Collaborative Decision Making: Results of Experiments to Identify Limitations of Information Exchanges in Stand and Gate Operations.

Extended Abstract for ATM-2001

Peter Martin, EUROCONTROL Experimental Centre, Brétigny-sur-Orge, France Olivier Delain, representing EUROCONTROL Experimental Centre Fadi Fakhoury, representing EUROCONTROL Experimental Centre

technology – instead effort must be focussed Summary on resolving the organisational and procedural issues, in particular removal of the What are the benefits of exchanging disincentives to communication through badly information to build a CDM environment? or thoughtlessly designed processes.

The A-CDM-D project was set up in 1999 to Collaborative Decision Making investigate development of a CDM system. A key part of the project was the evaluation of Based on previous CDM studies, [Ref.1] the operational benefit of information identifies different “levels of collaboration” exchange to airlines and other partners in the among the potential applications of CDM. The ATM system. project focussed on the two first levels of collaboration through: To achieve this EUROCONTROL organised a data collection process in which several airline • Data Sharing, collecting data from and airport partners provided data in an “open automatic sources (users’ operational book” approach, focusing on stand and gate environments) and manual sources (users’ operations. This enabled a detailed analysis inputs). This built up a database of of who had what information when, and what multiple sources of data. was the quality of the information. The goal of this analysis was to decide if there would be a • Traffic Prediction, which aggregated all benefit in information sharing and hence from collected data that relate to a given flight, Collaborative Decision Making. in order to supply a global view of the traffic at all times. Tools enabled us to The data allowed the team to identify some look at the partial views of the traffic specific weaknesses concerning gate and stand available to each user or a complete gate- management, with the consequence that to-gate view of the traffic. resources are used sub-optimally and this provides scope for additional aircraft The Experiments turnarounds. Data collection was carried out for two periods The analysis demonstrated that while much of of four weeks in October-November 2000. the required data is available, it is often not The experiment configuration was as shown in exchanged. The reasons are primarily cultural Figure 1. and commercial. We concluded that the challenge for CDM is not to provide new systems involving considerable expenditure in Site actors AutomaticAutomatic sources sources Brussels Site actors

Sabena / BIAC Brussels ATC BA CDB OCC BIAC S&G CANAC Station manager FICO Station manager BA’s MVT Brussels TWR A-CDM-D actor 4 A-CDM-D actor 2 A-CDM-D actor 3 Swissair’s MVT BA T1 Center OCC BIAC S&G

ZurichZurich Site Site actors actors LondonLondon Site Site actors actors

Swissair / Sabena BA Sabena CFMUCFMU OCC OCC Station manager

Station manager Station manager A-CDM-D actor 1 SR S&G EEC BA S&G A-CDM-D actor 3 A-CDM-D actor 5 A-CDM-D actor 4 BIAC S&G Swissair OCC BA T1 Center

Figure 1: Experiment Organisation • Update of the stand and gate allocation plan: the update of the plan based on the Data was collected by electronic links from stand and gate allocation. This action Brussels, Sabena, Swissair, could have an impact on the Pre-tactical and CFMU systems. level.

Data was collected manually by having staff Gates can be analysed as a flow constraint. monitoring events in the AOC and airport There are a limited number of gates available operations rooms. for each airline that makes the gates a scarce resource. Also, there is a connection between The data was collated in the Traffic Prediction gates and ramp/taxiways. Sometimes aircraft database at the end of the runs and analysed to have to wait for each other when they pushback understand operations around gates and stands into the same alley or taxiway. Conversely, at Brussels (managed by BIAC) and when an arriving aircraft finds its gate Heathrow Terminal 1 (managed by British occupied, it must wait on the taxiway leading Airways). to the gate or into the alley.

Information for Stand & Gate Management Ramps and taxiways provide a system of queues that lead aircraft departures from the At least three generic functions may be gates to the runways. The taxi-out time, that is identified in the “Stand and gate management” the time each departure spends between function: pushback and takeoff, can be considered as the time each departure spends in the queuing • Stand & Gate planning, ensuring the system. There is a strong correlation between development of the stand & gate plan, the taxi-out time and the number of departures. performed at strategic level (seasonal level), pre-tactical level (month level, Assessment of discrete information week level) and tactical level (day of operations level). Typically a stand and gate unit issues and receives information from four organisations as • Stand & Gate allocation: the allocation shown in Figure 2. during the day of operation (e.g. day of operations level) of a stand and a gate to a given flight. Figure 2: Stand & Gate Management Main Information Flows

Handlers and servicing companies

- Handling data (fuel, Stand and gate at catering …) ADEP and ADES - Meteorological data -Paxdata Stand and gate at - Airport and ADEP and ADES Pier data Stand & Gate management Airport Stand & Gate management ATC at ADEP unit at arrival and departure departments unit at arrival and departure and ADES airportairport - ETOT/ATOT Stand and gate at - EOBT/AOBT ADEP and ADES - ETA/ATA - STOT/ETOT/ATOT - EIBT/AIBT - EOBT/AOBT Stand and gate at - STA/ETA ADEP and ADES - EIBT/AIBT - Aircraft data

Airline’s Operations Control or Terminal Centre

usefulness and to highlight new types of The main decisions taken by a stand and gate information that should be shared between unit during tactical phase address the actors. allocation, de-allocation and re-allocation of the stands and gates. Complexity, uncertainty • Timeliness: To assess whether information and multiple constraints characterise their is distributed in sufficient time for environment. effective use in operations planning. Associated metrics characterise the Decisions are made with the objectives of advance notification time of information. optimising airport capacity (use of available capacity through maximisation of resource use, • Predictability: To evaluate whether throughput, etc.). These are based on aggregation of information might improve information (e.g. arrival and departure time the predictability of information. estimates) that changes over time or have a poor timeliness/accuracy. Such data are Results at London T1 Stand and Gate received from several sources (e.g. airlines, handlers, local ATC, etc.) and constrained by Inbound London Heathrow flights several variables: operating airline’s schedule, aircraft type, destination country, passenger The data sample was partitioned into BA and information, terminal and piers capacities…) non-BA flights. BA is responsible for managing stands and gates for all airlines using Analysis Approach T1, and the operators have much better information about the arrival of BA flights The experiment was structured to analyse the through direct access to the company’s own data gathered in terms of several key information systems. parameters: As a result the main weakness concerned • Completeness: To determine if the information about non-BA flights in-bound. At different actors receive all the information present, for these flights S&G planners only that they could or should have been sent. receive estimates of in-block times (EIBT):

• Accuracy and reliability: To examine if • When the aircraft enters/leaves the arrivals information exchanged is correct and stack (from ATC), useful for actors. The fundamental • When the aircraft is in final approach question is to determine whether new (from ATC), CDM information is an improvement over existing sources, to assess its operational • If the flight is running very late, an update Availability of Additional Data Sources to of the in-block time may be received (from Stand and Gate Managers the company). For Brussels to London flights, the airport

First EIBT issued First ETD issued First ETA received by BA for BA by CDB for non Non regulated flights by BA for non-BA flights BA-flights flights 2h31 hours 50 minutes 31 minutes before STA before STA before STA 9 minutes 3 minutes 6 minutes accuracy accuracy accuracy

database system Central Data Base (CDB) in Figure 3: Accuracy of Estimates Brussels computes and updates different values (Heathrow inbound flights) – non- of Estimated Off Block Time (EOBT), regulated flights, normal days reflecting constraints (such as CFMU slots) or potential internal disruptions.

First ETD issued First EIBT Last slot issued First ETA received by CDB for non- issued by BA by the CFMU for Regulated flights by BA for non-BA BA flights for BA flights non BA flights flights 2h30 hours 2h29 hours 2h07 hours 19 minutes before STA before STA before STA before STA 11 minutes 9 minutes 20 minutes 9 minutes accuracy accuracy accuracy z accuracy

However, the Brussels CDB computes an Figure 4: Accuracy of Estimates estimation of the departure time from Brussels (Heathrow inbound flights) – regulated on average 2h44 minutes before scheduled flights, normal days time of arrival and with a good accuracy of 9 minutes. The results are shown in Figures 3 and 4. For non-BA flights during normal days, S&G For flow regulated flights (i.e. not the complete planners at T1 receive the first EIBT for non- sample of Brussels-London flights) the CFMU BA flights on average 31 minutes (unregulated issues the last slot on average 2h08 minutes flights) or 19 minutes (flow regulated flights) before scheduled time of arrival. The resulting before scheduled time of arrival. The Computed Time of Arrival (CTA) has an corresponding averages for data accuracy accuracy of 15 minutes. ranges are 6 and 9 minutes respectively. Thus, whereas the data is quite accurate, the Thus in principle both of these sources could timeliness is poor, and is available too late provide an earlier estimate of arrival time for much reactivity, flexibility and (ETA) for optimisation of stand and gate optimisation of stand and gate allocations. allocation at T1.

In comparison for BA flights, the T1 operators However, as EOBT is by nature an estimate, have available (from their own systems) a first the corresponding confidence or reliability of EIBT on average 2h30 minutes (2h31 minutes such data can be questioned since several for normal flights and 2h29 minutes for updates could be distributed before reaching regulated flights) before scheduled time of the actual value (AOBT). Discussion with arrival, with an average accuracy of 9 minutes. resource managers revealed that they would Potentially this allows a greater planning and prefer to rely on either AOBT or ATOT to optimisation of allocations. predict a reliable EIBT, thereby limiting the horizon of the advance warning, and placing an upper limit on the look-ahead in the allocation plan. In practise taxi time variability means that realistically only the ATOT at the messages). We see from the experiments that departure airport really provides a sufficient this is rarely the case. and reliable indication for computing the EIBT, particularly for short haul flights. Such Accuracy/Reliability an improvement would require distribution of data to destination airports as soon as the The evolution of accuracy of estimates aircraft takes off (e.g. through movement available concerning the different populations of flights was tracked during the experiments, as shown in Figures 5 and 6.

Normal days, Non regulated flights

0:14

0:12

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0:08

0:07

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0:00 STA-3 STA-2 1/2 STA -2 STA-1 1/2 STA-1 STA-1/2 STA STA+1/2 STA+1

EIBT issued by BA EIBT as received by BA EOBT as received by the CDB

Figure 5: Accuracy of inbound LHR flights – Normal days, Non-regulated flights (minutes)

Normal days, Regulated flights

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0:00 STA-3 STA-2 1/2 STA -2 STA-1 1/2 STA-1 STA-1/2 STA STA+1/2 STA+1

EIBT issued by BA EIBT as received by BA EOBT as received by the CDB CTA as issued by the CFMU

Figure 6: Accuracy of inbound LHR flights – Normal days, Regulated flights (minutes) The accuracy of the in-block time estimates It was observed that the EIBT accuracy issued by BA for its flights is constant until measured for non-BA flights as received by the STA-1½ hours where improvements can be stand and gate unit is constant until STA-1 realised (e.g. after the landing of the previous hour (average accuracy: 13 minutes). This flight at the outstation). Between STA-1½ hour accuracy improves rapidly between STA-1/2 and STA, the average accuracy improves hour (average accuracy 12 min) and STA+1/2 rapidly from 10 minutes down to 1 minute. At hours (average accuracy 1 min) once the flights STA-1 hour (the scheduled time of take-off) are in radar coverage. the accuracy of BA’s own flight data is 5 minutes. The accuracy of the off-block time estimates received and issued by the One example is shown in Figure 7 (next page). CDB follows the same scheme as BA own This example shows in detail the information flight data, but for all flights. This again distributed for a single flight between Zurich demonstrates the benefits of distributing and Brussels. Blue lines indicate estimates estimates by the departure to the arrival airport available to stand and gate whereas red lines systems. indicate estimates seen in other systems. The flight was expected in block at Brussels airport Similar evolution of accuracy of estimates can at 1115, and hence a stand and gate allocation be seen for flow regulated flights (see Figure was made for the flight concerned. The stand 6), with low accuracy of EIBT values for non- and gate managers eventually received an BA flights until STA-1/2hr but much better update 1215. Until that time the stand was EIBT for BA own flights. blocked for the aircraft concerned. At that point, an update was received indicating For flow regulated flights the CFMU can also AOBT and ATOT, 33 minutes before ATA. provide a CTA. However, this data is not However, as shown on the figure, a series of currently updated after slot issue, so it is of more accurate estimates were distributed by the little use as an aid to improving EIBTs at T1. airline to the CFMU at 0800, 0832 and 1121. If these had been available to the stand and In conclusion, there are several means to gate management system, the staff there could improve the accuracy of time estimates have reacted sooner. available to T1 stand and gate managers for inbound non-BA flights. The most likely improvement would be by extrapolation of The consequence of the absence of information the actual take-off time of the aircraft from exchange was that the stand and gate allocated outstation (which occurs at approximately earlier to the flight were not used for STA-1 hour). This means that for all practical approximately 45 minutes. Grossing up this purposes the best strategy for improving consequence, the airport could (ignoring information distribution is to: other constraints such as limitations) perform several additional a) distribute indications and warnings of rotations daily with resulting extra landing delays fees and revenues from passengers. b) ensure widespread distribution of actuals (i.e. ATOT,…)

Use of Arrival Time Estimates: Brussels Review of Results and Conclusions Stand and Gate Operations Observed Weaknesses The poor quality of information available to the destination airport staff concerning non- The experiments demonstrated that there is a home based carriers was also observed at the clear difficulty in providing others with other airports we studied, i.e. Brussels and accurate, reliable and timely information, and Zurich. In general the home bases’ ATC hence there is a need for CDM. Weaknesses authority provides the earliest reliable detected concerned both distribution of information, which typically extends to just estimates and decisions by the actors (e.g. outside the local FIR at the first point of radar which aircraft is allocated to which flight). correlation. Figure 7: Stand & Gate Operation at Brussels Value of Estimate 13:11 13:03 ATA 13:02

ETA CFMU ATA= 13:03 Updates

12:08 Decision Making

11:51 Update of Stand / Until 12:15 Gate at 12:30 Airport has no update of ETA

11:15 (STA)

12:30 Time Progression

08:00 08:32 10:23 11:21 12:40 12:59 13:01 13:03 (CFMU) (CFMU) (CFMU) (CFMU) (S/G) (S/G) (CDB) (CDB)

may be of higher quality (e.g. in terms of accuracy against timeliness aspects) but would Completeness be difficult to implement since different competing objectives may lead to In terms of completeness, most of the required inconsistencies. information already exists in existing systems: the issue is to fill information gaps by assuring The Decisions of Individual Actors are Not fuller distribution of this information. Such Known improvements rely on the will of actors to distribute data that are not available to others Of particular note is the way decisions are not and highlight the importance of proprietary distributed. For example, ATC intentions issues linked to information distribution. regarding reduced use of runways or taxiways are not widely published. Early information Quality of Information could be used to enable Aircraft Operators to establish priorities for departure. Significant discrepancies in quality of information (including completeness of Similarly Aircraft Operators decisions information, accuracy, reliability and regarding aircraft changes, connections waiting timeliness) were noted. Improving the quality for a feeder aircraft, etc. are not generally of information is the main challenge for all made available to Parking Control. Such data actors. It is felt that timeliness aspect is would enable it to plan its operations with probably the key aspect for enhancing the efficiency. overall process. Identification of Challenges The benefit of merging different sources was not measured, as the existence of different and It is easy to identify a global objective such as proprietary estimates at a given time reflect to increase the overall efficiency of the system. different plans. Thus reconciliation of To realise such improvements it is important to competing plans seems more promising consider how each actor’s environment compared to pure and simple aggregation of (composed of objectives, constraints, data. A strategy would be to establish clear individual strategy, behaviour, priorities to responsibilities of each actor for providing, access to scarce resources etc.) impacts the updating and sharing data. others. It is necessary to recognise the difference of objectives. Individually these can However, our analyses have found that the be translated in specific “efficiencies” such as aggregation of information in disruption cases performance, costs and return on investments depending on the actor considered. These experiments have demonstrated that a lot One important aspect that has been noted of information required to improve the during experiments was to understand the processes already exists. It is not distributed actors’ behaviour regarding the system. While because there is insufficient reason to do so, specific actors are provided with inaccurate perhaps for simple lack of a clear motivation, and unreliable information and complain about for operational or economic reasons, or even this, the reason is that the provision of accurate because there are clear disincentives in terms data by the owners may lead to detrimental of penalties suffered. Our near term work on effects. Thus, the challenge for CDM is to find CDM will therefore concentrate on local a way to remove such disincentives through airport studies to examine what can be done to agreed procedures. remove these barriers to effective communication, and at all times focus on the Future CDM Activity associated return on investment.

CDM is a process that already occurs widely in many parts of the ATM system: for example Acknowledgements locally at airports, or between CFMU and airlines. At the present time these processes The authors would like to thank the are less efficient than is possible because of participating airports and airlines for their limitations in the exchange of information on support in this study, and other members of the estimates and decisions by the actors. ACDMD project consortium responsible for the Demonstrator development.

Acronyms

ACDMD Air Collaborative Decision Making Demonstrator AIBT Actual In-Block Time ATA Actual Time of Arrival ATOT Actual Take-Off Time BA British Airways BIAC Brussels Company CDB Central Data Base (at Brussels Airport) CDM Collaborative Decision Making CTA Calculated Time of Arrival (by the CFMU) EIBT Estimated In-Block Time ETOT Estimated Take-Off Time S&G Stand and Gate STA Scheduled Time of Arrival T1 London Heathrow Terminal One

References

[1] Potential Applications of CDM. EEC Report 19/1998. Martin et al.

Biographical Note

Peter Martin is a project manager, responsible for the EEC activity on Collaborative Decision Making since its inception. Olivier Delain is an ATM analyst concerned with a variety of R&D projects, particularly in the flow management domain. Fadi Fakhoury is a consultant experienced in the airline and transport industries.