IHM Aéronautiques de l'ÉNAC

Applied reseach in Aeronautical Human-Computer Interaction

MoTa Project Summary

Rédigé par railane - -

Project Modern Taxiing (MoTa) studies the impact of future taxiing technologies such as Datalink and autonomous taxiing tugs on airport taxiing operations and air traffic controller workload.

Aircraft traffic is increasing not only in the air but also on the ground at airports that already are close to saturation. As a consequence, collision risk, time delays, pollution, and stress for the air traffic control officer (ATCO) are rising. However, new automated techniques are being developed, aiming at saving fuel during the ground taxiing phase. Although the environmental benefit would be interesting on its own, technologies such as the TaxiBot© system may also increase the number of ground movements, or the throughput. Project MoTa deals with providing ground ATCOs a tool that will help with managing increased traffic and taking advantage of modern aircraft taxiing techniques when available. The tool consists of an integrated ground control interface featuring the latest progress in modern taxiing methods and multi-agent algorithms for enhanced ground automation while still supporting current and conventional ground control procedures during the transition period. In addition to the new integrated ground control interface, autonomous taxiing tugs (inspired by the TaxiBot system) were simulated. The concept is to use the tugs to continue towing the aircraft after pushback, along the taxiways until the runway holding point, thus saving fuel since aircraft engines would be started later in the taxiing sequence. In that manner, a departure aircraft would be handled as usual by ground control, but when the tug is detached from the aircraft after depositing it at the runway, the empty tug would return to the parking areas via the same taxiways as the rest of the traffic. It was assumed that no other infrastructure would be built to support the tugs. As the taxiing tug is still a concept and deployed at only a few airports, different assumptions had to be made on the future operational procedures. Since one objective of the project is to ensure that proposed solutions are robust and compatible with the ATCO's workload, the most constraining assumptions were retained.

The Ground Control Working Position

The interface is based on the AVISO (the ground radar image currently in use at Paris Charles De Gaulle (CDG)) but it was enriched to include information from the paper flight strips that are still used in France, thus capable of replacing the paper strips entirely. Together, these two technologies provide the minimum information required to manage today’s ground taxiing operations.

Illustration 1: MoTa ground controller interface prototype, as in use for the South ground sector at CDG.

As seen in previous illustration, flight information is displayed on the aircraft label and in a flight list in a concealable side panel. The standard path suggestion for an aircraft can be retrieved by selecting its icon or label (i.e. a stylus touch). As seen in the Figure 1 inset, ACA1609 is departing on runway 26R and the ATCO can validate the suggested path (marked in yellow) by clicking on one of the 3 holding points to the runway (represented by the large green zones on the runway threshold).

The aircraft context menu can also be opened by clicking on the label in addition to the icon. This helps in selecting the correct aircraft in dense traffic. The ATCO can manage the frequency status by assuming or transferring the vehicle, to inputting a non-standard route using the “Automatic [path completion]” or “Manual [path completion]” options, or using path input shortcuts such as “Follow [another aircraft]” which is quicker than inputting the same route again.

Illustration 2: standard route modification using waypoints.
Illustration 3: conflicts representation.
Illustration 4: warning representation.

A non-standard route can be defined by adding waypoints on the path. In Illustration 2, a point has been added to force the aircraft to avoid AF626BV which is stopped on the taxiway. The difference between the automatic and manual modes is the completion of the route. The automatic mode will complete the suggestion to the final destination whereas the manual mode stops the route on the last added waypoint, hence allowing definition of partial routes that stop at any point along the taxiway.

Illustration 3 and 4 shows the conflict and warning visualizations. In Illustration 3, two aircraft are highlighted because of a potential crossover. AF626BV has been instructed to turn right while ACA1609 is going straight ahead and neither of them has been told to give way to the other. In Illustration4, TAY401Z is circled in red to alert the ATCO that it has stopped for more than 10 seconds. The ATCO must determine if the aircraft has a technical problem, is momentarily paused, or requires transfer to the next sector

Evolutions

Illustration 5: partial and conditional clearances (stop, yield, follow).
Illustration 6: Runway holding points load estimation.
Illustration 7: Precise runway configuration change insertion.

Multi-Agent System modelling of the platform

The developed Multi Agent System optimizes aircraft ground trajectories in a decentralized manner and also manages autonomous tugs movements. Taxiways and vehicles (autonomous, service, and aircraft) are represented in this environment as agents. A taxiway agent manages resource usage (whether it is employed or not by another vehicle) and maintains a schedule of future aircraft passages. Vehicle agents asynchronously explore (i.e., independently of the others) and express their intentions with respect to resource usage by communicating with the taxiways every second. These vehicles schedule their usage of the taxiways as needed.

Three experiments were conducted to validate two different levels of this technology across two different types of scenarios. The Modern Taxiing platform was designed based on Roissy Charles-de-Gaulle airport in Paris, France, and was simulated at the Ecole Nationale de l’Aviation Civile with 18 air traffic controller instructors with experience from airports around Europe. Four of the 18 participated in all three experiments, whereas 2 out of the 18 participated in just two experiments. The other twelve participated in one experiment. The first experiment was conducted in the fall of 2014 and the second and third experiments were conducted in the fall of 2015.

Each participant performed two 35-minute ground taxiing scenarios, Medium and Hard, which were both simulated in each of the three experiments. Both scenarios varied by the number of aircraft and different operational events (restricted zone, pilot errors, closed taxiway, change in configuration, towed aircraft). In experiments 2 and 3, a percentage of the aircraft fleet was equipped with data link, and in experiment 3, a fleet of autonomous taxiing tractors based on the tug system was introduced. The first experiment provided a reference point with respect to performance with current technology. The second experiment evaluated the use of the interface alone (interface includes the tactile screen, the path suggestion, and the decision support system unless otherwise noted). The third experiment evaluated the inclusion of the tugs in addition to the technology of the second experiment. The second experiment represents technology that could be used in the near future, whereas the technology of the third experiment is farther along the line.

Results

Operational results

The results of the entire experiment campaign show that the Modern Taxiing platform can increase the overall performance of ground taxiing, with greater throughput and less time in the ground sector. The use of the tugs appears to reduce the technology gains, with the greatest performance occurring when using only the interface, without the tugs. However, the advantages due to technology also come at a price, with an increase in perceived workload although the physiological response does not significantly vary. The technology is still currently too immature for accepted use by the air traffic controllers, but comments made during debriefing suggest that with improvements, the participants would be accepting of this new technology in an operational context. The technology also appears to assist participants during some operational events, namely, in managing the impact of a towed aircraft, a change in configuration, and a pilot error.

Globally, the current MoTa platform performs well in nominal conditions but is less robust to off-nominal behaviour (e.g. misplacement of hands, stylus, or misclicks; major trajectory modifications). Participants struggled with modifying trajectories due to ergonomic problems or path suggestion algorithm inconsistencies. This problem was particularly compounded when a change in configuration was planned, as suggested paths could not be varied with different configurations for each aircraft. Additionally, participants commented on a mistrust of the tugs, particularly when they entered the ground sector autonomously. Inappropriate or unimportant alerts were raised due to the tugs, which added to the decreased sense of awareness regarding the situation and possibly increased workload (division of attention, information decluttering). Nevertheless, no participant completely distrusted the automation (lowest score was 2.33/6) and several participants (both from CDG and not) said that with this interface they would use the data link system if available at their home airport. Additionally, experience with the interface improves acceptability. Several participants stated that they were more at ease with the platform towards the end of their sessions than at the beginning.

However, there are some limitations to this study. The run order may be confounded with the experiment run order and the gains in performance and self-reported workload may be due to learning effects and not the technology. The change in configuration due to the change in winds was simulated differently between Experiments 1 and 2, 3, with less rerouting of aircraft towards the end of their original trajectories in the latter two experiments. This change may have contributed to improved performance in the hard scenario. The small sample size, especially of controllers who have experience with CDG, limits the conclusions that can be drawn from this study.

The ATCOs interviews during the simulation sessions provided different enhancement suggestions. Some of them were actually implemented, such as an easier way to input a partial clearance that would replace the full manual mode which was identified as a weakness, some pie menu and a/c labels design upgrades and an interaction to set precisely after which departing a/c the runway configuration change would actually occur. Other enhancement propositions were of interest but would need more time to be implemented, for example the possibility to define magnifier windows to monitor more closely specific congested areas or the representation of a workflow for each aircraft (i.e. next action for the ATCO on an aircraft with a time estimate).

Environnmental impact

Current results indicate that the taxi times and fuel consumption are reduced with the implementation of the MoTa interface and the TaxiBot-like tugs, especially with dense traffic scenarios (-17% with the interface, -21% with the interface and the tugs).

It shall be noted that the present analysis was conducted based on a simulation of one part of CDG. It would be advised to conduct additional studies on a whole airport to confirm this trend.

CO2 emissions are directly derived from fuel consumption reductions. As an order of magnitude, it is considered that every kg of fuel burnt corresponds to an emission of 3.15 kg of CO2.

Conclusion

Key indicators

Exp 1 (radar image + paper strips) Exp 2 (tactile interface) Exp 3 (interface + autonomous tugs)
Percentage of Aircraft Correctly Treated 81,75% (~55 ac/h) 94% (~65 ac/h) 87,5% (~59 ac/h)
Deviation from Ideal Trajectory (in minutes) 1,97 0,7 1,52
Normalized Taxi Time 1,97 1,31 1,60
Number of Stop and Go 1,01 0,43 0,45
Workload (TLX score) 4,59 3,33 4,67
Average Fuel Consumption per movement (in kg) 138 127 (-8%) 120 (-13%)

The new ground control working position noticeably increases the platform throughput, and the taxiing time are a bit lower. In addition, the number of stop and gos is also reduced which can have an impact on the fuel consumption.

The ATCO workload is improved when introducing the tactile interface, although the monitoring of autonomous tugs raises it again.

On average 68 % of the aircraft were given a route electronically during the exercises (maximum is 90 % in medium scenario and 85% in hard). It has to be noted that it was not an objective given to the ATCOs, they were asked to manage the traffic as a priority and using the tool as much as they could.

Route input time is 6.75 seconds on average. This may include an undetermined decision making time during the input process.

Future work

Future work should consider improving on the design of the MoTa platform, both in terms of information representation and algorithm definition. Potential avenues of research could be determining the maximum performance achievable with the MoTa platform, coordinating several actors of the airport (either all the same platform or with slight variations), integrating an Arrival/Departure manager, and incorporating in real-time human input to improve suggested solutions over time with respect to ATCO preferences.

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