Modeling Arrival Flight Times within the Terminal Airspace

2021 ◽  
pp. 036119812110114
Author(s):  
Osama Alsalous ◽  
Susan Hotle
Keyword(s):  
United States ◽  
Travel Time ◽  
The United States ◽  
Time Of Day ◽  
Civil Aviation ◽  
Weather Data ◽  
Travel Times ◽  
International Airport ◽  
Area Of Interest ◽  
The Impact

Air traffic management efficiency in the descent phase of flights is a key area of interest in aviation research for the United States, Europe, and recently other parts of the world. The efficiency of arrival travel times within the terminal airspace is one of nineteen key performance indicators defined by the Federal Aviation Administration (FAA) and the International Civil Aviation Organization, typically within 100 nmi of arrival airports. This study models the relationship between travel time within the terminal airspace and contributing factors using a multivariate log-linear model to quantify the impact that these factors have on the total travel time within the last 100 nmi. The results were compared with the baseline set of variables that are currently used for benchmarking at the FAA. The analyzed data included flight and weather data from January 1, 2018 to March 31, 2018 for five airports in the United States: Chicago O’Hare International Airport, Hartsfield-Jackson Atlanta International, San Francisco International Airport, John F. Kennedy International Airport, and LaGuardia Airport. The modeling results showed that there is a significant improvement in prediction accuracy of travel times compared with the baseline methodology when additional factors, such as wind, meteorological conditions, demand and capacity, ground delay programs, market distance, time of day, and day of week, are included. Root mean squared error values from out-of-sample testing were used to measure the accuracy of the estimated models.

scholarly journals Examining the Impact of Adverse Weather on Travel Time Reliability of Urban Corridors in Shanghai

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yajie Zou ◽  
Ting Zhu ◽  
Yifan Xie ◽  
Linbo Li ◽  
Ying Chen
Keyword(s):  
Travel Time ◽  
Weather Condition ◽  
Time Of Day ◽  
Weather Data ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Time Data ◽  
Adverse Weather Condition ◽  
Adverse Weather ◽  
The Impact

Travel time reliability (TTR) is widely used to evaluate transportation system performance. Adverse weather condition is an important factor for affecting TTR, which can cause traffic congestions and crashes. Considering the traffic characteristics under different traffic conditions, it is necessary to explore the impact of adverse weather on TTR under different conditions. This study conducted an empirical travel time analysis using traffic data and weather data collected on Yanan corridor in Shanghai. The travel time distributions were analysed under different roadway types, weather, and time of day. Four typical scenarios (i.e., peak hours and off-peak hours on elevated expressway, peak hours and off-peak hours on arterial road) were considered in the TTR analysis. Four measures were calculated to evaluate the impact of adverse weather on TTR. The results indicated that the lognormal distribution is preferred for describing the travel time data. Compared with off-peak hours, the impact of adverse weather is more significant for peak hours. The travel time variability, buffer time index, misery index, and frequency of congestion increased by an average of 29%, 19%, 22%, and 63%, respectively, under the adverse weather condition. The findings in this study are useful for transportation management agencies to design traffic control strategies when adverse weather occurs.

scholarly journals Statistical Weather-Impact Models: An Application of Neural Networks and Mixed Effects for Corn Production over the United States

2016 ◽  
Vol 55 (11) ◽  
pp. 2509-2527 ◽  
Author(s):  
Jordane A. Mathieu ◽  
Filipe Aires
Keyword(s):  
United States ◽  
The United States ◽  
Weather Data ◽  
Corn Yield ◽  
Mixed Effect ◽  
Local Conditions ◽  
Weather Impact ◽  
Yield Forecasting ◽  
The Impact ◽  
Impact Models

AbstractStatistical meteorological impact models are intended to represent the impact of weather on socioeconomic activities, using a statistical approach. The calibration of such models is difficult because relationships are complex and historical records are limited. Often, such models succeed in reproducing past data but perform poorly on unseen new data (a problem known as overfitting). This difficulty emphasizes the need for regularization techniques and reliable assessment of the model quality. This study illustrates, in a general way, how to extract pertinent information from weather data and exploit it in impact models that are designed to help decision-making. For a given socioeconomic activity, this type of impact model can be used to 1) study its sensitivity to weather anomalies (e.g., corn sensitivity to water stress), 2) perform seasonal forecasting (yield forecasting) for it, and 3) quantify the longer-term (several decades) impact of weather on it. The size of the training database can be increased by pooling data from various locations, but this requires statistical models that are able to use the localization information—for example, mixed-effect (ME) models. Linear, neural-network, and ME models are compared, using a real-world application: corn-yield forecasting over the United States. Many challenges faced in this paper may be encountered in many weather-impact analyses: these results show that much care is required when using space–time data because they are often highly spatially correlated. In addition, the forecast quality is strongly influenced by the training spatial scale. For the application that is described herein, learning at the state scale is a good trade-off: it is specific to local conditions while keeping enough data for the calibration.

On the synthesis of shear-coupled PL waves

1968 ◽  
Vol 58 (6) ◽  
pp. 1849-1877 ◽  
Author(s):  
Ramesh Chander ◽  
L. E. Alsop ◽  
Jack Oliver
Keyword(s):  
United States ◽  
Phase Velocity ◽  
Travel Time ◽  
Current Knowledge ◽  
Shear Waves ◽  
The United States ◽  
Synthetic Seismograms ◽  
Travel Times ◽  
Precise Knowledge ◽  
S Curve

ABSTRACT Using the shear-coupled PL wave hypothesis of Oliver as a basis, a method is developed for computing synthetic long-period seismograms between the onset of the initial S-type body phase and the beginning of surface waves. Comparison of observed and synthetic siesmograms shows that this hypothesis can explain, in considerable detail, most of the waves with periods greater than about 20 sec recorded during this interval. The synthetic seismograms are computed easily on a small digital computer; they resemble the observed seismograms much more closely than the synthetic seismograms obtained through the superposition of normal modes of the Earth that have been reported in the literature. The synthesis of shear-coupled PL waves depends on a precise knowledge of the phase-velocity curve of the PL wave and travel-time curves of shear waves. Hence, in principle, if one of these quantities is well-known the other can be determined by this method. Phase-velocity curves of the PL wave are determined for the Baltic shield, the Russian platform, the Canadian shield, the United States, and the western North-Atlantic ocean, on the assumption that J-B travel-time curves of shear waves apply to these areas. These dispersion curves show the type of variations to be expected on the basis of the current knowledge of the crustal structures in these areas. Examples are presented to show that J-B travel-times of shear waves along paths between Kenai Peninsula, Alaska and Palisades, equatorial mid-Atlantic ridge and Palisades, and Kurile Islands and Uppsala need to be revised. Shear-wave travel-time curves that are not unique for reasons explained in the study but that give synthetic seismograms in agreement with the observed seismograms were obtained. The new S curves are compared with the J-B travel-time curves for S; and they all predict S waves to arrive later than the time given by J-B tables for epicentral distances smaller than about 30°. The new S curve for the Alaska to Palisades path appears to agree with one of the branches of a multi-branched S curve proposed recently by Ibrahim and Nuttli for the ‘average United States’ insofar as travel-times are concerned, but there are some differences in the slopes of the two curves.

Interstate Speed Profiles

2018 ◽  
Vol 2672 (42) ◽  
pp. 57-70 ◽  
Author(s):  
Wenjing Pu
Keyword(s):  
United States ◽  
Performance Management ◽  
Travel Time ◽  
The United States ◽  
Time Of Day ◽  
National Average ◽  
Time Data ◽  
Data Set ◽  
Average Speed ◽  
High Level

This paper draws the first set of high-level, national speed profiles for the entire Dwight D. Eisenhower National System of Interstate and Defense Highways (Interstate system) in the United States based on the 2016 year-long National Performance Management Research Data Set (NPMRDS) and a conflated NPMRDS-HPMS (Highway Performance Monitoring System) geospatial network. This set of quantitative profiles include: ( a) national average speeds of 2016, ( b) national average speed time of day variations, ( c) national average speed day of week variations, ( d) national average speed seasonal variations, and ( e) state average speed and travel time distributions in peak hours. This work demonstrates that the integration of the private sector’s emerging big travel-time data and the public sector’s HPMS has provided a powerful resource to monitor travel-time-related performance of the nation’s highways. As the United States is transforming the Federal-aid Highway Program into a performance-based program with enhanced accountability and transparency, this integrated resource will help states and metropolitan planning organizations (MPOs) to monitor their performance and progress towards achieving targets, and enable the Federal Highway Administration (FHWA) not only to draw high-level national highway performance profiles but also to pinpoint the exact where, when, and how much the challenges are.

scholarly journals Beyond the dichotomy: How ride-hailing competes with and complements public transport

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262496
Author(s):  
Oded Cats ◽  
Rafal Kucharski ◽  
Santosh Rao Danda ◽  
Menno Yap
Keyword(s):  
United States ◽  
Travel Time ◽  
Public Transport ◽  
The United States ◽  
Travel Times ◽  
Service Accessibility ◽  
Transport Services ◽  
Us Cities ◽  
Public Transport Services ◽  
Two Alternatives

Since ride-hailing has become an important travel alternative in many cities worldwide, a fervent debate is underway on whether it competes with or complements public transport services. We use Uber trip data in six cities in the United States and Europe to identify the most attractive public transport alternative for each ride. We then address the following questions: (i) How does ride-hailing travel time and cost compare to the fastest public transport alternative? (ii) What proportion of ride-hailing trips do not have a viable public transport alternative? (iii) How does ride-hailing change overall service accessibility? (iv) What is the relation between demand share and relative competition between the two alternatives? Our findings suggest that the dichotomy—competing with or complementing—is false. Though the vast majority of ride-hailing trips have a viable public transport alternative, between 20% and 40% of them have no viable public transport alternative. The increased service accessibility attributed to the inclusion of ride-hailing is greater in our US cities than in their European counterparts. Demand split is directly related to the relative competitiveness of travel times i.e. when public transport travel times are competitive ride-hailing demand share is low and vice-versa.

The impact of military research and development priorities on the evolution of the civil economy in capitalist states

1990 ◽  
Vol 16 (4) ◽  
pp. 321-339 ◽  
Author(s):  
Barry Buzan ◽  
Gautam Sen
Keyword(s):  
United States ◽  
Research And Development ◽  
Nuclear Power ◽  
Technological Development ◽  
Significant Proportion ◽  
The United States ◽  
Civil Aviation ◽  
Military Research ◽  
Civil Economy ◽  
The Impact

The objective of this paper is to identify the process by which military research and development (R&D) priorities affect the evolution of major sectors of the civil economy in capitalist states. Military priorities channel a significant proportion of the resources that capitalist societies devote to R&D: for the United States in the period 1982–4, military R&D amounted to 28.9 per cent of gross domestic expenditure on R&D. The nature of military priorities favours some areas of technological development over others, and when these favoured areas are opened up for military purposes, it is often possible to build a major civil industry on the resultant technology. Examples of this process include nuclear power, civil aviation, space satellites and computers. Some, though by no means all, of the commanding heights of civil economies are thus powerfully shaped by the opportunities created by specifically military R&D.

scholarly journals Increased travel times to United States SARS-CoV-2 testing sites: a spatial modeling study

2020 ◽  
Author(s):  
Benjamin Rader ◽  
Christina M. Astley ◽  
Karla Therese L. Sy ◽  
Kara Sewalk ◽  
Yulin Hswen ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Travel Time ◽  
Demographic Data ◽  
The United States ◽  
List Type ◽  
Median Income ◽  
Travel Times ◽  
Cross Sectional ◽  
Geographic Accessibility

AbstractImportanceAccess to testing is key to a successful response to the COVID-19 pandemic.ObjectiveTo determine the geographic accessibility to SARS-CoV-2 testing sites in the United States, as quantified by travel time.DesignCross-sectional analysis of SARS-CoV-2 testing sites as of April 7, 2020 in relation to travel time.SettingUnited States COVID-19 pandemic.ParticipantsThe United States, including the 48 contiguous states and the District of Columbia.ExposuresPopulation density, percent minority, percent uninsured, and median income by county from the 2018 American Community Survey demographic data.Main OutcomeSARS-CoV-2 testing sites identified in two national databases (Carbon Health and CodersAgainstCovid), geocoded by address. Median county 1 km2 gridded friction surface of travel times, as a measure of geographic accessibility to SARS-CoV-2 testing sites.Results6,236 unique SARS-CoV-2 testing sites in 3,108 United States counties were identified. Thirty percent of the U.S. population live in a county (N = 1,920) with a median travel time over 20 minutes. This was geographically heterogeneous; 86% of the Mountain division population versus 5% of the Middle Atlantic population lived in counties with median travel times over 20 min. Generalized Linear Models showed population density, percent minority, percent uninsured and median income were predictors of median travel time to testing sites. For example, higher percent uninsured was associated with longer travel time (β = 0.41 min/percent, 95% confidence interval 0.3-0.53, p = 1.2×10−12), adjusting for population density.Conclusions and RelevanceGeographic accessibility to SARS-Cov-2 testing sites is reduced in counties with lower population density and higher percent of minority and uninsured, which are also risk factors for worse healthcare access and outcomes. Geographic barriers to SARS-Cov-2 testing may exacerbate health inequalities and bias county-specific transmission estimates. Geographic accessibility should be considered when planning the location of future testing sites and interpreting epidemiological data.Key PointsSARS-CoV-2 testing sites are distributed unevenly in the US geography and population.Median county-level travel time to SARS-CoV-2 testing sites is longer in less densely populated areas, and in areas with a higher percentage of minority or uninsured populations.Improved geographic accessibility to testing sites is imperative to manage the COVID-19 pandemic in the United States.

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