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Passenger disembarkation takes place in the confined space of the aircraft cabin. Boarding can be regulated to a certain extent, but this does not apply to disembarking at the end of a flight. COVID-19 constraints require that cabin procedures not only be operationally efficient but also effectively reduce the risk of virus transmission to passengers. We have developed a new mathematical model that accounts for these conflicting goals. We used an already improved seat assignment for passenger groups (e.g., families or couples) and implemented a genetic algorithm that generates improved disembarkation sequences. Our use cases show a significant 40% reduction in disembarkation time when physical spacing between passenger groups is required to comply with pandemic regulations. To inform passenger groups about the disembarkation sequence, we propose to activate the cabin lights at the seats in a dedicated way. Thus, our developed methodology could already be applied to actual airline operations.
M. Schultz; M. Soolaki. Optimized aircraft disembarkation considering COVID-19 regulations. Transportmetrica B: Transport Dynamics 2021, 1 -21.
AMA StyleM. Schultz, M. Soolaki. Optimized aircraft disembarkation considering COVID-19 regulations. Transportmetrica B: Transport Dynamics. 2021; ():1-21.
Chicago/Turabian StyleM. Schultz; M. Soolaki. 2021. "Optimized aircraft disembarkation considering COVID-19 regulations." Transportmetrica B: Transport Dynamics , no. : 1-21.
The air traffic is mainly divided into en-route flight segments, arrival and departure segments inside the terminal maneuvering area, and ground operations at the airport. To support utilizing available capacity more efficiently, in our contribution we focus on the prediction of arrival procedures, in particular, the time-to-fly from the turn onto the final approach course to the threshold. The predictions are then used to determine advice for the controller regarding time-to-lose or time-to-gain for optimizing the separation within a sequence of aircraft. Most prediction methods developed so far provide only a point estimate for the time-to-fly. Complementary, we see the need to further account for the uncertain nature of aircraft movement based on a probabilistic prediction approach. This becomes very important in cases where the air traffic system is operated at its limits to prevent safety-critical incidents, e.g., separation infringements due to very tight separation. Our approach is based on the Quantile Regression Forest technique that can provide a measure of uncertainty of the prediction not only in form of a prediction interval but also by generating a probability distribution over the dependent variable. While the data preparation, model training, and tuning steps are identical to classic Random Forest methods, in the prediction phase, Quantile Regression Forests provide a quantile function to express the uncertainty of the prediction. After developing the model, we further investigate the interpretation of the results and provide a way for deriving advice to the controller from it. With this contribution, there is now a tool available that allows a more sophisticated prediction of time-to-fly, depending on the specific needs of the use case and which helps to separate arriving aircraft more efficiently.
Stanley Förster; Michael Schultz; Hartmut Fricke. Probabilistic Prediction of Separation Buffer to Compensate for the Closing Effect on Final Approach. Aerospace 2021, 8, 29 .
AMA StyleStanley Förster, Michael Schultz, Hartmut Fricke. Probabilistic Prediction of Separation Buffer to Compensate for the Closing Effect on Final Approach. Aerospace. 2021; 8 (2):29.
Chicago/Turabian StyleStanley Förster; Michael Schultz; Hartmut Fricke. 2021. "Probabilistic Prediction of Separation Buffer to Compensate for the Closing Effect on Final Approach." Aerospace 8, no. 2: 29.
The corona pandemic significantly changes the processes of aircraft and passenger handling at the airport. In our contribution, we focus on the time-critical process of aircraft boarding, where regulations regarding physical distances between passengers will significantly increase boarding time. The passenger behavior is implemented in a field-validated stochastic cellular automata model, which is extended by a module to evaluate the transmission risk. We propose an improved boarding process by considering that most of the passengers are travel together and should be boarded and seated as a group. The NP-hard seat allocation of groups with minimized individual interactions between groups is solved with a genetic algorithm. Then, the improved seat allocation is used to derive an associated boarding sequence aiming at both short boarding times and low risk of virus transmission. Our results show that the consideration of groups will significantly contribute to a faster boarding (reduction of time by about 60%) and less transmission risk (reduced by 85%) compared to the standard random boarding procedures applied in the pandemic scenario.
Michael Schultz; Majid Soolaki. Analytical approach to solve the problem of aircraft passenger boarding during the coronavirus pandemic. Transportation Research Part C: Emerging Technologies 2021, 124, 102931 -102931.
AMA StyleMichael Schultz, Majid Soolaki. Analytical approach to solve the problem of aircraft passenger boarding during the coronavirus pandemic. Transportation Research Part C: Emerging Technologies. 2021; 124 ():102931-102931.
Chicago/Turabian StyleMichael Schultz; Majid Soolaki. 2021. "Analytical approach to solve the problem of aircraft passenger boarding during the coronavirus pandemic." Transportation Research Part C: Emerging Technologies 124, no. : 102931-102931.
Competitive price pressure and economic cost pressure constantly force airlines to improve their optimization strategies. Besides predictable operational costs, delay costs are a significant cost driver for airlines. Especially reactionary delay costs can endanger the profitability of such a company. These time-dependent costs depend on the number of sensitive transfer passengers. This cost component is represented by the number of missed flights and the connectivity of onward flights, i.e., the offer of alternative flight connections. The airline has several options to compensate for reactionary delays, for example, by increasing cruising speeds, shortening turnaround times, rebookings and cancellations. The effects of these options on the cost balance of airline total operating costs have been examined in detail, considering a flight-specific number of transfer passengers. The results have been applied to a 24-h rotation schedule of a large German hub airport. We found, that the fast turnaround and increasing cruise speed are the most effective strategies to compensate for passenger-specific delay costs. The results could be used in a multi-criteria trajectory optimization to find a balance between environmentally-driven and cost-index-driven detours and speed adjustments.
Judith Rosenow; Philipp Michling; Michael Schultz; Jörn Schönberger. Evaluation of Strategies to Reduce the Cost Impacts of Flight Delays on Total Network Costs. Aerospace 2020, 7, 165 .
AMA StyleJudith Rosenow, Philipp Michling, Michael Schultz, Jörn Schönberger. Evaluation of Strategies to Reduce the Cost Impacts of Flight Delays on Total Network Costs. Aerospace. 2020; 7 (11):165.
Chicago/Turabian StyleJudith Rosenow; Philipp Michling; Michael Schultz; Jörn Schönberger. 2020. "Evaluation of Strategies to Reduce the Cost Impacts of Flight Delays on Total Network Costs." Aerospace 7, no. 11: 165.
With the rise of COVID-19, the sustainability of air transport is a major challenge, as there is limited space in aircraft cabins, resulting in a higher risk of virus transmission. In order to detect possible chains of infection, technology-supported apps are used for social distancing. These COVID-19 applications are based on the display of the received signal strength for distance estimation, which is strongly influenced by the spreading environment due to the signal multipath reception. Therefore, we evaluate the applicability of technology-based social distancing methods in an aircraft cabin environment using a radio propagation simulation based on a three-dimensional aircraft model. We demonstrate the susceptibility to errors of the conventional COVID-19 distance estimation, which can lead to large errors in the determination of distances and to the impracticability of traditional tracing approaches during passenger boarding/deboarding. In the context of the future connected cabin, a robust distance measurement must be implemented to ensure safe travel. Finally, our results can be transferred to similar fields of application, e.g., trains or public transport.
Paul Schwarzbach; Julia Engelbrecht; Albrecht Michler; Michael Schultz; Oliver Michler. Evaluation of Technology-Supported Distance Measuring to Ensure Safe Aircraft Boarding during COVID-19 Pandemic. Sustainability 2020, 12, 8724 .
AMA StylePaul Schwarzbach, Julia Engelbrecht, Albrecht Michler, Michael Schultz, Oliver Michler. Evaluation of Technology-Supported Distance Measuring to Ensure Safe Aircraft Boarding during COVID-19 Pandemic. Sustainability. 2020; 12 (20):8724.
Chicago/Turabian StylePaul Schwarzbach; Julia Engelbrecht; Albrecht Michler; Michael Schultz; Oliver Michler. 2020. "Evaluation of Technology-Supported Distance Measuring to Ensure Safe Aircraft Boarding during COVID-19 Pandemic." Sustainability 12, no. 20: 8724.
Air Transportation is a major contributor to international mobility and has high requirements to ensure safe and secure operations. Aircraft ground operations are impacted significantly by the current pandemic situation so that standard operating procedures need a redesign to incorporate the upcoming sanitation requirements. In particular, the passenger boarding process is challenged with requirements for physical distances between passengers, while in addition to standard cleaning, the cabin has to be disinfected after each flight. We evaluate potential alterations of these two aircraft cabin processes with respect to a pre-pandemic reference aircraft turnaround. The implementation of microscopic approaches allows to consider individual interactions and a step-wise process adaptation aiming for an efficient operational design. We find a significant extension of boarding times (more than doubled) if the physical distance rule is applied. The new disinfection process further extends the critical path of the turnaround, so we see a high impact on airport and airline operations. To compensate for the increased workload and process times, we provide an integrated cleaning and disinfection procedure with additional personnel. Our results indicate that the pre-pandemic turnaround times cannot be maintained for the same seat load, even if the process adaptations are being implemented. However, a seat allocation scheme with empty middle-seats (seat load of 67%) and the use of an apron position (additional use of rear aircraft door for boarding) enable pre-pandemic turnaround times without additional cleaning personnel. Aircraft turnarounds at terminal positions require between 10% (with additional personnel) and 20% (without additional personnel) more ground time.
Michael Schultz; Jan Evler; Ehsan Asadi; Henning Preis; Hartmut Fricke; Cheng-Lung Wu. Future aircraft turnaround operations considering post-pandemic requirements. Journal of Air Transport Management 2020, 89, 101886 -101886.
AMA StyleMichael Schultz, Jan Evler, Ehsan Asadi, Henning Preis, Hartmut Fricke, Cheng-Lung Wu. Future aircraft turnaround operations considering post-pandemic requirements. Journal of Air Transport Management. 2020; 89 ():101886-101886.
Chicago/Turabian StyleMichael Schultz; Jan Evler; Ehsan Asadi; Henning Preis; Hartmut Fricke; Cheng-Lung Wu. 2020. "Future aircraft turnaround operations considering post-pandemic requirements." Journal of Air Transport Management 89, no. : 101886-101886.
Air travel appears as particularly hazardous in a pandemic situation, since infected people can travel worldwide and could cause new breakouts in remote locations. The confined space conditions in the aircraft cabin necessitate a small physical distance between passengers and hence may boost virus transmissions. In our contribution, we implemented a transmission model in a virtual aircraft environment to evaluate the individual interactions between passengers during aircraft boarding and deboarding. Since no data for the transmission is currently available, we reasonably calibrated our model using a sample case from 2003. The simulation results show that standard boarding procedures create a substantial number of possible transmissions if a contagious passenger is present. The introduction of physical distances between passengers decreases the number of possible transmissions by approx. 75% for random boarding sequences, and could further decreased by more strict reduction of hand luggage items (less time for storage, compartment space is always available). If a second door is used for boarding and deboarding, the standard boarding times could be reached. Individual boarding strategies (by seat) could reduce the transmission potential to a minimum, but demand for complex pre-sorting of passengers. Our results also exhibit that deboarding consists of the highest transmission potential and only minor benefits from distance rules and hand luggage regulations.
Michael Schultz; Jörg Fuchte. Evaluation of Aircraft Boarding Scenarios Considering Reduced Transmissions Risks. Sustainability 2020, 12, 5329 .
AMA StyleMichael Schultz, Jörg Fuchte. Evaluation of Aircraft Boarding Scenarios Considering Reduced Transmissions Risks. Sustainability. 2020; 12 (13):5329.
Chicago/Turabian StyleMichael Schultz; Jörg Fuchte. 2020. "Evaluation of Aircraft Boarding Scenarios Considering Reduced Transmissions Risks." Sustainability 12, no. 13: 5329.
At present, en-route flight traffic is carried out on a system of predefined routes with a low number of intersections between aircraft trajectories. This enables the air traffic controllers to control and supervise the traffic, especially around these intersections. Consequently, the route system leads to a low ratio of used to unused airspace, where not necessarily the shortest route is used for each flight. To reduce trajectory length, the idea of free routing has been developed, whereby each aircraft uses the direct connection between origin and destination airport, generating a traffic distribution which uses nearly the entire available airspace. As a consequence, many intersections between flight trajectories occur, making it more difficult for controllers to handle. We use these intersections as the basis of a so-called main-flow system with trajectories consisting of intersection points instead of waypoints. The intersections of all trajectories of a traffic sample are clustered and the resulting cluster centres are used as nodes in a route system. Additional processing is applied to identify a system of main flows and reduce the number of intersections to an acceptable amount. Our approach is able to identify major traffic flows within unstructured great-circle traffic and to create a main-flow system which is a compromise between the flexibility of free routing and the easier surveillance by controllers in the case of a predefined route network. To prove the ability of the proposed method to identify main flows, it was applied to a scenario of planned flights following the standard route structure. Subsequent tests with two different free-routing scenarios led to new route systems where the median adapted trajectory length for flights of the traffic sample is merely 0.9% (respectively 4.1%) higher than the direct connections. Furthermore, structural complexity is lower for intersections (cluster centres) of the new main-flow system compared to those of direct or great-circle scenarios.
Ingrid Gerdes; Annette Temme; Michael Schultz. From free-route air traffic to an adapted dynamic main-flow system. Transportation Research Part C: Emerging Technologies 2020, 115, 102633 .
AMA StyleIngrid Gerdes, Annette Temme, Michael Schultz. From free-route air traffic to an adapted dynamic main-flow system. Transportation Research Part C: Emerging Technologies. 2020; 115 ():102633.
Chicago/Turabian StyleIngrid Gerdes; Annette Temme; Michael Schultz. 2020. "From free-route air traffic to an adapted dynamic main-flow system." Transportation Research Part C: Emerging Technologies 115, no. : 102633.
Publicly available aircraft airborne and ground movement data pave the way to new advanced analyses of complex behaviours and collaborative decision making tools for the optimisation of airport operations. Such data-driven approaches will allow cost efficient implementations, which are a key enabler for the efficient integration of small/medium sized airports into the air transportation network. We present an operational milestone concept based on Automatic Dependent Surveillance - Broadcast (ADS-B) messages emitted by approaching and departing aircraft. Since aircraft have to be equipped with a compliant transponder from 2020, airports only need cheap receivers to observe operations at the runway/taxiway system and on the apron (including parking positions). These observations will allow for a systematic monitoring (using operational milestones) and predictive analytics to provide estimated values for future system states. In this contribution, we present the core elements of an innovative framework, which may bring new insights for airport operations optimisation, with a particular focus on small and medium ones. We process here aircraft movements on the ground and around Zurich airport and present four examples of applications, which will enable prediction-based decision assistance tools for efficient airport operations.
Michael Schultz; Xavier Olive; Judith Rosenow; Hartmut Fricke; Sameer Alam. Analysis of airport ground operations based on ADS-B data. 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) 2020, 1 -9.
AMA StyleMichael Schultz, Xavier Olive, Judith Rosenow, Hartmut Fricke, Sameer Alam. Analysis of airport ground operations based on ADS-B data. 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT). 2020; ():1-9.
Chicago/Turabian StyleMichael Schultz; Xavier Olive; Judith Rosenow; Hartmut Fricke; Sameer Alam. 2020. "Analysis of airport ground operations based on ADS-B data." 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) , no. : 1-9.
Airports across the world are expanding by building multiple ground control towers and resorting to complex taxiway and runway system, in response to growing air traffic. Current outcome- based ground safety management at the airside may impede our potential to learn from and adapt to evolving air traffic scenarios, owing to the sparsity of accidents when compared with number of daily airside operations. To augment airside ground safety at Singapore Changi airport, in this study, we predict dynamic hot spots- areas where multiple aircraft may come in close vicinity on taxiways, as pre-cursor events to airside conflicts. We use airside infrastructure and A-SMGCS operations data of Changi airport to model aircraft arrival at different taxiway intersections both in temporal and spatial dimensions. The statistically learnt spatial-temporal model is then used to compute conflict probability at identified intersections, in order to evaluate conflict coefficients or hotness values of hot spots. These hot spots are then visually displayed on the aerodrome diagram for heightened attention of ground ATCOs. In the Subjective opinion of Ground Movement Air Traffic Controller, highlighted Hot Spots make sense and leads to better understanding of taxiway movements and increased situational awareness. Future research shall incorporate detailed human-in-the-loop validation of the dynamic hot spot model by ATCOs in 360 degree tower simulator.
Hasnain Ali; Raphael Delair; Duc-Thinh Pham; Sameer Alam; Michael Schultz. Dynamic Hot Spot Prediction by Learning Spatial- Temporal Utilization of Taxiway Intersections. 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) 2020, 1 -10.
AMA StyleHasnain Ali, Raphael Delair, Duc-Thinh Pham, Sameer Alam, Michael Schultz. Dynamic Hot Spot Prediction by Learning Spatial- Temporal Utilization of Taxiway Intersections. 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT). 2020; ():1-10.
Chicago/Turabian StyleHasnain Ali; Raphael Delair; Duc-Thinh Pham; Sameer Alam; Michael Schultz. 2020. "Dynamic Hot Spot Prediction by Learning Spatial- Temporal Utilization of Taxiway Intersections." 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) , no. : 1-10.
Increasing demands on a highly efficient air traffic management system go hand in hand with increasing requirements for predicting the aircraft’s future position. In this context, the airport collaborative decision-making framework provides a standardized approach to improve airport performance by defining operationally important milestones along the aircraft trajectory. In particular, the aircraft landing time is an important milestone, significantly impacting the utilization of limited runway capacities. We compare different machine learning methods to predict the landing time based on broadcast surveillance data of arrival flights at Zurich Airport. Thus, we consider different time horizons (look ahead times) for arrival flights to predict additional sub-milestones for n-hours-out timestamps. The features are extracted from both surveillance data and weather information. Flights are clustered and analyzed using feedforward neural networks and decision tree methods, such as random forests and gradient boosting machines, compared with cross-validation error. The prediction of landing time from entry points with a radius of 45, 100, 150, 200, and 250 nautical miles can attain an MAE and RMSE within 5 min on the test set. As the radius increases, the prediction error will also increase. Our predicted landing times will contribute to appropriate airport performance management.
Gong Chen; Judith Rosenow; Michael Schultz; Ostap Okhrin. Using Open Source Data for Landing Time Prediction with Machine Learning Methods. Proceedings 2020, 59, 5 .
AMA StyleGong Chen, Judith Rosenow, Michael Schultz, Ostap Okhrin. Using Open Source Data for Landing Time Prediction with Machine Learning Methods. Proceedings. 2020; 59 (1):5.
Chicago/Turabian StyleGong Chen; Judith Rosenow; Michael Schultz; Ostap Okhrin. 2020. "Using Open Source Data for Landing Time Prediction with Machine Learning Methods." Proceedings 59, no. 1: 5.
Low cost carriers usually operate from no-frills budget terminals which are designed for quick aircraft turnaround, faster passenger connections with minimal inter-gate passenger transfer times. Such operations are highly sensitive to factors such as aircraft delays, turnaround time and flight connection time and may lead to missed connections for self-connecting transfer passengers. In this paper, we propose a passenger-centric model to analyze the effect of turnaround times, minimum connection times and stochastic delays on missed connections of self-connecting passengers. We use Singapore Changi International Airport Terminal 4, which mainly caters to budget/low cost carriers, as a case study to demonstrate the impact of operational uncertainties on these passenger connections, considering an optimal gate assignment by using heuristic search for scheduled arrivals. The proposed model also incorporates reassignment of gates in the disrupted scenario to minimize spatial deviation from the optimized gate assignments. Results show that the chances of missed connections can be significantly reduced by operationally maintaining higher turnaround time and minimum connection time and by bringing down delays at the airport. Specifically, by maintaining the flight turnaround time at 50 min, minimum connection time at 60 min and by containing arrival delays within 70% of the current delay spread at Terminal 4, transfer passenger missed connections can be prevented for almost all the flights. The gate assignment method adopted in this study is generic and may help to identify the gates, which are more prone to missed connections given operational uncertainties under different flight scenarios.
Hasnain Ali; Yash Guleria; Sameer Alam; Michael Schultz. A Passenger-Centric Model for Reducing Missed Connections at Low Cost Airports With Gates Reassignment. IEEE Access 2019, 7, 179429 -179444.
AMA StyleHasnain Ali, Yash Guleria, Sameer Alam, Michael Schultz. A Passenger-Centric Model for Reducing Missed Connections at Low Cost Airports With Gates Reassignment. IEEE Access. 2019; 7 (99):179429-179444.
Chicago/Turabian StyleHasnain Ali; Yash Guleria; Sameer Alam; Michael Schultz. 2019. "A Passenger-Centric Model for Reducing Missed Connections at Low Cost Airports With Gates Reassignment." IEEE Access 7, no. 99: 179429-179444.
Runway utilisation is a function of actual yearly runway throughput and annual capacity. The aim of the analysis in this project is to find data driven prediction models based on the features and relevant scenarios that might impact runway utilisation. The Gradient Boosting machine learning method will be assessed on their forecast performance and computational time for predicting the procedural and non-procedural runway exit to be utilised after the landing rollout. The Gradient Boosting method obtained an accuracy of 79% and was used to observe key related precursors of unique data patterns. Tests were conducted using runway and final approach data consisting of 54,679 arrival flights at Vienna airport.
Floris Herrema; Ricky Curran; Sander Hartjes; Mohamed Ellejmi; Steven Bancroft; Michael Schultz. A machine learning model to predict runway exit at Vienna airport. Transportation Research Part E: Logistics and Transportation Review 2019, 131, 329 -342.
AMA StyleFloris Herrema, Ricky Curran, Sander Hartjes, Mohamed Ellejmi, Steven Bancroft, Michael Schultz. A machine learning model to predict runway exit at Vienna airport. Transportation Research Part E: Logistics and Transportation Review. 2019; 131 ():329-342.
Chicago/Turabian StyleFloris Herrema; Ricky Curran; Sander Hartjes; Mohamed Ellejmi; Steven Bancroft; Michael Schultz. 2019. "A machine learning model to predict runway exit at Vienna airport." Transportation Research Part E: Logistics and Transportation Review 131, no. : 329-342.
Fragmentation has been suspected of contributing to inefficiencies in the European Air Traffic Management (ATM) system. Heterogeneities between providers may contain multiple aspects, such as airspace structure, staff rostering, or systems used for flow management. Applying the scientific approach of data envelopment analysis, this article provides a new outlook on the relationship between airspace fragmentation and efficiency in the admittedly complex and highly dynamic environment of European ATM. We show that there are airspaces that might benefit from economies of scale, but that there is a tipping point where diseconomies of scale occur. Subsequently, the current approach of functional airspace blocks might inhere inefficiencies for some air navigation service providers.
Thomas Standfuss; Frank Fichert; Michael Schultz; Petros Stratis. Efficiency losses through fragmentation? Scale effects in European ANS provision. Competition and Regulation in Network Industries 2019, 20, 275 -289.
AMA StyleThomas Standfuss, Frank Fichert, Michael Schultz, Petros Stratis. Efficiency losses through fragmentation? Scale effects in European ANS provision. Competition and Regulation in Network Industries. 2019; 20 (4):275-289.
Chicago/Turabian StyleThomas Standfuss; Frank Fichert; Michael Schultz; Petros Stratis. 2019. "Efficiency losses through fragmentation? Scale effects in European ANS provision." Competition and Regulation in Network Industries 20, no. 4: 275-289.
The future airspace has to provide a reliable infrastructure and operational concept to ensure efficient and safe operations considering both flight-centric operations and the integration of new entrants. We propose an approach for a dynamic sectorization to manage the air traffic demand and flow appropriately. Our dynamic sectorization results in enhancements of the current operational structure (less deviation in controller task load) and leads to a significantly lower controller task load for the newly created airspace. Since future 4D trajectory management demands an efficient consideration of operational (e.g., temporally restricted areas), ecological (e.g., contrail prevention), and economic (e.g., functional airspace blocks) constraints, our dynamic sectorization method contributes to the highly flexible use of current and future airspace. In this paper, we provide an overview of several use cases and describe the working principle of our approach: fuzzy clustering of air traffic, Voronoi diagram for initial structures, and evolutionary algorithms for optimization.
M. Schultz; I. Gerdes; T. Standfuß; A. Temme. Future Airspace Design by Dynamic Sectorization. Lecture Notes in Electrical Engineering 2019, 19 -34.
AMA StyleM. Schultz, I. Gerdes, T. Standfuß, A. Temme. Future Airspace Design by Dynamic Sectorization. Lecture Notes in Electrical Engineering. 2019; ():19-34.
Chicago/Turabian StyleM. Schultz; I. Gerdes; T. Standfuß; A. Temme. 2019. "Future Airspace Design by Dynamic Sectorization." Lecture Notes in Electrical Engineering , no. : 19-34.
Multicriteria trajectory optimisation is expected to increase aviation safety, efficiency and environmental compatibility, although neither the theoretical calculation of such optimised trajectories nor their implementation into today’s already safe and efficient air traffic flow management reaches a satisfying level of fidelity. The calibration of the underlying objective functions leading to the virtually best available solution is complicated and hard to identify, since the participating stakeholders are very competitive. Furthermore, operational uncertainties hamper the robust identification of an optimised trajectory. These uncertainties may arise from severe weather conditions or operational changes in the airport management. In this study, the impact of multicriteria optimised free route trajectories on the air traffic flow management is analysed and compared with a validated reference scenario which consists of real flown trajectories during a peak hour of Europe’s complete air traffic in the upper airspace. Therefore, the TOolchain for Multicriteria Aircraft Trajectory Optimisation (TOMATO) is used for both the multicriteria optimisation of txrajectories and the calculation of the reference scenario. First, this paper gives evidence for the validity of the simulation environment TOMATO, by comparison of the integrated reference results with those of the commercial fast-time air traffic optimiser (AirTOp). Second, TOMATO is used for the multicriteria trajectory optimisation, the assessment of the trajectories and the calculation of their integrated impact on the air traffic flow management, which in turn is compared with the reference scenario. Thereby, significant differences between the reference scenario and the optimised scenario can be identified, especially considering the taskload due to frequent altitude changes and rescinded constraints given by waypoints in the reference scenario. The latter and the strong impact of wind direction and wind speed cause wide differences in the patterns of the lateral trajectories in the airspace with significant influence on the airspace capacity and controller’s taskload. With this study, the possibility of a successful 4D free route implementation into Europe’s upper airspace is proven even over central Europe during peak hours, when capacity constraints are already reaching their limits.
J. Rosenow; H. Fricke; T. Luchkova; M. Schultz. Impact of optimised trajectories on air traffic flow management. The Aeronautical Journal 2019, 123, 157 -173.
AMA StyleJ. Rosenow, H. Fricke, T. Luchkova, M. Schultz. Impact of optimised trajectories on air traffic flow management. The Aeronautical Journal. 2019; 123 (1260):157-173.
Chicago/Turabian StyleJ. Rosenow; H. Fricke; T. Luchkova; M. Schultz. 2019. "Impact of optimised trajectories on air traffic flow management." The Aeronautical Journal 123, no. 1260: 157-173.
Reliable and predictable ground operations are essential for punctual air traffic movements. Uncertainties in the airborne phase have significantly less impact on flight punctuality than deviations in aircraft ground operations. The ground trajectory of an aircraft primarily consists of the handling processes at the stand, defined as the aircraft turnaround, which are mainly controlled by operational experts. Only the aircraft boarding, which is on the critical path of the turnaround, is driven by the passengers’ experience and willingness or ability to follow the proposed procedures. We used a recurrent neural network approach to predict the progress of a running boarding event. In particular, we implemented and trained the Long Short-Term Memory model. Since no operational data of the specific passenger behavior is available, we used a reliable, validated boarding simulation environment to provide data about the aircraft boarding events. First predictions show that uni-variate input (seat load progress) produces insufficient results, so we consider expected passenger interactions in the aircraft cabin as well. These interactions are aggregated to a prior-developed complexity metric and allow an efficient evaluation of the current boarding progress. With this multi-variate input, our Long Short-Term Memory model achieves appropriate prediction results for the boarding progress.
Michael Schultz; Stefan Reitmann. Machine learning approach to predict aircraft boarding. Transportation Research Part C: Emerging Technologies 2018, 98, 391 -408.
AMA StyleMichael Schultz, Stefan Reitmann. Machine learning approach to predict aircraft boarding. Transportation Research Part C: Emerging Technologies. 2018; 98 ():391-408.
Chicago/Turabian StyleMichael Schultz; Stefan Reitmann. 2018. "Machine learning approach to predict aircraft boarding." Transportation Research Part C: Emerging Technologies 98, no. : 391-408.
This study successfully implements flight specific delay costs in an air traffic simulation with a multi-criteria trajectory optimization and exemplifies a coupling of turnaround and trajectory optimization of historical real flights. Therein, delay costs and detour costs for the reduction of contrail formation are individually calculated for each flight and considered in a flight specific multi-criteria trajectory optimization with the air traffic simulation environment TOMATO. Detours in the optimized trajectories are mainly caused by the intent of avoiding contrail formation. With this case study, the historical flight plan could be stretched and departures and arrivals could be more homogeneously distributed during the analyzed three hours while at the same time ecological costs could be saved by 15 per cent. Therewith the promising potential of System Wide Information Management between airports, airlines, air traffic control and customers could once again be shown.
Judith Rosenow; Michael Schultz. COUPLING OF TURNAROUND AND TRAJECTORY OPTIMIZATION BASED ON DELAY COST. 2018 Winter Simulation Conference (WSC) 2018, 2273 -2284.
AMA StyleJudith Rosenow, Michael Schultz. COUPLING OF TURNAROUND AND TRAJECTORY OPTIMIZATION BASED ON DELAY COST. 2018 Winter Simulation Conference (WSC). 2018; ():2273-2284.
Chicago/Turabian StyleJudith Rosenow; Michael Schultz. 2018. "COUPLING OF TURNAROUND AND TRAJECTORY OPTIMIZATION BASED ON DELAY COST." 2018 Winter Simulation Conference (WSC) , no. : 2273-2284.
We provide an overview about the research done in the field of airport and airline operations with a specific focus on a fast, reliable and sustainable passenger boarding. The reliable prediction operational processes along the aircraft air-ground trajectory demands a comprehensive consideration of economic, environmental, and handling constraints of airlines and airports. In particular, the critical process of passenger boarding is driven by passengers’ ability to follow the proposed boarding procedures and is not controlled by operational experts. In this paper we implement and compare two individual-based approaches which cover both specific passenger behavior during boarding and operational airline constraints. Both models used similar input values, but exhibit different magnitudes in the benefit evaluation. Furthermore, we demonstrate that there are still unused potentials to further improve boarding progress by using innovative infrastructural adaptations inside the aircraft cabin.
Michael Schultz; Michael Schmidt. Advancements in Passenger Processes at Airports from Aircraft Perspective. Sustainability 2018, 10, 3877 .
AMA StyleMichael Schultz, Michael Schmidt. Advancements in Passenger Processes at Airports from Aircraft Perspective. Sustainability. 2018; 10 (11):3877.
Chicago/Turabian StyleMichael Schultz; Michael Schmidt. 2018. "Advancements in Passenger Processes at Airports from Aircraft Perspective." Sustainability 10, no. 11: 3877.
Weather events have a significant impact on airport performance and cause delayed operations if the airport capacity is constrained. We provide quantification of the individual airport performance with regards to an aggregated weather-performance metric. Specific weather phenomena are categorized by the air traffic management airport performance weather algorithm, which aims to quantify weather conditions at airports based on aviation routine meteorological reports. Our results are computed from a data set of 20.5 million European flights of 2013 and local weather data. A methodology is presented to evaluate the impact of weather events on the airport performance and to select the appropriate threshold for significant weather conditions. To provide an efficient method to capture the impact of weather, we modelled departing and arrival delays with probability distributions, which depend on airport size and meteorological impacts. These derived airport performance scores could be used in comprehensive air traffic network simulations to evaluate the network impact caused by weather induced local performance deterioration.
Michael Schultz; Sandro Lorenz; Reinhard Schmitz; Luis Delgado. Weather Impact on Airport Performance. Aerospace 2018, 5, 109 .
AMA StyleMichael Schultz, Sandro Lorenz, Reinhard Schmitz, Luis Delgado. Weather Impact on Airport Performance. Aerospace. 2018; 5 (4):109.
Chicago/Turabian StyleMichael Schultz; Sandro Lorenz; Reinhard Schmitz; Luis Delgado. 2018. "Weather Impact on Airport Performance." Aerospace 5, no. 4: 109.