Relevant bibliographies by topics / Air traffic data

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Air traffic data'

Author: Grafiati
Published: 22 February 2025

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Journal articles on the topic "Air traffic data"

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Desimoni, Federico, Sergio Ilarri, Laura Po, Federica Rollo, and Raquel Trillo-Lado. "Semantic Traffic Sensor Data: The TRAFAIR Experience." Applied Sciences 10, no. 17 (August 25, 2020): 5882. http://dx.doi.org/10.3390/app10175882.

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Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.
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Manvelidze, A. B. "Air lines network modelling algorithm." Strategic decisions and risk management, no. 6 (February 13, 2018): 22–29. http://dx.doi.org/10.17747/2078-8886-2017-6-22-29.

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This analysis is dedicated to find out methods for setting of route networks where new aircraft can be effectively put into service. The conception of this analysis is based on the idea of so called connectivity principle for airports connected by passenger traffic with each other.For the passenger traffic analysis the author took passenger traffic data by federal districts starting from the Far East. Then consequently the data for Siberian, Ural, Wolga, Northwestern, Central, Southern and North Caucasian federal districts were analyzed. Passenger traffic to the Crimea was treated separately. Detailed specifications of passenger traffics were provided in order to determine the connections between airports both within federal districts and beyond them and with neighboring areas in western direction. Query of routes was done based on limitations for non-stop flight range and on minimum and maximum (for significant traffics) flight frequencies.The analysis approach lets us concentrate attention on those airlines which at best fit for putting into service of chosen aircraft. Also this method permits to determine the routes with currently insufficient or low traffics but where there’s a definite growth potential. When analysis data are combined with traffic data and tariffs, then it becomes possible to determine the most profitable routes for introduction of new aircraft. Traffic volume, actual figures and forecast, consolidated characteristics of chosen airlines, list of airlines for further studies of efficiency and competitiveness of introduced aircraft are determined.
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Olive, Xavier. "traffic, a toolbox for processing and analysing air traffic data." Journal of Open Source Software 4, no. 39 (July 5, 2019): 1518. http://dx.doi.org/10.21105/joss.01518.

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Asirvadam, Tina Vimala, Sonali Rao S, and Balachander T. "Predicting Air Traffic Density in an Air Traffic Control Sector." ECS Transactions 107, no. 1 (April 24, 2022): 5037–45. http://dx.doi.org/10.1149/10701.5037ecst.

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Air traffic over Indian skies continues to increase rapidly and due to this increased demand, efforts must be made to efficiently balance demand with capacity. Air traffic flow management is a concept that allows for better demand and capacity management. It helps airlines plan routes and schedule flights. The ability to estimate the volume of air traffic is hence a critical element in flow management. Determining traffic in an ATC sector necessitates a thorough examination of various hidden parameters. The prediction is carried out using machine learning algorithms, Recurrent Neural Network, and Long Short Term Memory that examine the data and find a pattern which then anticipates the traffic in the sector at any given moment in the near future. Given the real-time data and experimental findings from this study, it is clear that Long Short Term Memory provides a clearer indicator of the study's goal.
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Duley, Jacqueline A., Scott M. Galster, and Raja Parasuraman. "Information Manager for Determining Data Presentation Preferences in Future Enroute Air Traffic Management." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 1 (October 1998): 47–51. http://dx.doi.org/10.1177/154193129804200112.

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One proposed vision of the future National Airspace System (NAS) involves a change in philosophy from that of air traffic control to one of air traffic management, i.e. Free Flight. In order to accommodate this philosophy change, new technologies will be implemented to assist the air traffic manager (today's air traffic controller) in decision making. When enhancing the system we must also consider the interface between the air traffic manager and this new system and its corresponding new philosophy. To better determine the design of such an interface we must first understand the information needs of the air traffic manager. The present study investigated the information requirements of 58 enroute air traffic controllers. The controllers provided their preferences in presentation frequency as well as the importance of the information to be displayed. The results reveal the potential for adaptive automation as a form of information management.
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Dudás, Gábor, Lajos Boros, Viktor Pál, and Péter Pernyész. "Mapping cost distance using air traffic data." Journal of Maps 12, no. 4 (July 3, 2015): 695–700. http://dx.doi.org/10.1080/17445647.2015.1061463.

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Awan, Faraz Malik, Roberto Minerva, and Noel Crespi. "Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks." Sensors 20, no. 13 (July 4, 2020): 3749. http://dx.doi.org/10.3390/s20133749.

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Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including C O , N O , N O 2 , N O x , and O 3 . We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting.
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Kharchenko, Volodymur, Ivan Ostroumov, Nataliia Kuzmenko, and Arkadiy Larionov. "Airplane positioning using airborne collision avoidance system data." E3S Web of Conferences 164 (2020): 03050. http://dx.doi.org/10.1051/e3sconf/202016403050.

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Mid-air collision is an important problem of modern air transport system. Development of Airborne Collision Avoidance System (ACAS) is grounded on safety criteria in order to support air traffic capacity and equipment. In research, we study the possibility and performance of airplane positioning by data accumulated in ACAS. ACAS surveillance equipment supports accurate range and poor angular measurements of airspace user’s data. An approach is based on a fusion of surveillance data with airplanes locations obtained from decoded Automatic Depended Surveillance-Broadcast messages. Performance of positioning approach is considered in more detail. An error of positioning in the horizontal plane is estimated in relation with uncertainty of airspace users’ location. Numerical demonstration with a live air traffic data indicates poor positioning accuracy in comparison with primary positioning system on-board of airplane and accuracy dependence from geometry and capacity of air traffic.
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Majumdar, Arnab, Washington Y. Ochieng, Gérard McAuley, Jean Michel Lenzi, and Catalin Lepadatu. "The Factors Affecting Airspace Capacity in Europe: A Cross-Sectional Time-Series Analysis Using Simulated Controller Workload Data." Journal of Navigation 57, no. 3 (August 24, 2004): 385–405. http://dx.doi.org/10.1017/s0373463304002863.

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Air traffic in Europe is increasing at a rapid rate and traffic patterns no longer display pronounced daily peaks but instead exhibit peak spreading. Airspace capacity planning can no longer be for the peak period but must consider the whole day. En route airspace capacity in the high density European air traffic network is determined by controller workload. Controller workload is primarily affected by the features of the air traffic and ATC sector. This paper considers the air traffic and ATC sector factors that affect controller workload throughout the whole day. A simulation study using the widely used Reorganized ATC Mathematical Simulator (RAMS) model of air traffic controller workload is conducted for the Central European Air Traffic Services (CEATS) Upper Area Control Centre region of Europe. A cross-sectional time series analysis of the simulation output is conducted with corrections for temporal autocorrelation in the data. The results indicate that a subset of traffic and sector variables and their parameter estimates can be used to predict controller workload in any sector of the CEATS region in any given hour.
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Dekoninck, Luc, and Marcel Severijnen. "Correlating Traffic Data, Spectral Noise and Air Pollution Measurements: Retrospective Analysis of Simultaneous Measurements near a Highway in The Netherlands." Atmosphere 13, no. 5 (May 5, 2022): 740. http://dx.doi.org/10.3390/atmos13050740.

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Road traffic simultaneously emits noise and air pollution. This relation is primarily assessed by comparing A-weighted noise levels (LAeq) and various air pollutants. However, despite the common local traffic source, LAeq and the various sets of air pollution show a lower correlation than expected. Prior work, using simultaneous mobile noise and air pollution measurements, shows that the spectral content of the noise explains the complex and highly nonlinear relation between noise and air pollution significantly better. The spectral content distinguishes between traffic volume and traffic dynamics, two relevant modifiers explaining both the variability in noise and air pollution emissions of the local traffic flow. In May 2011, the environmental agency in the Netherlands performed noise and air pollutant measurements near a major highway and included spectral noise. In the resulting report, the analysis of the traffic, the noise and a wide set of air pollutants only showed a strong correlation between noise and NO. In this work, this dataset is re-evaluated using the noise-related covariates, engine noise and cruising noise, defined in prior work. The modeling approach proves valid for most of the measured air pollutants except for the large PM fractions. Conclusion: the prior established methodology explains the complex interaction between traffic dynamics, noise emission and air pollution emissions for a wide variety of air pollutants. The applicability of the ‘noise-as-a-traffic-proxy’ approach is extended.
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Dissertations / Theses on the topic "Air traffic data"

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Condé, Rocha Murça Mayara. "Data-driven modeling of air traffic flows for advanced Air Traffic Management." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120378.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 209-219).
The Air Traffic Management (ATM) system enables air transportation by ensuring a safe and orderly air traffic flow. As the air transport demand has grown, ATM has become increasingly challenging, resulting in high levels of congestion, flight delays and environmental impacts. To sustain the industry growth foreseen and enable more efficient air travel, it is important to develop mechanisms for better understanding and predicting the air traffic flow behavior and performance in order to assist human decision-makers to deliver improved airspace design and traffic management solutions. This thesis presents a data-driven approach to modeling air traffic flows and analyzes its contribution to supporting system level ATM decision-making. A data analytics framework is proposed for high-fidelity characterization of air traffic flows from large-scale flight tracking data. The framework incorporates a multi-layer clustering analysis to extract spatiotemporal patterns in aircraft movement towards the identification of trajectory patterns and traffic flow patterns. The outcomes and potential impacts of this framework are demonstrated with a detailed characterization of terminal area traffic flows in three representative multi-airport (metroplex) systems of the global air transportation system: New York, Hong Kong and Sao Paulo. As a descriptive tool for systematic analysis of the flow behavior, the framework allows for cross-metroplex comparisons of terminal airspace design, utilization and traffic performance. Novel quantitative metrics are created to summarize metroplex efficiency, capacity and predictability. The results reveal several structural, operational and performance differences between the metroplexes analyzed and highlight varied action areas to improve air traffic operations at these systems. Finally, the knowledge derived from flight trajectory data analytics is leveraged to develop predictive and prescriptive models for metroplex configuration and capacity planning decision support. Supervised learning methods are used to create prediction models capable of translating weather forecasts into probabilistic forecasts of the metroplex traffic flow structure and airport capacity for strategic time horizons. To process these capacity forecasts and assist the design of traffic flow management strategies, a new optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal Airport Acceptance Rates in Ground Delay Programs, an overall delay reduction of up to 9.7% is achieved.
by Mayara Condé Rocha Murça.
Ph. D.
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Rehm, Frank. "Visual data analysis in air traffic management /." Köln : DLR, 2007. http://diglib.uni-magdeburg.de/Dissertationen/2007/frarehm.htm.

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Lin, Joyce C. (Joyce Chaisin) 1979. "VisualFlight : the air traffic control data analysis system." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87266.

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Popescu, Vlad M. "Airspace analysis and design by data aggregation and lean model synthesis." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49126.

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Air traffic demand is growing. New methods of airspace design are required that can enable new designs, do not depend on current operations, and can also support quantifiable performance goals. The main goal of this thesis is to develop methods to model inherent safety and control cost so that these can be included as principal objectives of airspace design, in support of prior work which examines capacity. The first contribution of the thesis is to demonstrate two applications of airspace analysis and design: assessing the inherent safety and control cost of the airspace. Two results are shown, a model which estimates control cost depending on autonomy allocation and traffic volume, and the characterization of inherent safety conditions which prevent unsafe trajectories. The effects of autonomy ratio and traffic volume on control cost emerge from a Monte Carlo simulation of air traffic in an airspace sector. A maximum likelihood estimation identifies the Poisson process to be the best stochastic model for control cost. Recommendations are made to support control-cost-centered airspace design. A novel method to reliably generate collision avoidance advisories, in piloted simulations, by the widely-used Traffic Alert and Collision Avoidance System (TCAS) is used to construct unsafe trajectory clusters. Results show that the inherent safety of routes can be characterized, determined, and predicted by relatively simple convex polyhedra (albeit multi-dimensional and involving spatial and kinematic information). Results also provide direct trade-off relations between spatial and kinematic constraints on route geometries that preserve safety. Accounting for these clusters thus supports safety-centered airspace design. The second contribution of the thesis is a general methodology that generalizes unifying principles from these two demonstrations. The proposed methodology has three steps: aggregate data, synthesize lean model, and guide design. The use of lean models is a result of a natural flowdown from the airspace view to the requirements. The scope of the lean model is situated at a level of granularity that identifies the macroscopic effects of operational changes on the strategic level. The lean model technique maps low-level changes to high-level properties and provides predictive results. The use of lean models allows the mapping of design variables (route geometry, autonomy allocation) to design evaluation metrics (inherent safety, control cost).
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Marzuoli, Aude Claire. "En-route air traffic optimization under nominal and perturbed conditions, on a 3D data-based network flow model." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43639.

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Air Traffic Management (ATM) aims at ensuring safe and efficient movement of aircraft in the airspace. The National Airspace System is currently undergoing a comprehensive overhaul known as NextGen. With the predicted growth of air transportation, providing traffic flow managers with the tools to support decision making is essential. These tools should aid in accommodating the air traffic throughput increase, while limiting controller workload and ensuring high safety levels. In the National Airspace System (NAS), the goal of en-route Traffic Flow Management (TFM) is to balance air traffic demand against available airspace capacity, in order to ensure a safe and expeditious flow of aircraft, both under nominal and perturbed conditions. The objective of this thesis is to develop a better understanding of how to analyze, model and simulate air traffic in a given airspace, under both nominal and degraded conditions. First, a new framework for en-route Traffic Flow Management and Airspace Health Monitoring is developed. It is based on a data-driven approach for air traffic flow modeling using historical data. This large-scale 3D flow network of the Cleveland center airspace provides valuable insight on airspace complexity. A linear formulation for optimizing en-route Air Traffic is proposed. It takes into account a controller taskload model based on flow geometry, in order to estimate airspace capacity. The simulations run demonstrate the importance of sector constraints and traffic demand patterns in estimating the throughput of an airspace. To analyze airspace degradation, weather blockage maps based on vertically integrated liquid (VIL) are incorporated in the model, representing weather perturbations on the same data set used to compute the flows. Comparing the weather blockages and the network model of the airspace provides means of quantifying airspace degradation. Simulations under perturbed conditions are then run according to different objectives. The results of the simulations are compared with the data from these specific days, to identify the advantages and drawbacks of the present model.
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Enea, Gabriele. "Simulation-Based Study to Quantify Data-Communication Benefits in Congested Airport Terminal Area." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/31206.

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The scope of this study was to evaluate the impact of the air traffic controller-to-pilot communication standard known as CPDLC or Data-Communication on the future air traffic operations. The impact was evaluated from the double viewpoint of airport delays and air traffic controllersâ workload. RAMS simulation software is used to perform all the runs and from its output data the values of terminal area delays and controllers workload are obtained. The New York Metroplex terminal area was used as a case study. Because of its complexity, where three major airports (i.e. JFK, Newark, and La Guardia) interact and constraint each other, this area was particularly interesting to be studied and the data analyzed gave a valuable insight on the possible future impact of Data-Communication in congested terminal areas. The results of the study, based on some previous man-in-the-loop simulations performed by the FAA in the nineties, showed that significant potential benefits could be obtained with the complete implementation of such technologies in the workload experienced by air traffic controllers. Moreover some small but not negligible benefits were obtained in the total delays accrued by each airport studied. On the other hand, the simulations of the future demand predicted by the FAA demonstrated that without a significant increment in capacity or limitation on the traffic growth intolerable delays would be recorded across the NAS in the future. For the complexity of the simulation model calibration and for the very time-consuming run time not all the scenarios described in the methodology were tested, demonstrating the weakness of RAMS as a ground simulation model.
Master of Science
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White, Kyle John Sinclair. "Increasing service visibility for future, softwarised air traffic management data networks." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8536/.

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Air Traffic Management (ATM) is at an exciting frontier. The volume of air traffic is reaching the safe limits of current infrastructure. Yet, demand for more air traffic continues. To meet capacity demands, ATM data networks are increasing in complexity with: greater infrastructure integration, higher availability and precision of services; and the introduction of unmanned systems. Official recommendations into previous disruptive outages have high-lighted the need for operators to have richer monitoring capabilities and operational systems visibility, on-demand, in response to challenges. The work presented in this thesis, helps ATM operators better understand and increase visibility into the behaviour of their services and infrastructure, with the primary aim to inform decision-making to reduce service disruption. This is achieved by combining a container-based NFV framework with Software- Defined Networking (SDN). The application of SDN+NFV in this work allows lightweight, chain-able monitoring and anomaly detection functions to be deployed on-demand, and the appropriate (sub)set of network traffic routed through these virtual network functions to provide timely, context-specific information. This container-based function deployment architecture, allows for punctual in-network processing through the instantiation of custom functionality, at appropriate locations. When accidents do occur, such as the crash of a UAV, the lessons learnt should be integrated into future systems. For one such incident, the accident investigation identified a telemetry precursor an hour prior. The function deployment architecture allows operators to extend and adapt their network infrastructure, to incorporate the latest monitoring recommendations. Furthermore, this work has examined relationships in application-level information and network layer data representing individual examples of a wide range of generalisable cases including: between the cyber and physical components of surveillance data, the rate of change in telemetry to determine abnormal aircraft surface movements, and the emerging behaviour of network flooding. Each of these examples provide valuable context-specific benefits to operators and a generalised basis from which further tools can be developed to enhance their understanding of their networks.
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Schiper, Nicole. "Traffic data sampling for air pollution estimation at different urban scales." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSET008/document.

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La circulation routière est une source majeure de pollution atmosphérique dans les zones urbaines. Les décideurs insistent pour qu’on leur propose de nouvelles solutions, y compris de nouvelles stratégies de management qui pourraient directement faire baisser les émissions de polluants. Pour évaluer les performances de ces stratégies, le calcul des émissions de pollution devrait tenir compte de la dynamique spatiale et temporelle du trafic. L’utilisation de capteurs traditionnels sur route (par exemple, capteurs inductifs ou boucles de comptage) pour collecter des données en temps réel est nécessaire mais pas suffisante en raison de leur coût de mise en oeuvre très élevé. Le fait que de telles technologies, pour des raisons pratiques, ne fournissent que des informations locales est un inconvénient. Certaines méthodes devraient ensuite être appliquées pour étendre cette information locale à une grande échelle. Ces méthodes souffrent actuellement des limites suivantes : (i) la relation entre les données manquantes et la précision de l’estimation ne peut être facilement déterminée et (ii) les calculs à grande échelle sont énormément coûteux, principalement lorsque les phénomènes de congestion sont considérés. Compte tenu d’une simulation microscopique du trafic couplée à un modèle d’émission, une approche innovante de ce problème est mise en oeuvre. Elle consiste à appliquer des techniques de sélection statistique qui permettent d’identifier les emplacements les plus pertinents pour estimer les émissions des véhicules du réseau à différentes échelles spatiales et temporelles. Ce travail explore l’utilisation de méthodes statistiques intelligentes et naïves, comme outil pour sélectionner l’information la plus pertinente sur le trafic et les émissions sur un réseau afin de déterminer les valeurs totales à plusieurs échelles. Ce travail met également en évidence quelques précautions à prendre en compte quand on calcul les émissions à large échelle à partir des données trafic et d’un modèle d’émission. L’utilisation des facteurs d’émission COPERT IV à différentes échelles spatio-temporelles induit un biais en fonction des conditions de circulation par rapport à l’échelle d’origine (cycles de conduite). Ce biais observé sur nos simulations a été quantifié en fonction des indicateurs de trafic (vitesse moyenne). Il a également été démontré qu’il avait une double origine : la convexité des fonctions d’émission et la covariance des variables de trafic
Road traffic is a major source of air pollution in urban areas. Policy makers are pushing for different solutions including new traffic management strategies that can directly lower pollutants emissions. To assess the performances of such strategies, the calculation of pollution emission should consider spatial and temporal dynamic of the traffic. The use of traditional on-road sensors (e.g. inductive sensors) for collecting real-time data is necessary but not sufficient because of their expensive cost of implementation. It is also a disadvantage that such technologies, for practical reasons, only provide local information. Some methods should then be applied to expand this local information to large spatial extent. These methods currently suffer from the following limitations: (i) the relationship between missing data and the estimation accuracy, both cannot be easily determined and (ii) the calculations on large area is computationally expensive in particular when time evolution is considered. Given a dynamic traffic simulation coupled with an emission model, a novel approach to this problem is taken by applying selection techniques that can identify the most relevant locations to estimate the network vehicle emissions in various spatial and temporal scales. This work explores the use of different statistical methods both naïve and smart, as tools for selecting the most relevant traffic and emission information on a network to determine the total values at any scale. This work also highlights some cautions when such traffic-emission coupled method is used to quantify emissions due the traffic. Using the COPERT IV emission functions at various spatial-temporal scales induces a bias depending on traffic conditions, in comparison to the original scale (driving cycles). This bias observed in our simulations, has been quantified in function of traffic indicators (mean speed). It also has been demonstrated to have a double origin: the emission functions’ convexity and the traffic variables covariance
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Sangpetchsong, K. "The application of relative navigation to civil air traffic management." Thesis, Cranfield University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341128.

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Kendrick, Christine M. "Improving the Roadside Environment through Integrating Air Quality and Traffic-Related Data." PDXScholar, 2016. http://pdxscholar.library.pdx.edu/open_access_etds/3086.

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Urban arterial corridors are landscapes that give rise to short and long-term exposures to transportation-related pollution. With high traffic volumes, congestion, and a wide mix of road users and land uses at the road edge, urban arterial environments are important targets for improved exposure assessment to traffic-related pollution. Applying transportation management strategies to reduce emissions along arterial corridors could be enhanced if the ability to quantify and evaluate such actions was improved. However, arterial roadsides are under-sampled in terms of air pollution measurements in the United States and using observational data to assess such effects has many challenges such as lack of control sites for comparisons and temporal autocorrelation. The availability of traffic-related data is also typically limited in air monitoring and health studies. The work presented here uses unique long-term roadside air quality monitoring collected at the intersection of an urban arterial in Portland, OR to characterize the roadside atmospheric environment. This air quality dataset is then integrated with traffic-related data to assess various methods for improving exposure assessment and the roadside environment. Roadside nitric oxide (NO), nitrogen dioxide (NO2), and particle number concentration (PNC) measurements all demonstrated a relationship with local traffic volumes. Seasonal and diurnal characterizations show that roadside PM2.5 (mass) measurements do not have a relationship with local traffic volumes, providing evidence that PM2.5 mass is more tied to regional sources and meteorological conditions. The relationship of roadside NO and NO2 with traffic volumes was assessed over short and long-term aggregations to assess the reliability of a commonly employed method of using traffic volumes as a proxy for traffic-related exposure. This method was shown to be insufficient for shorter-time scales. Comparisons with annual aggregations validate the use of traffic volumes to estimate annual exposure concentrations, demonstrating this method can capture chronic but not acute exposure. As epidemiology and exposure assessment aims to target health impacts and pollutant levels encountered by pedestrians, cyclists, and those waiting for transit, these results show when traffic volumes alone can be a reliable proxy for exposure and when this approach is not warranted. Next, it is demonstrated that a change in traffic flow and change in emissions can be measured through roadside pollutant concentrations suggesting roadside pollution can be affected by traffic signal timing. The effect of a reduced maximum traffic signal cycle length on measurements of degree of saturation (DS), NO, and NO2 were evaluated for the peak traffic periods in two case studies at the study intersection. In order to reduce bias from covariates and assess the effect due to the change in cycle length only, a matched sampling method based on propensity scores was used to compare treatment periods (reduced cycle length) with control periods (no change in cycle length). Significant increases in DS values of 2-8% were found along with significant increases of 5-8ppb NO and 4-5ppb NO2 across three peak periods in both case studies. Without matched sampling to address the challenges of observational data, the small DS and NOx changes for the study intersection would have been masked and matched sampling is shown to be a helpful tool for future urban air quality empirical investigations. Dispersion modeling evaluations showed the California Line Source Dispersion Model with Queuing and Hotspot Calculations (CAL3QHCR), an approved regulatory model to assess the impacts of transportation projects on PM2.5, performed both poor and well when predictions were compared with PM2.5 observations depending on season. Varying levels of detail in emissions, traffic signal, and traffic volume data for model inputs, assessed using three model scenarios, did not affect model performance for the study intersection. Model performance is heavily dependent on background concentrations and meteorology. It was also demonstrated that CAL3QHC can be used in combination with roadside PNC measurements to back calculate PNC emission factors for a mixed fleet and major arterial roadway in the U.S. The integration of roadside air quality and traffic-related data made it possible to perform unique empirical evaluations of exposure assessment methods and dispersion modeling methods for roadside environments. This data integration was used for the assessment of the relationship between roadside pollutants and a change in a traffic signal setting, a commonly employed method for transportation management and emissions mitigation, but rarely evaluated outside of simulation and emissions modeling. Results and methods derived from this work are being used to implement a second roadside air quality station, to design a city-wide integrated network of air quality, meteorological, and traffic data including additional spatially resolved measurements with feedback loops for improved data quality and data usefulness. Results and methods are also being used to design future evaluations of transportation projects such as freight priority signaling, improved transit signal priority, and to understand the air quality impacts of changes in fleet composition such as an increase in electric vehicles.
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Books on the topic "Air traffic data"

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Flight deck benefits of integrated data link communication. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1992.

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United States. Air Traffic Operations Service. Data communications. [Washington, D.C.?]: U.S. Dept. of Transportation, Federal Aviation Administration, 1989.

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Crawford, Jason A. Modal emissions modeling with real traffic data. College Station, Tex: Texas Transportation Institute, Texas A&M University System, 1999.

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Center, Ames Research, ed. Design and evaluation of an advanced air-ground data-link system for air traffic control. Moffett Field, Calif: NASA, Ames Research Center, 1992.

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Center, Ames Research, ed. Design and evaluation of an advanced air-ground data-link system for air traffic control. Moffett Field, Calif: NASA, Ames Research Center, 1992.

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Phillips, Charles T. Integration of air traffic databases: A case study. Washington, DC: Operations Research Service, Federal Aviation Administration, 1995.

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United States. Air Traffic Service. Data communications. [Washington, D.C.?]: U.S. Dept. of Transportation, Federal Aviation Administration, Air Traffic Service, 1986.

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Knox, Charles E. Flight tests with a data link used for air traffic control information exchange. Hampton, Va: Langley Research Center, 1991.

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Alan, Yost, United States. Federal Aviation Administration. Office of Aviation Research., and John A. Volpe National Transportation Systems Center (U.S.), eds. Controller and pilot error in airport operations: A review of previous research and analysis of safety data. Washington, DC: Federal Aviation Administration, Office of Aviation Research, 2001.

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Cardosi, Kim M. Controller and pilot error in airport operations: A review of previous research and analysis of safety data. Washington, DC: Federal Aviation Administration, Office of Aviation Research, 2001.

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Book chapters on the topic "Air traffic data"

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Zanin, Massimiliano, Andrew Cook, and Seddik Belkoura. "Data Science." In Complexity Science in Air Traffic Management, 105–29. Burlington, VT : Ashgate, [2016] |: Routledge, 2016. http://dx.doi.org/10.4324/9781315573205-7.

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Zakaria, Zainuddin, and Sun Woh Lye. "Unearthing Air Traffic Control Officer Strategies from Simulated Air Traffic Data." In Human Interaction, Emerging Technologies and Future Systems V, 364–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85540-6_46.

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Temme, Annette, Ingrid Gerdes, and Roland Winkler. "Computational Intelligence in Air Traffic Management." In Computational Intelligence in Intelligent Data Analysis, 285–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32378-2_20.

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Besada, Juan A., Guillermo Frontera, Ana M. Bernardos, and Gonzalo de Miguel. "Adaptive Data Fusion for Air Traffic Control Surveillance." In Lecture Notes in Computer Science, 118–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21222-2_15.

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Rehm, Frank, Frank Klawonn, and Rudolf Kruse. "Single Cluster Visualization to Optimize Air Traffic Management." In Studies in Classification, Data Analysis, and Knowledge Organization, 319–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-70981-7_36.

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Ziv, Alexander, Ruwim Berkowicz, Eugene Genikhovich, Finn Palmgren, and Ekaterina Yakovleva. "Analysis of the St. Petersburg Traffic Data Using the OSPM Model." In Urban Air Quality — Recent Advances, 297–310. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0312-4_21.

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Lane, S. E. J. "The Implementation and Impact of Automatic Data Processing on UK Military ATC Operations." In Automation and Systems Issues in Air Traffic Control, 47–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76556-8_5.

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Nguyen, Binh Thanh, Pham Lu Quang Minh, Huynh Vu Minh Nguyet, Do Huu Phuoc, Pham Dinh Tai, and Huy Truong Dinh. "Intelligent Urban Transportation System to Control Road Traffic with Air Pollution Orientation." In Future Data and Security Engineering, 211–21. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91387-8_14.

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Švec, Jan, and Luboš Šmídl. "Semantic Entity Detection in the Spoken Air Traffic Control Data." In Speech and Computer, 394–401. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11581-8_49.

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Shi, Feng, Peng Cheng, Rui Geng, and Mo Yang. "An Air Traffic Flow Analysis System Using Historical Radar Data." In Recent Advances in Computer Science and Information Engineering, 541–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25766-7_72.

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Conference papers on the topic "Air traffic data"

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Muszka, Tibor, and Peter Szabó. "Copernicus Satellite Data and Air Traffic Management." In 2024 New Trends in Aviation Development (NTAD), 102–5. IEEE, 2024. https://doi.org/10.1109/ntad63796.2024.10850205.

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zhou, zhihui, Haiyan Chen, Yirui Fu, and Ligang Yuan. "Air traffic complexity assessment based on multiscale spatio-temporal data." In Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024), edited by Hao Chen and Wei Shangguan, 2. SPIE, 2025. https://doi.org/10.1117/12.3050626.

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S, Sivamurugan, Vedapriya N, Vijayashree K, and Nithya P. "Road Traffic Forecasting Using Air Pollution and Atmospheric Data." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 1–5. IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780058.

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Momtaz, Soufiane, Otmane Idrissi, Abdelmajid Bousselham, and Mohammed Mestari. "Comparing Lateral and Vertical Spacing Action Applied To Arrival Air Traffic." In 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icds62089.2024.10756306.

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Chen, Xiaoguang, Hao Li, Yumeng Zhou, and Meng Li. "Discussion on Data Classification Technology for Data Security in Air Traffic Management Information Systems." In 2024 2nd International Conference on Intelligent Communication and Networking (ICN), 139–43. IEEE, 2024. https://doi.org/10.1109/icn64251.2024.10865942.

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Avagyan, Garik, Juan C. Armijos, Carson K. Leung, Jasmine J. Tabuzo, Aivee F. Teodocio, and Alfredo Cuzzocrea. "An Environmental Data Science Solution for Data Analytics Exploration of Traffic Interaction on Air Quality." In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), 1800–1805. IEEE, 2024. http://dx.doi.org/10.1109/compsac61105.2024.00284.

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Yu, Shasha, Yijun Chen, and Xuejun Zhang. "Insight into complex mega-system: Examining the Evolution of Air Traffic Research." In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), 1225–34. IEEE, 2024. https://doi.org/10.1109/iciba62489.2024.10869283.

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Khamlae, Ponlawat, Chollakorn Nimpattanavong, Worawat Choensawat, and Kingkarn Sookhanaphibarn. "Visualization System for Air Traffic data." In 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE). IEEE, 2020. http://dx.doi.org/10.1109/gcce50665.2020.9291971.

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Rehm, Frank, Frank Klawonn, Georg Russ, and Rudolf Kruse. "Modern Data Visualization for Air Traffic Management." In NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2007. http://dx.doi.org/10.1109/nafips.2007.383804.

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Comitz, Paul, and Avinash Pinto. "A Software Factory for Air Traffic Data." In 2006 ieee/aiaa 25TH Digital Avionics Systems Conference. IEEE, 2006. http://dx.doi.org/10.1109/dasc.2006.313758.

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Reports on the topic "Air traffic data"

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Kendrick, Christine. Improving the Roadside Environment through Integrating Air Quality and Traffic-Related Data. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.3081.

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Kwon, Jaymin, Yushin Ahn, and Steve Chung. Spatio-Temporal Analysis of the Roadside Transportation Related Air Quality (STARTRAQ) and Neighborhood Characterization. Mineta Transportation Institute, August 2021. http://dx.doi.org/10.31979/mti.2021.2010.

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To promote active transportation modes (such as bike ride and walking), and to create safer communities for easier access to transit, it is essential to provide consolidated data-driven transportation information to the public. The relevant and timely information from data facilitates the improvement of decision-making processes for the establishment of public policy and urban planning for sustainable growth, and for promoting public health in the region. For the characterization of the spatial variation of transportation-emitted air pollution in the Fresno/Clovis neighborhood in California, various species of particulate matters emitted from traffic sources were measured using real-time monitors and GPS loggers at over 100 neighborhood walking routes within 58 census tracts from the previous research, Children’s Health to Air Pollution Study - San Joaquin Valley (CHAPS-SJV). Roadside air pollution data show that PM2.5, black carbon, and PAHs were significantly elevated in the neighborhood walking air samples compared to indoor air or the ambient monitoring station in the Central Fresno area due to the immediate source proximity. The simultaneous parallel measurements in two neighborhoods which are distinctively different areas (High diesel High poverty vs. Low diesel Low poverty) showed that the higher pollution levels were observed when more frequent vehicular activities were occurring around the neighborhoods. Elevated PM2.5 concentrations near the roadways were evident with a high volume of traffic and in regions with more unpaved areas. Neighborhood walking air samples were influenced by immediate roadway traffic conditions, such as encounters with diesel trucks, approaching in close proximity to freeways and/or busy roadways, passing cigarette smokers, and gardening activity. The elevated black carbon concentrations occur near the highway corridors and regions with high diesel traffic and high industry. This project provides consolidated data-driven transportation information to the public including: 1. Transportation-related particle pollution data 2. Spatial analyses of geocoded vehicle emissions 3. Neighborhood characterization for the built environment such as cities, buildings, roads, parks, walkways, etc.
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Edwards, Lulu, Charles Weiss, J. Newman, Fred Nichols, L. Coffing, and Quint Mason. Corrosion and performance of dust palliatives : laboratory and field studies. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42125.

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This report details laboratory and field experiments on BioPreferred® dust suppressants to assess performance and corrosion characteristics. Numerous bio-based dust suppressant products are marketed, but little data are available to assess performance for dust abatement and corrosion of common metals. A laboratory study used an air impingement device and the Portable In-Situ Wind ERosion Laboratory (PI-SWERL) to simulate wind speeds similar to those in field conditions for rotary wing aircraft. Laboratory corrosion studies used metal coupons imbedded in soil treated with dust palliative. Field trials were conducted using ground vehicle traffic to minimize cost and lower safety concerns while increasing surface wear from repetitive traffic. These studies clearly show that bio-based products demonstrate low corrosion potential with similar dust abatement performance to synthetic-based agents.
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Pinchuk, O. P., O. M. Sokolyuk, O. Yu Burov, Evgeniy Lavrov, Svitlana Shevchenko, and Valeriia Aksakovska. ICT for training and evaluation of the solar impact on aviation safety. CEUR Workshop Proceedings, 2020. http://dx.doi.org/10.33407/lib.naes.722580.

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The paper discusses information and communication technology use for studying reasons of aviation accidents because of the aviation operator errors as result of internal and external influence. The model and technique are proposed and include integrated ICT united previously developed (initial professional selection and day-to-day pre-shift check), open access cloud-based (NASA and ICAO) and real-time operative (air traffic controllers and pilots control) ICTs, which data are stored in one database. Proposed ICT has been checked to study effect of the solar wind parameters (speed and density) on appearance of aviation incidents and accidents during one year observation. Results of that study were compared with corresponding results of another period of solar activity, as well as with data obtained in laboratory conditions to study cognitive tests performance under effect of the solar wind.
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Hartle, Jennifer C., Ossama (Sam) A. Elrahman, Cara Wang, Daniel A. Rodriguez, Yue Ding, and Matt McGahan. Assessing Public Health Benefits of Replacing Freight Trucks with Cargo Cycles in Last Leg Delivery Trips in Urban Centers. Mineta Transportation Institute, June 2022. http://dx.doi.org/10.31979/mti.2022.1952.

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Increased urbanization, population growth, and demand for time-sensitive deliveries means increased freight movement in cities, which contributes to emissions, noise, and safety concerns. One innovative mode gaining widespread attention for urban deliveries is cargo cycles—bicycles adapted for freight delivery. Despite the recognized potential and possible success of transporting at least 25% of freight via cycle, research remains limited. This research investigates the potential of cargo cycle delivery for last mile freight in Oakland, California, with a focus on the West Oakland neighborhood. The data collection included interviews, focus groups, vehicle field observation and counts, and traffic simulation modeling. The traffic simulation examined scenarios where businesses converted different percentages of current deliveries to cargo cycles using a transfer hub as the starting point for their cargo cycle delivery. The best-case scenario—where the maximum percentage of deliveries were made with cargo cycle instead of motorized vehicles—resulted in reductions of 2600 vehicle miles traveled (VMT) per day. In that case scenario, the vehicle miles traveled (VMT) reduction is equivalent to a reduction in emissions of PM2.5, PM10, NOx, and reactive organic gas (ROG) of taking about 1000 Class 4 box trucks off the roads of West Oakland per day. In the worst-case scenario, with a significantly smaller percentage of motorized package deliveries converted to cargo cycles, there is a reduction of 160 VMT, equivalent to the removal of approximately 80 Class 4 box trucks off the roads of West Oakland per day. This potential reduction in air pollution and traffic congestion, as well as job creation, would benefit West Oakland residents.
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Bobashev, Georgiy, R. Joey Morris, Elizabeth Costenbader, and Kyle Vincent. Assessing network structure with practical sampling methods. RTI Press, May 2018. http://dx.doi.org/10.3768/rtipress.2018.op.0049.1805.

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Using data from an enumerated network of worldwide flight connections between airports, we examine how sampling designs and sample size influence network metrics. Specifically, we apply three types of sampling designs: simple random sampling, nonrandom strategic sampling (i.e., selection of the largest airports), and a variation of snowball sampling. For the latter sampling method, we design what we refer to as a controlled snowball sampling design, which selects nodes in a manner analogous to a respondent-driven sampling design. For each design, we evaluate five commonly used measures of network structure and examine the percentage of total air traffic accounted for by each design. The empirical application shows that (1) the random and controlled snowball sampling designs give rise to more efficient estimates of the true underlying structure, and (2) the strategic sampling method can account for a greater proportion of the total number of passenger movements occurring in the network.
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Bain, Rachel, David Young, Marin Kress, Katherine Chambers, and Brandan Scully. US port connectivity and ramifications for maintenance of South Atlantic Division ports. Engineer Research and Development Center (U.S.), January 2023. http://dx.doi.org/10.21079/11681/46385.

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This study utilized automatic identification system (AIS) data to quantify vessel traffic patterns within a predominantly US port network from 1 January 2009 to 31 December 2020, with the methods validated using independent data sets collected between 1 January 2015 and 31 December 2019. The analysis focused on South Atlantic Division (SAD) ports. AIS-derived data characterized individual ports’ traffic and port-to-port connectivity for the network. With foreign vessel entrances and clearances (E&C) data, the AIS-reported vessel characteristics enabled calculation of ships’ physical volume, which was a reasonable proxy for tonnage at many SAD ports. The PageRank algorithm was then applied to port-to-port traffic, revealing how individual ports participate in cargo movement through the network. PageRank scores also provided insight into the maritime supply chain beyond traditional traffic metrics. For example, many East Coast SAD ports ranked higher by PageRank than by raw tonnage. Because of the supply chain implications of shared vessel traffic, PageRank scores can augment tonnage metrics when prioritizing channel and infrastructure maintenance. Vessel volume, port-to-port connectivity, and PageRank scores reveal maritime supply chain resilience by identifying alternative destinations for cargo bound for disrupted ports, robustness across supply chains, and the effects of seasonality and disruptions.
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Kress, Marin, David Young, Katherine Chambers, and Brandan Scully. Measuring maritime connectivity to Puerto Rico and the Virgin Islands using Automatic Identification System (AIS) data. Engineer Research and Development Center (U.S.), August 2023. http://dx.doi.org/10.21079/11681/47495.

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The purpose of this Coastal and Hydraulics Engineering technical note (CHETN) is to summarize a portion of recently published work (Young, Kress, et al. 2022) that used archival Automatic Identification System (AIS) data to measure the commercial vessel traffic connected to Puerto Rican and US Virgin Island (USVI) port areas from January 2015 to June 2020. Vessel movement derived from AIS was aggregated to construct a network that measured the port-to-port connectivity for all ports in the network and the interconnectivity of traffic between those ports. AIS data provided a description of vessel movement and the identification of specific vessel classes. Metrics such as interconnectedness can be used in conjunction with standard US Army Corps of Engineers (USACE) metrics describing waterway utilization, which traditionally have included total tonnage and specific commodity tonnage. The ability to consider the self-selected vessel-type broadcast via AIS, as well as dominant commodity type and tonnage reported through statistical publications, provides a fuller and more accurate description of waterway capacity utilization. This knowledge, along with port-to-port interconnectedness, reveals potential redundancies between ports, robustness across supply chains, and the impacts of seasonality, thereby allowing the USACE to expand its understanding of maritime supply-chain resilience.
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Olstad, Tyra, Brian Peterson, J. M. Hutchinson, J. Beeco, and Damon Joyce. Exploring spatial patterns of overflights at Bryce Canyon National Park. National Park Service, 2024. http://dx.doi.org/10.36967/2304315.

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This study explored spatial patterns of overflights at Bryce Canyon National Park (BRCA). Overflights were analyzed from August 14, 2023 to March 12, 2024 using Automatic Dependent Surveillance-Broadcast (ADS-B) data. Phase 1 of analysis focused on all overflights and found airspace use was predominantly by commuter traffic along major flight paths. Phase 2 of analysis focused on low-level overflights that fly below 11,500 ft mean sea level (MSL) and fly within 10-miles of the BRCA boundary. It found that most flights occurred between 9,500?10,500 ft MSL. Flights below 8,500 ft MSL concentrated around the Bryce Canyon Airport just outside the park and the main scenic amphitheater within the park. Phase 3 of analysis removed all overflights that were civil patrol flights, major airlines, and survey flights. The remaining flights were low-level overflights, including air tours. Kernel density analysis revealed that these flights were most concentrated in the northwest corner of the park at an altitude interval of was 501?1,000 ft above ground level (AGL). The greatest percentage of flights were between 1,500?2,000 ft AGL, clustered over the main amphitheater and Fairyland / Tropic Ditch. Midday was the most popular time of day for overflights. Two thirds of the flights were by fixed-wing single engine aircraft, with relatively few rotorcraft. This information can be used for planning and management purposes, as well as a resource and baseline for future research.
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Sakhare, Rahul Suryakant, Jairaj Desai, Wyatt Woker, Howell Li, Jijo K. Mathew, Justin Mahlberg, Enrique D. Saldivar-Carranza, Deborah Horton, and Darcy M. Bullock. Connected Vehicle-Centric Dashboards for TMC of the Future. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317642.

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The adoption of dashboards and tools into Traffic Management Centers (TMC) has been growing with advancements in connected vehicle (CV) data. These tools are now being utilized—not only for analyzing work zones, severe crashes, winter operations, and traffic signals—but also to provide measures for characterizing overall system mobility, resiliency, and after-action assessments. Previous studies have extended the concepts to include the enhanced trajectory-based CV data into dashboards that aid agencies in assessing and managing roadways. This study presents the extension of these tools that further improve the value and insights provided. It also highlights the evolution of CV data in Indiana. CV data in Indiana has grown to over 364 billion statewide records. Average overall penetration rate of CV data on interstates has increased to 6.32% in May 2022 with trucks accounting for 1.7%. Sections of this study also present the impact of rain intensity on interstate traffic and incorporation of such weather data into heatmap and other tools. Updates to existing dashboards and a summary of newly developed dashboards are synopsized in this report. Finally, this report presents a case study that highlights the use of these tools to assess and analyze the impact of tornadoes on interstate traffic in Indiana. As interest in these tools has grown, this project facilitated continued improvements and added features to meet the needs of INDOT and their partners.
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