Heathrow Airport Research Articles

A Flow-Speed Model for Motorways in England: Analysis Under Various Weather Conditions

This work proposes a single regime speed–flow model to fit the speed–flow relationship on the M25, London’s main motorway which is recurrently congested, especially near Heathrow Airport. The proposed model had a better performance compared with the existing classic models. A whole year’s field data on various sites of the M25 motorway were collected by the National Highways MIDAS (Motorway Incident Detection and Automatic Signalling) system and analysed. The proposed model was fitter on both four-lane and lane-by-lane conditions than the existing models, in terms of lower relative error and RMSE values and higher R2 values (minimum R2 = 0.79), which means the proposed model captured the speed–flow relationship better. In addition, the proposed model was used to fit traffic characteristics under different weather conditions and decided the threshold of the CM algorithms using the Gaussian function. The results showed that both free-flow speed and road capacity were significantly reduced by up to 7% and 11%, respectively, under different rainfall conditions, and that congestion management should be triggered in advance on rainy days. Further analysis of extensive data over a wider space and time is required to test the transferability of these findings to other contexts.

Impact of short-term aircraft noise on cardiovascular disease risk in the area surrounding London Heathrow airport: the RISTANCO epidemiological study.

Long-term exposure to aircraft noise has been associated with small increases in cardiovascular disease risk, but there are almost no short-term exposure studies. Research questions were: Is there an association between short-term changes in exposure to aircraft noise and cardiovascular morbidity and mortality? What are the key effect modifiers? Is there variability in risk estimates between areas with consistent versus changing patterns of noise exposure? Do risk estimates differ when using different noise metrics? Descriptive analyses of noise levels and variability at different times of day, analyses of inequalities in noise exposure and case-crossover analyses of cardiovascular events in relation to aircraft noise exposure. Area surrounding London Heathrow airport. 2014-18. Whole population in study area. Cardiovascular disease hospitalisations and mortality. Aircraft noise levels modelled using a standard noise model for: (1) daily equivalent continuous sound levels at different times of day; (2) daily number of events above defined noise thresholds (2018 only). National Health Service digital hospital admission records and Office for National Statistics mortality records for 2014-18 for cardiovascular outcomes, plus individual-level confounders available from healthcare records. Confounder data including road traffic noise (Leicester modelled), rail noise and air pollution (Department for Environment, Food and Rural Affairs), area level deprivation and ethnicity (UK Census). The morning shoulder period (06.00-07.00 hours) was the noisiest of all eight bands (mean: 50.92 dB). The morning shoulder period also had the third highest number of noisy events (flights) > 60 dB per day, with three events across postcodes on average. However, the highest number of noisy events occurred in daytime (highest between 07.00 and 15.00 hours, second highest 15.00 and 19.00 hours). To identify areas with high variability in aircraft noise exposure (due to changes in flight paths because of wind direction and airport operations), we used coefficients of variation (CoV). The period 24.00-04.30 hours had the highest mean CoV (67.33-74.16), followed by 04.30-06.00 hours and 23.00-24.00 hours. Postcodes in the least deprived quintiles of Carstairs index or avoidable death rate had the lowest noise levels. In case-crossover analyses, we observed increased risk for cardiovascular disease hospital admissions for evening noise 19.00-23.00 hours (odds ratio 1.005, 95% confidence interval 1.000 to 1.010 per 5 dB), but not for other periods or mortality. Further analyses suggested that increased risks were occurring in postcodes with low CoV for noise. We found effect modification by age, sex, ethnicity, deprivation and season. The industry standard noise model, the Aviation Environmental Design Tool, used does not take account of wind direction, which may have led to some exposure misclassification. We developed a comprehensive dataset of daily aircraft noise variability. We found small associations between cardiovascular hospitalisations (but not deaths) and evening aircraft noise levels, particularly in areas with low variability of noise. More studies are needed to understand the effect of noise variation and respite/relief on cardiovascular disease. This award was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme (NIHR award ref: 15/192/13) and is published in full in Public Health Research; Vol. 12, No. 13. See the NIHR Funding and Awards website for further award information.

Comparative Analysis of Machine Learning Models for Weather Forecasting: A Heathrow Case Study

Accurate weather forecasting, especially temperature prediction, is fundamental for various segments within the UK, including agriculture, energy, and policy planning, as the nation adapts to the effects of climate change. This study addresses the limitations of conventional linear models in capturing the complex, non-linear relationships within meteorological data by comparing the effectiveness of different Machine Learning (ML) strategies. This study evaluates the performance of baseline ML models, such as Linear Regression (LR), Support Vector Regression (SVR), and K-Nearest Neighbors (KNN). It also examines advanced ensemble and boosting models such as Decision Tree (DT), Random Forest (RF), XGBoost (XGB), LightGBM (LGBM), and CatBoost, using a comprehensive dataset from Heathrow Airport. Detailed preprocessing, model training, and optimization through cross-validation were conducted, with performance assessed using Mean Squared Error (MSE) and Coefficient of Determination (R²) metrics. The results demonstrate that ensemble methods, particularly XGB and LGBM, offer superior predictive accuracy for weather forecasting tasks, highlighting their potential to enhance predictive models in meteorological applications.

Travelers' perception of smart airport facilities: An X (Twitter) sentiment analysis

This study investigates travelers' perceptions of smart airport facilities through sentiment analysis. It collected 39,616 comments from thirteen smart airports, and 25,572 tweets were analyzed. Most mentioned words about the smart airport included "technology" and "security". Comments also highlighted concerns about airport customer service, including "bag," "transit," and "disabled." The results also reveal that travelers are more concerned about the outcome of these facilities, such as the efficiency and time management of flights via smart facilities. The most mentioned words in the tweets related to smart airport operations are "flight," "waiting," and "time". Despite most smart facilities being equipped in the airports aiming to raise travelers' satisfaction, the results showed that 12 smart airports' tweets were generally neutral. Smart airport facilities might not add as much value to the airport impression as we expect. Brisbane International Airport was the only one with a positive perception of smart airport facilities. Most travelers mentioned that the airport had faster and better wifi and the airport is continuously improving. In contrast, Charles de Gaulle Airport and London Heathrow Airport had the highest percentage of negative sentiment, with 25.90% and 27.52% of tweets being negative, respectively. Travelers' complained that self-check-in kiosks were a mess, unhelpful staff and poor wifi. The results reveal travelers' concerns regarding smart airport facilities, and they let us know the importance of smart facility management. Smart airport facilities were initially designed to shorten travelers' time and enhance satisfaction, yet unhelpful staff and poor managed kiosks raise dissatisfaction. This study helps airport managers and operators to address the weakest part of smart airports as reflected in social media comments. It also fills the academic voids in examining travelers' satisfaction with smart facilities using social media and sentiment analysis via artificial intelligence.

The Arrivals Gate at Heathrow Airport.

The Arrivals Gate at Heathrow Airport.

Low-frequency cabin noise of rapid transit trains

Abstract Rapid transit or mass rapid transit (MRT) is a high-capacity public transport designed to carry a large number of passengers, especially during the peak hours. They are becoming very popular in major cities and some deem the presence of the rapid transit system in a city as a symbol of modern development and essential feature of urban life. As the rapid transit system expands, the traveling time on a rapid transit train may increase due to longer journey and cabin noise has become an environmental concern for the passengers. In the present study, we would attempt to do a more detailed study of the effect of viaduct height, in particular viaducts of different heights on the cabin noise of various rapid transit systems. The present study examined and benchmarked the cabin noise in terms of both dB(A) and dB(C) for four different rapid transit systems, namely part of the East-West line including the Tuas-West extension on elevated tracks with very high viaduct of the Singapore MRT System; part of Paris Line 2 from Anvers to Belleville station including a stretch of elevated track on viaduct; part of the Piccadilly line of London from Heathrow Airport to Green Park station with a stretch on surface ground; and finally part of Chongqing Line 3 from Gongmao to Lianglukou station across the Yangtze river. It was found that the cabin noise would be dominated by low-frequency content and would be better reflected if the measurements were presented in dB(C), especially for trains running on elevated tracks of greater height.

Validating aircraft noise models: Aviation environmental design tool at Heathrow

Aircraft noise models are fundamental tools for noise abatement, control, enforcement, evaluation, and policy-making. Validation of aircraft noise models is necessary to ensure their reliability and credibility, particularly given their significant impact on society, the economy, and public health. However, validating such models is often a complex undertaking, and an acceptable validation methodology still needs to be developed. In this study, the Federal Aviation Administration's (FAA) Aviation Environmental Design Tool (AEDT) aircraft noise model is validated by correlating the calculated and measured noise levels for a specific aircraft flying in a particular flight path at Heathrow Airport. The validation results suggest that the AEDT noise model estimates the actual noise level quite accurately for landings, with a variation less than 2 dB(A), but might be inaccurate for takeoffs for certain aircraft types, with variations reaching 10 dB(A), resulting in a considerable difference between the measured and calculated noise levels.

A New Simheuristic Approach for Stochastic Runway Scheduling

We consider a stochastic, dynamic runway scheduling problem involving aircraft landings on a single runway. Sequencing decisions are made with knowledge of the estimated arrival times (ETAs) of all aircraft due to arrive at the airport, and these ETAs vary according to continuous-time stochastic processes. Time separations between consecutive runway landings are modeled via sequence-dependent Erlang distributions and are affected by weather conditions, which also evolve continuously over time. The resulting multistage optimization problem is intractable using exact methods, and we propose a novel simheuristic approach based on the application of methods analogous to variable neighborhood search in a high-dimensional stochastic environment. Our model is calibrated using flight tracking data for over 98,000 arrivals at Heathrow Airport. Results from numerical experiments indicate that our proposed simheuristic algorithm outperforms an alternative based on deterministic forecasts under a wide range of parameter values, with the largest benefits seen when the underlying stochastic processes become more volatile and also when the on-time requirements of individual flights are given greater weight in the objective function.Funding: This work was supported by the Engineering and Physical Sciences Research Council [Grant EP/M020258/1].Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/trsc.2022.0400 .

BEYOND THE COURTROOM: THE EVOLUTION OF RIGHTS-BASED CLIMATE LITIGATION FROM URGENDA TO HELD AND ITS POLICY IMPACT

This paper examines the growing trend of employing international and human rights law in domestic climate change lawsuits as a strategic tool for legal action. Using a qualitative and comparative analysis of seminal case law, such as Urgenda Foundation v the Netherlands and R (Friends of the Earth and others) v Heathrow Airport Ltd and Held v State, this paper aims to explain the juridical trajectories and methodologies employed in rights-based climate litigation. The primary objective is to critically evaluate the potential regulatory impact of this emergent jurisprudential paradigm on both domestic legislation and international climate change treaties. The study posits that when traditional enforcement mechanisms enshrined in international environmental law treaties prove ineffective, domestic litigation grounded in human rights claims serve as a catalyst for transformative jurisprudence. This, in turn, can exert substantial pressure on state and non-state actors, compelling them to adopt more strict regulatory measures to mitigate the effects of climate change. The aim of this paper is twofold: first, to elucidate the effectiveness and development of rights-based jurisprudence in climate litigation, and second, to assess its potential for influencing the creation of stronger regulatory mechanisms at both state and international levels. The paper argues that when international treaties fail to take adequate climate change action, domestic lawsuits based on human rights claims start to serve as a lever of change, pressuring both state and non-state actors into adopting more ambitious measures. The study underscores the importance of this rights-based approach not merely as a legal strategy but as a multifaceted tool for effectuating systemic regulatory advancements and fostering climate justice.

COVID-19 IMPACT ON THE AIRPORTS’ AND THEIR HOME CARRIERS’ MARKETING STRATEGY

The article is dedicated to the impact of the crisis caused by the COVID-19 pandemic on the aviation industry and the measures taken by the governments, airports and home carriers to overcome the consequences of the said crisis. Government actions and state aid programs for airports and airlines are reviewed. Practical examples of government aid to airports and airlines are provided. It is also reviewed how these actions affected the performance of the largest European hub airports, namely – London (Heathrow), Istanbul, Paris (Charles de Gaulle), Amsterdam and Madrid. Threats and opportunities for both European airports and airlines are analyzed with the focus on the perspectives of London (Heathrow) airport. The operation of the mentioned airports was analyzed according to the following indicators - the recovery of passenger traffic (2023/2022 and 2023/2019), connectivity, GDP indicators of the countries where the mentioned airports are located. Information on the home carriers of the airports, i.e., the share of the airline's passengers in the total number of passengers handled by the airport, financial indicators of the airlines, plans for the development of the fleet and route network is provided. Recommendations on cooperation between airports and their base airlines are provided.

Linear snowfall trails during freezing fog in South East England, 23 January 2023: evidence for anthropogenic snowfall

AbstractAnthropogenic (industrial) snowfalls in the UK have hitherto been regarded as rare and extremely localised. This article analyses the distribution and possible causes of three ‘stripes’ of anthropogenic snow extending up to 10 km from the vicinity of London’s Heathrow airport on 23 January 2023. The early morning had been exceptionally cold at the airport, partly due to the relatively late arrival of freezing fog compared to other parts of the lower Thames valley. This is likely to have facilitated the formation of the snowfall by means of ice nuclei.

Emissions of ultrafine particles from civil aircraft: dependence upon aircraft type and passenger load

Very high concentrations of ultrafine particles (UFP) were measured at Heathrow Airport London. Exposure to UFP is strongly linked to adverse health effects and guidance for exposure limits has recently been provided by the World Health Organization (WHO). Using 1 s resolution UFP measurements and aircraft GPS data, measurements were assigned to individual aircraft and their operating mode, and this information was used to model UFP emission rates. In all cases, the highest emission rates were associated with departing aircraft, with rates for larger aircraft higher than smaller aircraft. Emission rates per passenger is influenced by the number of passengers carried, especially for arriving aircraft. Calculated emission rates are significantly higher than stated literature values, due to the inclusion of condensable particles in the measurements. These condensable particles are specifically not included in the regulated emission rates. Measured UFP concentrations within the airport boundary (and therefore not accessible to the general public) exceed the WHO guidance, indicating that UFP concentrations outside of the airport boundary could also be of concern. Assessing population exposure close to airports will be of increasing importance in future.

Assessment of anxiety levels for Heathrow Airport workers after Covid-19 pandemic situation

Objectives: The worldwide spreading of Covid-19 and the serious side effects associated with have pushed the scientist to study the several challenges produced after the end of the pandemic. Emotional health problems that airport workers have progressed after Covid-19 especially due to the high contact with people from different nationalities. The purpose of this research was to examine the level of anxiety of the selected population in the association with several factors like gender, age, and other demographical factors. Materials and methods: A 214 workers from Heathrow airport with 113 females and 101 males and with age range 20–50 were selected to the study. Different demographical factors were examined to see its relation with anxiety levels that resulted from Self-Rating Anxiety Scale (SAS). SPSS 28x was used to perform different statistical analysis test on the collected data. Results: Gender, experience, salary, and education were found to affect the level of anxiety significantly with a P-value lower or equal to 0.05 at confidence interval of 95%. Males were found to exert higher percentage of anxiety symptoms when compared with female. While salary above or lower than 30000$ was found to have a similar percentages and effect. On the other hand, experience played an important role to reduce anxiety, while education have increased the risk of anxiety. Conclusion: Different authorities and work locations including airports should be taking in consideration the anxiety after the end of Covid-19 pandemic to reduce the serious clinical outcomes associated with anxiety.

Aircraft noise and cardiovascular morbidity and mortality near Heathrow Airport: a case-crossover study

Aircraft noise and cardiovascular morbidity and mortality near Heathrow Airport: a case-crossover study

Health benefits of reducing aircraft pollution: evidence from changes in flight paths

This paper investigates externalities generated by air transportation pollution on health. As a source of exogenous variation, we use an unannounced 5-month trial that reallocated early morning aircraft landings at London Heathrow Airport. Our measure of health is prescribed medications spending on conditions known to be aggravated by pollution, especially sleep disturbances. We observe a significant and substantial decrease in prescribed drugs for respiratory and central nervous system disorders in the areas subjected to reduced air travel between 4:30 am and 6.00 am compared with the control regions. Our findings suggest a causal influence of aviation on health conditions.

Energy Harvesting on Airport Runway Using Piezoelectric Devices

Abstract—Exploiting ambient sources of vibration energy has become an emerging trend in the field of energy harvesting. There are several methods by which this can be achieved, out of which piezoelectricity has emerged as one of the efficient methods. For effective application of piezoelectric devices to harvest energy, it is important to recognise the sources or areas from where a large amount of vibration energy can be exploited. In this paper we present a multimorph piezoelectric device that would be employed to harvest vibrational energy from airport run-­‐ ways. We have explored the economic impact of our proposal by hypothetically employing our system on Heathrow Airport, London. Primarily the paper focuses on material selection, basicmechanical design and the economic impact of the piezoelectric harvester. KEYWORDS:Piezoelectric, Polyvinylidene fluoride, Quartz crystal, Lead zirconate titanate (PZT),M31 bacteriophage

GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting

A prompt and precise estimation of traffic conditions on the scale of a few minutes by analyzing past data is crucial for establishing an effective intelligent traffic management system. Nevertheless, because of the irregularity and nonlinear features of traffic flow data, developing a prediction model with excellent robustness poses a significant obstacle. Therefore, we propose genetic-search-algorithm-improved kernel extreme learning machine, termed GA-KELM, to unleash the potential of improved prediction accuracy and generalization performance. By substituting the inner product with a kernel function, the accuracy of short-term traffic flow forecasting using extreme learning machines is enhanced. The genetic algorithm evades manual traversal of all possible parameters in searching for the optimal solution. The prediction performance of GA-KELM is evaluated on eleven benchmark datasets and compared with several state-of-the-art models. There are four benchmark datasets from the A1, A2, A4, and A8 highways near the ring road of Amsterdam, and the others are D1, D2, D3, D4, D5, D6, and P, close to Heathrow airport on the M25 expressway. On A1, A2, A4, and A8, the RMSEs of the GA-KELM model are 284.67 vehs/h, 193.83 vehs/h, 220.89 vehs/h, and 163.02 vehs/h, respectively, while the MAPEs of the GA-KELM model are 11.67%, 9.83%, 11.31%, and 12.59%, respectively. The results illustrate that the GA-KELM model is obviously superior to state-of-the-art models.

After flight. Visions from London Heathrow

This paper uses creative methodologies to explore the future of relations between art and flight. Responding to the conditions of the pandemic, I take Heathrow Airport, which is near my home, as an observable and symbolic landscape inflected with art’s mobilities but also a window onto changing times. I draw on material gathered within the constraints of the lockdown walk – diary notes, photographs, correspondence – as well as theoretical framings that include planetary social thought, evidentiary forms of materialism and aesthetic reflections on the modern ruin. I ask: what axioms and assumptions about art and flight are unsettled by this landscape? What is revealed about relations between art and the planetary? And what possible futures does it offer artists striving to square internationalism with ecological consciousness?In the context of a climate crisis, the deserted airport is suggestive of a catastrophic future that reads as an allegory of fossil fuel dependence. Yet the complexity of disentangling art from flight, an inherently carbon hungry mode of transport, should not be underestimated – it will need to negotiate not just the terrain of environmental impact but also the artistic identities, exhibition models and curatorial personas that have flourished under the conditions of cheap flight.

Aircraft noise and cardiovascular morbidity and mortality near Heathrow Airport: A case-crossover study

Aircraft noise causes annoyance and sleep disturbance and there is some evidence of associations between long-term exposures and cardiovascular disease (CVD). We investigated short-term associations between previous day aircraft noise and cardiovascular events in a population of 6.3 million residing near Heathrow Airport using a case-crossover design and exposure data for different times of day and night. We included all recorded hospitalisations (n = 442,442) and deaths (n = 49,443) in 2014–2018 due to CVD. Conditional logistic regression was used to estimate the ORs and adjusted for NO2 concentration, temperature, and holidays. We estimated an increase in risk for 10 dB increment in noise during the previous evening (Leve OR = 1.007, 95% CI 0.999–1.015), particularly from 22:00–23:00 h (OR = 1.007, 95% CI 1.000–1.013), and the early morning hours 04:30–06:00 h (OR = 1.012, 95% CI 1.002–1.021) for all CVD admissions, but no significant associations with day-time noise. There was effect modification by age-sex, ethnicity, deprivation, and season, and some suggestion that high noise variability at night was associated with higher risks. Our findings are consistent with proposed mechanisms for short-term impacts of aircraft noise at night on CVD from experimental studies, including sleep disturbance, increases in blood pressure and stress hormone levels and impaired endothelial function.

Simulating Changes in Aircraft Noise at Heathrow Airport during the 2020 Covid-19 Lockdown

Global restrictions on domestic and international travel introduced in March 2020 as a result of the Covid-19 pandemic resulted in a significant reduction in air traffic movements around the world. This paper presents the findings of research carried out at London Heathrow Airport exploring the day-by-day changes in aircraft noise exposure and event levels over the period March 2020 to June 2020. The research was carried out using validated modelling of aircraft procedures and noise profiles alongside radar data obtained from the airport. This allowed trends in metrics such as LAeq, N65, and overflight to be considered in the form of contours, and at community locations. This was facilitated using geospatial databases and interactive dynamic reporting toolkits. The research has allowed estimates to be made of the point where aircraft noise at Heathrow Airport reached a minimum. It also provides some helpful insight as to the potential of generating daily noise exposure data and the advantages, and disadvantages of modelling using radar data.