Search Results (26)

The terminal maneuvering area (TMA) serves as a critical transition zone between upper enroute airways and airports, representing one of the most complex regions for managing high volumes of arrival and departure traffic. This paper presents the multi-route TMA scheduling problem as an optimization challenge aimed at optimizing TMA interventions, such as rerouting, speed control, time-based metering, dynamic minimum time separation, and holding procedures; the objective function minimizes schedule deviations and the accumulated holding time. Furthermore, the problem is formulated as a mixed-integer linear program (MILP) to facilitate finding solutions. A rolling horizon control (RHC) dynamic optimization framework is also introduced to decompose the large-scale problem into manageable subproblems for iterative resolution. To demonstrate the applicability and effectiveness of the proposed scheduling models, a hub airport—Chengdu Tianfu International Airport (ICAO code: ZUTF) in the Cheng-Yu Metroplex—is selected for validation. Numerical analyses confirm the superiority of the proposed models, which are expected to reduce aircraft delays, shorten airborne and holding times, and improve airspace resource utilization. This study provides intelligent decision support and engineering design ideas for the macroscopic-level collaborative optimization framework of the Integrated Arrival–Departure and Surface (IADS) system. Full article

Accurate short-term passenger forecasts help regional airports align capacity with demand and plan investments effectively. Drawing on quarterly traffic data for 2010–2024 supplied by the Polish Civil Aviation Authority, this study employs Holt–Winters exponential smoothing to predict passenger volumes at Szczecin–Goleniów Airport for 2025. Additive and multiplicative formulations were parameterized with Excel Solver, using the mean absolute percentage error to identify the better-fitting model. The additive version captured both the steady post-pandemic recovery and pronounced seasonal peaks, indicating that passenger throughput is likely to rise modestly year on year, with the highest loads expected in the summer quarter and the lowest in early spring. These findings suggest the airport should anticipate continued growth and consider adjustments to terminal capacity, apron allocation, and staffing schedules to maintain service quality. Because the Holt–Winters method extrapolates historical patterns and does not incorporate external shocks—such as economic downturns, policy changes, or public health crises—its projections are most reliable over the short horizon examined and should be complemented by scenario-based analyses in future work. This study contributes to sustainable airport management by providing a reproducible, data-driven forecasting framework that can optimize resource allocation with minimal environmental impact. Full article

The construction of parallel runways is an effective solution to address the constraints of urban land resources and mitigate flight delays caused by the increasing volume of air traffic. To ensure the safety of parallel approach operations and further enhance operational efficiency, this study proposes a quantitative safety risk assessment method for parallel approaches based on Monte Carlo simulation (MCS) and extreme value theory (EVT). Taking a parallel runway at a major airport in Southwest China as a case study, historical Automatic Dependent Surveillance-Broadcast (ADS-B) trajectory data were processed and analyzed to derive traffic flow characteristics and the actual distribution of approach performance. Subsequently, we developed a collision probability estimation model for parallel approaches based on Monte Carlo–extreme value theory (MC-EVT). Monte Carlo simulation was employed to conduct simulation experiments on the parallel approach process, and the collision risk was quantitatively assessed by integrating experimental data with an analysis based on extreme value theory. Finally, taking the parallel runways of a major airport in southwest China as a case study, experiments were conducted under various parallel approach scenarios to quantitatively assess the collision risk between aircraft. The experimental results indicate that the MC-EVT-based safety risk assessment method for parallel approaches reduces the reliance on traffic flow assumptions. Compared to the conventional Monte Carlo method, it achieves a faster convergence rate, significantly reduces computational workload, and improves computational efficiency by a factor of ten, thus demonstrating that the proposed method is capable of accurately and effectively quantifying low-probability collision risks. Furthermore, the findings reveal a strong correlation between parallel runway width and collision risk. The approach risk under a mixed-aircraft-type configuration is higher than that of a single-aircraft-type configuration, while offset approaches can enhance approach safety. This study can provide valuable references for the construction of parallel runways and the development of regulatory frameworks for parallel approach operations in China. Full article

Aviation emissions significantly impact air quality, contributing to environmental degradation and public health risks. This study aims to assess the impact of aviation-related emissions on air quality at Alexandroupolis Regional Airport, Greece, and evaluate the role of meteorological factors in pollution dispersion. Using machine learning models, we analyzed emissions data, including CO₂, NOx, CO, HC, SOx, PM_2.5, fuel consumption, and meteorological parameters from 2019–2020. Results indicate that NOx and CO₂ emissions showed the highest correlation with air traffic volume and fuel consumption (R = 0.63 and 0.67, respectively). Bayesian Linear Regression and Linear Regression emerged as the most accurate models, achieving an R² value of 0.96 and 0.97, respectively, for predicting PM_2.5 concentrations. Meteorological factors had a moderate influence, with precipitation negatively correlated with PM_2.5 (−0.03), while temperature and wind speed showed limited effects on emissions. A significant decline in aviation emissions was observed in 2020, with CO₂ emissions decreasing by 28.1%, NOx by 26.5%, and PM_2.5 by 35.4% compared to 2019, reflecting the impact of COVID-19 travel restrictions. Carbon dioxide had the most extensive percentage distribution, accounting for 75.5% of total emissions, followed by fuels, which accounted for 24%, and the remaining pollutants, such as NOx, CO, HC, SOx, and PM_2.5, had more minor impacts. These findings highlight the need for optimized air quality management at regional airports, integrating machine learning for predictive monitoring and supporting policy interventions to mitigate aviation-related pollution. Full article

In this paper, we present the main results from the Second ATS-Level Assessment of the Clean Sky 2 Technology Evaluator. We first present the models employed and then move to the passenger and fleet forecast results up to 2050. Based upon these traffic forecasts, we show the environmental effect of Clean Sky 2 technology in terms of CO₂ emissions. The main benefit of the forecast method employed is its high resolution in terms of each flight route between airports being modelled. Consequently, we can consider effects such as airport capacity constraints which will have a substantial impact on future passenger volume and fleet development. Full article

With the global increase in air transport demand, the shortage of ground handling personnel to support ground operations at airports has become a major challenge, impacting airport services and causing considerable flight delays. This study presents a novel method to generate trip data that specify the origin and destination locations as the purpose of travel for each ground support equipment (GSE) vehicle. The proposed method uses data obtained from comprehensive observations of 2234 GSE vehicles over a 24 h × 7 d time interval at Tokyo International Airport. From these observations and trip data, the characteristics of the driving conditions for each GSE vehicle type, the locations where GSE traffic volume increases in the airport, and changes in the time interval are identified. The primary results show that the GSE traffic volume is the highest mainly around passenger terminals and in the vehicle corridors connecting these terminals, which aligns with the airport’s operational status. Investigating GSE driving conditions, such as the traffic flow throughout an airport, can provide valuable data to improve the efficiency of GSE scheduling and facilitate the introduction of automated driving technology. Full article

The advancement of Arrival MANager (AMAN) is crucial for addressing the increasing complexity and demand of modern airspace. This study evaluates the operational feasibility and effectiveness of an innovative AMAN designed for en route airspace, the so-called En Route AMAN. The En Route AMAN functions as a controller support system, facilitating the sharing of information between en route air traffic controllers (ATCos), approach controllers (current AMAN), and airport controllers (Departure Managers) in airports with multiple runways. The En Route AMAN aims to support upstream ATCos by sequencing and spacing of incoming streams via speed control and runway assignment, thereby enhancing overall air traffic efficiency. Human-In-The-Loop simulations involving rated ATCos are performed under scenarios that replicate real-world traffic and weather conditions. These simulations focus on upstream airspace to assess the impact of En Route AMAN on delay mitigation and ATCos’ performance. Unlike previous studies that solely relied on theoretical models and fast-time simulation for operational feasibility evaluation, this approach incorporates ATCos’ real-time decision-making, situational awareness, and task management, addressing critical operationalization challenges. The results demonstrated that the En Route AMAN could reduce the average flight duration by up to 25.6 s and decrease the total number of ATCo instructions by up to 20% during peak traffic volume. These findings support that the En Route AMAN is both operationally viable and effective in mitigating arrival delays, highlighting the importance of Human-In-The-Loop for practical validation. Full article

Integrating advanced air mobility (AAM) into the logistics of high-value electronic commodities can enhance efficiency and promote sustainability. The objective of this study is to optimize the logistics network for high-value electronics by integrating AAM solutions, specifically using heavy-lift cargo drones for middle-mile transport and using the mostly rural and small urban U.S. state of North Dakota as a case study. The analysis utilized geographic information system (GIS) and spatial optimization models to strategically assign underutilized airports as multimodal freight hubs to facilitate the shift from long-haul trucks to middle-mile air transport. Key findings demonstrate that electronics, because of their high value-to-weight ratio, are ideally suited for air transport. Comparative analysis shows that transport by drones can reduce the average cost per ton by up to 60% compared to traditional trucking. Optimization results indicate that a small number of strategically placed logistical hubs can reduce average travel distances by more than 13% for last-mile deliveries. Cost analyses demonstrate the viability of drones for middle-mile transport, especially on lower-volume rural routes, highlighting their efficiency and flexibility. The study emphasizes the importance of utilizing existing infrastructure to optimize the logistics network. By replacing truck traffic with drones, AAM can mitigate road congestion, reduce emissions, and extend infrastructure lifespan. These insights have critical implications for supply chain managers, shippers, urban planners, and policymakers, providing a decision support system and a roadmap for integrating AAM into logistics strategies. Full article

Road congestion in Amman City has been increasing yearly, due to the increase in private car ownership and traffic volumes. This study aims to (a) evaluate the toll road’s effects on society and the economy in Amman, Jordan, through a survey questionnaire using statistical software (SPSS), (b) assess the impact of the toll road on reducing congestion and delays using micro-simulation (VISUM), (c) identify the optimal toll price for a selected road using VISUM and (d) validate the simulated models with the optimal revenue. Traffic, geometric, and cost data about the toll technique of two sections on the Airport Highway (from the Ministry of Foreign Affairs to the Madaba Interchange; and from the Madaba Interchange to the Queen Alia International Airport (QAIA) Interchange) were used for simulation purposes. The toll road (across seven different scenarios at different prices) was evaluated for optimal revenue. The survey questionnaire was made based on all scenarios, including the AM peak hour. The operation cost for the toll road was determined based on the Greater Amman Municipality (GAM). The best scenario was determined based on the value of revenue (JOD). The results indicate that higher acceptance is achieved when applying road pricing during the AM peak hour and that users prefer the charging method based on travelled distance (54.02%). Additionally, the total cost of the manual toll collection (MTC) method is 126,935 JOD. Road pricing can reduce traffic delay (or speed up traffic flow) by 4.61 min in the southbound direction and by 9.52 min in the northbound direction. The optimal toll value is 0.25 JOD (34.08%), with revenues of 1089.6 JOD for 2024 and 1122.6 JOD for 2025. Eventually, applying road pricing on the airport road is shown to be effective and economically feasible only when using the manual method. Full article

A transfer function is the main model of a design program that correlates mechanistically calculated damage to a pavement with the actual field distress. In this study, a pavement condition index (PCI) model that reflects environmental and traffic loads was developed as a transfer function for a design program for airport concrete pavements. Seven runways from five airports in Korea, for which design data were available, were selected as target runways, and their design, traffic, and weather data were collected. The minimum tensile stress of the slab generated by environmental loads and the maximum tensile stress induced by combined environmental and traffic loads were calculated by conducting a three-dimensional finite element analysis. The cumulative fatigue damage to the target runways was calculated by substituting the climatic conditions and traffic volume into the fatigue model, which considered the minimum and maximum tensile stresses. The PCI, which was uniformly and varyingly distributed according to pavement age, was adopted as the indicator of actual field distress, whereas the previously used structural condition index was mostly 100 because no structural distress occurred, regardless of the pavement age. The PCI model was established via multi-regression analysis to predict field PCIs using mechanistically calculated cumulative fatigue damage and pavement age as independent variables. The actual and predicted PCIs of the target airports were compared to validate the PCI model. Full article

Nowadays, in view of the growing traffic volume, an appropriate aircraft sequencing in the arrival sector is needed to maintain safety levels and improve the performance of the runway system and flight times. This paper presents a digital assistant supporting the air traffic controller in aircraft sequencing by providing suggestions for next waypoints, speed adjustments and altitude holdings. On the one hand, the suggested paths are such to preserve safety by ensuring the prescribed minimum separation, while also promoting environmental benefits through continuous descent operations (CDO). On the other hand, the suggestions aim to reduce landing times, improving the runway throughput. The proposed tool exploits multipath planning, for which a global optimization technique is used in conjunction with the dynamic time warping distance metric and a reinforcement learning approach to resolve conflicts through speed modulation and/or altitude holding. The performances of the assistant are assessed by means of a multi-agent simulator tailoring its reasoning on the procedures of Olbia airport (Italy). The analysis of a stream of many random aircraft has revealed its effectiveness in terms of arrival time reduction against a standard first-come-first-served strategy, usually adopted by controllers, and strong conflict reduction while considering a CDO-like adherence. Additionally, the man/machine interaction is investigated through an analysis of the overall latency from the suggestions provided by the digital assistant up to the actual aircraft maneuvers. Full article

Globalization has resulted in increases in air transportation demand and air passenger traffic. With the increases in air traffic, airports face challenges related to infrastructure, air services, and future development. Air traffic forecasting is essential to ensuring appropriate investment in airports. In this study, we combined fuzzy theory with support vector regression (SVR) to develop a fuzzy SVR (FSVR) model for forecasting international airport traffic. This model was used to predict the air traffic volumes at the world’s 10 busiest airports in terms of air traffic in 2018. The predictions were made for the period from August 2014 to December 2019. For fuzzy time series, the developed FSVR model can consider historical air traffic changes. The FSVR model can suitably divide air traffic changes into appropriate fuzzy sets, generate membership function values, and establish fuzzy relations to produce fuzzy interpolated values with minimal errors. Thus, in the prediction of continuous data, the fuzzy data with the smallest errors can be subjected to SVR to find the optimal hyperplane model with the minimum distance to the appropriate support vector sample points. The performance of the proposed model was compared with those of five other models. Of the compared models, the FSVR model exhibited the lowest mean absolute percentage error (MAPE), mean absolute error, and root mean square error for all types of traffic at all of the airports analyzed; all of the MAPE values were below 2.5. The FSVR model can predict future growth trends in air traffic, air passenger flows, aircraft flows, and logistics. An airport authority can use this model to analyze the existing operational facilities and service capacity, find bottlenecks in airport operations, and create a blueprint for future development. The findings revealed that implementing a hybrid modeling approach, specifically the FSVR model, can significantly enhance the performance of the SVR model. The FSVR model allows airlines to predict traffic growth patterns, identify viable new destinations, optimize their schedules or fleet, make accurate marketing decisions, and plan traffic effectively. The FSVR model can guide the timely construction of appropriate airport facilities with accurate predictions. Rapid, cost-effective, efficient, and balanced transportation planning enables the provision of fast, cost-effective, comfortable, safe, and convenient passenger and cargo services while ensuring the proper planning of the airport’s capacity for land-side transportation connections. Full article

Some of the most critical transportation infrastructures are road tunnels. Underground passageways for motorists are provided through this cost-effective engineering solution, which allows for high traffic volumes. A crucial aspect of the operation of road tunnels is fire safety. Risk assessments have been established to ensure the level of safety in tunnels. As the existing quantitative risk analysis (QRA) models are inapplicable to assess the fire risk in UK road tunnels, this paper presents a novel QRA model, named LBAQRAMo, for UK road tunnels. This model consists of two main sections: quantitative frequency analysis, to estimate the frequency of fire incidents via an event tree; and quantitative consequences analysis, to model the consequences of fire incidents. LBAQRAMo covers the risk to tunnel users. The result of the risk analysis is the expected value of the societal risk of the investigated tunnel, presented via F/N curve. Another major result of this model is the estimation of the number of fatalities for each scenario based on the comparison between required safe egress time (RSET) and available safe egress time (ASET). Risk evaluation was carried out by comparison of the tunnel under study with the UK ALARP limit. The operation of the model is demonstrated by its application to the Gibraltar Airport Tunnel as a case study. Simulation of 34 different possible scenarios show that the tunnel is safe for use. The sensitivity of the model to HGV fire incident frequency and basic pre-movement times was studied as well. Full article

Taxi travel flow patterns and their interday stability play an important role in the planning of urban transportation and public service facilities. Existing studies pay little attention to the stability of the travel flow patterns between days, and it is difficult to consider the impact of dynamic changes in daily travel demand analysis when supporting related decision making. Taxi trajectory data have been widely used in urban taxi travel-pattern analysis. This paper uses the taxi datasets of Shenzhen and New York to analyze and compare the interday stability of the taxi travel spatial structure and the flow volume based on the improved Levenshtein algorithm and geographic flow theory. The results show that (1) interday differences in taxi travel flow are obvious in both spatial structure and flow volume, high-frequency origin–destination (OD) trips are relatively stable; (2) the ODs between the central urban area and surrounding areas exhibit high traffic volume and high interday stability, and the ODs starting or ending at an airport exhibit high traffic stability; (3) one week’s data can describe 86% of the overall travel structure and 84% of travel flow in Shenzhen, and one week’s New York data can describe 73% of travel structure and 76% of travel flow. There are differences in the travel patterns of people in different cities, and the representativeness of datasets in different cities will be different. These findings can help to better understand the outcomes of taxi travel patterns derived from a relatively short period of data to avoid potential misuse in related decision making. Full article

In view of the time series update of airport runway health status detection data, the Markov chain of stochastic process theory was adopted. Considering the influence of aircraft traffic load, age, and pavement structure surface-layer thickness on the performance deterioration process of airport runways, the method of survival analysis was used. The parameter model of survival analysis was used to establish the duration function model of the four condition states of the airport runway PCI (pavement condition index). The Markov transition matrix for the performance prediction of airport runways was constructed. In order to evaluate the ability of the Markov transition matrix method to predict the trend of deterioration for PCI of the airport runway under different conditions of aircraft traffic volume and thickness of the runway pavement surface, a data set was constructed with the actual inspection data of the airport runway, and the corresponding samples were selected for analysis. The results showed that a Markov transition matrix for airport runway performance prediction, constructed based on survival analysis theory, can combine discontinuous inspection data or monitoring data with Weibull function survival curves. The method proposed in this paper can quantitatively predict the remaining service life of airport runways and provide support for cost-effective decisions about airport pavement maintenance and rehabilitation. Full article