acceptability ratings varied with traffic levels. Positive feedback was elicited for the overall concept with discussion on the proper allocation of functions and trust in automation.

Prevot, Thomas↗
NASA NTRS2016

Cardiac-Activity Measures for Assessing Airport Ramp-Tower Controller's Workload

Heart rate (HR) and heart rate variability (HRV) potentially offer objective, continuous, and non-intrusive measures of human-operator's mental workload. Such measurement capability is attractive for workload assessment in complex laboratory simulations or safety-critical field testing. The present study compares mean HR and HRV data with self-reported subjective workload ratings collected during a high-fidelity human-in-the-loop simulation of airport ramp traffic control operations, which involve complex cognitive and coordination tasks. Mean HR was found to be weakly sensitive to the workload ratings, while HRV was not sensitive or even contradictory to the assumptions. Until more knowledge on stress response mechanisms of the autonomic nervous system is obtained, it is recommended that these cardiac-activity measures be used with other workload assessment tools, such as subjective measures.

Hayashi, Miwa↗
NASA NTRS2016

Cardiac-Activity Measures for Assessing Airport Ramp-Tower Controller's Workload

Heart rate (HR) and heart rate variability (HRV) potentially offer objective, continuous, and non-intrusive measures of human-operators mental workload. Such measurement capability is attractive for workload assessment in complex laboratory simulations or safety-critical field testing. The present study compares mean HR and HRV data with self-reported subjective workload ratings collected during a high-fidelity human-in-the-loop simulation of airport ramp traffic control operations, which involve complex cognitive and coordination tasks. Mean HR was found to be weakly sensitive to the workload ratings, while HRV was not sensitive or even contradictory to the assumptions. Until more knowledge on stress response mechanisms of the autonomic nervous system is obtained, it is recommended that these cardiac-activity measures be used with other workload assessment tools, such as subjective measures.

Hayashi, Miwa↗
NASA NTRS1978

The effects of alcohol on driver performance in a decision making situation

The results are reviewed of driving simulator and in-vehicle field test experiments of alcohol effects on driver risk taking. The objective was to investigate changes in risk taking under alcoholic intoxication and relate these changes to effects on traffic safety. The experiments involved complex 15 minute driving scenarios requiring decision making and steering and speed control throughout a series of typical driving situations. Monetary rewards and penalties were employed to simulate the real-world motivations inherent in driving. A full placebo experimental design was employed, and measures related to traffic safety, driver/vehicle performance and driver behavior were obtained. Alcohol impairment was found to increase the rate of accidents and speeding tickets. Behavioral measures showed these traffic safety effects to be due to impaired psychomotor performance and perceptual distortions. Subjective estimates of risk failed to show any change in the driver's willingness to take risks when intoxicated.

Allen, R. W.↗
NASA NTRS2014

Evolutionary Agent-Based Simulation of the Introduction of New Technologies in Air Traffic Management

Accurate simulation of the effects of integrating new technologies into a complex system is critical to the modernization of our antiquated air traffic system, where there exist many layers of interacting procedures, controls, and automation all designed to cooperate with human operators. Additions of even simple new technologies may result in unexpected emergent behavior due to complex human/ machine interactions. One approach is to create high-fidelity human models coming from the field of human factors that can simulate a rich set of behaviors. However, such models are difficult to produce, especially to show unexpected emergent behavior coming from many human operators interacting simultaneously within a complex system. Instead of engineering complex human models, we directly model the emergent behavior by evolving goal directed agents, representing human users. Using evolution we can predict how the agent representing the human user reacts given his/her goals. In this paradigm, each autonomous agent in a system pursues individual goals, and the behavior of the system emerges from the interactions, foreseen or unforeseen, between the agents/actors. We show that this method reflects the integration of new technologies in a historical case, and apply the same methodology for a possible future technology.

Simulation↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
DOE OSTI2026

Optimization-based approaches to control of connected and automated vehicles: Principles, complexities, applications, challenges, and outlook

Safe and optimal motion control for connected and automated vehicles (CAVs) poses a fundamental optimization challenge at the intersection of system complexity, environmental uncertainty, and stringent real-time constraints. Existing surveys address this challenge in isolation – focusing either on specific control techniques or individual uncertainty sources – without providing a unified framework that characterizes the trade-offs among computational tractability, performance verifiability, and adaptive generalization across paradigms. This review addresses that gap by presenting a cohesive analytical framework concentrated on the decision-making and trajectory optimization layers of the CAV autonomy stack. We systematically analyze three major optimization paradigms – first-principles model-based optimization, data-driven methods, and hybrid synergistic architectures – evaluating each against four core complexity axes: problem formulation, constraint handling, optimality guarantees, and robustness. Key applications including platooning, trajectory planning, collision avoidance, and cooperative control are examined to reveal recurring methodological patterns and critical operational constraints that limit real-world performance. Our synthesis identifies verifiable hybrid architectures, incentive-aligned multi-agent cooperation, and hardware-algorithm co-design as the defining research frontiers, and distills a targeted agenda for developing CAV control systems that are simultaneously safe, computationally efficient, and deployable in the full complexity of real-world traffic environments.

Muzahid, Abu Jafar Md [University of Tennessee, Kn↗
NASA NTRS1992

Modular techniques for dynamic fault-tree analysis

It is noted that current approaches used to assess the dependability of complex systems such as Space Station Freedom and the Air Traffic Control System are incapable of handling the size and complexity of these highly integrated designs. A novel technique for modeling such systems which is built upon current techniques in Markov theory and combinatorial analysis is described. It enables the development of a hierarchical representation of system behavior which is more flexible than either technique alone. A solution strategy which is based on an object-oriented approach to model representation and evaluation is discussed. The technique is virtually transparent to the user since the fault tree models can be built graphically and the objects defined automatically. The tree modularization procedure allows the two model types, Markov and combinatoric, to coexist and does not require that the entire fault tree be translated to a Markov chain for evaluation. This effectively reduces the size of the Markov chain required and enables solutions with less truncation, making analysis of longer mission times possible. Using the fault-tolerant parallel processor as an example, a model is built and solved for a specific mission scenario and the solution approach is illustrated in detail.

Patterson-Hine, F. A.↗
NASA NTRS1994

Understanding the determinants of problem-solving behavior in a complex environment

It is often argued that problem-solving behavior in a complex environment is determined as much by the features of the environment as by the goals of the problem solver. This article explores a technique to determine the extent to which measured features of a complex environment influence problem-solving behavior observed within that environment. In this study, the technique is used to determine how complex flight deck and air traffic control environment influences the strategies used by airline pilots when controlling the flight path of a modern jetliner. Data collected aboard 16 commercial flights are used to measure selected features of the task environment. A record of the pilots' problem-solving behavior is analyzed to determine to what extent behavior is adapted to the environmental features that were measured. The results suggest that the measured features of the environment account for as much as half of the variability in the pilots' problem-solving behavior and provide estimates on the probable effects of each environmental feature.

Casner, Stephen A.↗
NASA NTRS2015

Terminal Sequencing and Spacing (TSS)

The Federal Aviation Administration's (FAA) Next Generation Air Transportation System (or NextGen) is being designed to support the predicted increases in traffic volume and to increase the capacity, efficiency and safety of the National Airspace System (NAS). The Federal Aviation Administration (FAA) identifies Performance-Based Navigation (PBN) as a key enabling capability of NextGen and is actively publishing PBN procedures at major airports throughout the United States. Standard Terminal Arrival Routes (STARs), procedures, and approaches are designed to facilitate fuel-efficient continuous descent operations. However, their use is limited during periods of high traffic demand due to the complexity of merging multiple streams of aircraft to the same airport. As a result, most arrivals in the Terminal Radar Approach Control (TRACON) area continue to be controlled using radar vectoring and step-down descents, resulting in high workload for controllers and diverting aircraft from efficient PBN trajectories. To address this issue, NASA developed the Terminal Sequencing and Spacing (TSS) system, an advanced arrival management technology that combines time-based scheduling and controller-based precision spacing tools. TSS is a ground-based controller automation tool that facilitates sequencing and merging arrivals on Performance-Based Navigation (PBN) routes, especially during highly congested demand periods. The two main components of TSS are: 1) a scheduler that de-conflicts merging arrivals in the terminal area by computing appropriate arrival times to the runway threshold and upstream terminal merge points, and 2) a set of Controller-Managed Spacing (CMS) decision support tools to efficiently assist schedule conformance. Sixteen high-fidelity human-in-the-loop simulations involving more than five hundred hours of evaluation time, were conducted to mature TSS from proof-of-concept design to a fully functional prototype. Results indicate high controller use and acceptability of the CMS tools as well as improved PBN route conformance (Figure 2). The TSS technology was transferred to the FAA in 2014, and it is targeted for deployment to several busy airports in the U.S. starting in 2018. Potential enhancements to TSS using DataComm will also be presented.

controller-managed spacing↗