Objective and Automatic Estimation of Excess Taxi-Times

Transcription

1 Objective and Automatic Estimation of Excess Taxi-Times Benjamin S. Levy, Ph.D., Sensis Corporation, Syracuse, New York Jeffrey E. Legge, Sensis Corporation, Syracuse, New York Abstract This paper describes the methodology developed at Sensis Corporation for the automatic and objective estimation of total and excess taxi-times from Airport Surface Detection Equipment Model X (ASDE-X) surveillance data, such that these quantities can be conditioned on the basis of runway and gate/ramp locations. For each airport in the daily summary, we report the number of arrival and departure operations, total taxi-time, excess taxi-time, percent of known aircraft types, and the percent of complete aircraft taxi trajectories. Other data columns in the daily summary include fuel burn, fuel cost, and emissions (i.e., HC, CO, NO x ), reported as total and excess quantities. A daily report is automatically generated for the airports at which Sensis Corporation currently makes recordings: ATL, BDL, CLT, DTW, IAD, MCO, MEM, MKE, ORD, PVD, SDF, SEA, and STL; this list will grow as more ASDE-X systems are fielded. Estimation of excess fuel burn and cost requires data on the aircraft type and excess taxi-time. The aircraft type determines the fuel burn rate, taken from the ICAO database; the excess taxitime depends on a complete taxi trajectory in the movement area. The percent of known fuel burn rates ranges from 85 to 94% for the current set of airports. The percent of complete trajectories ranges from 83 to 93% for taxiing in the movement area. For validation, we have undertaken comparison of operation counts from the processing of ASDE-X data with data reported in the FAA s Aviation System Performance Metrics (ASPM) database, and have found good agreement (standard error < 1 operation). Also, we have performed some comparisons of the ASDE-X total-time estimates against the reportable quantities from the on-time performance database of the Department of Transportation (DOT) Bureau of Transportation Statistics (BTS). This analysis is performed on a peraircraft basis by matching the tail numbers and out-off-on-in (OOOI time) events between the two data sets. Introduction As the price of fuel continues to rise, airlines are increasingly seeking innovative ways to reduce fuel consumption. One solution is to reduce excess taxi-time resulting from holds on the airport surface. While it is implausible to eliminate all holding (especially during times of high runway utilization), a detailed analysis of holding on a track-by-track basis allows stakeholders to identify and quantify the excess fuel associated with specific issues (e.g., summaries by airport, runway, carrier, aircraft type, etc.). A track is the time-ordered position data for a flight as it taxis. Algorithms have been developed to automatically identify aircraft holds from ASDE-X and Advanced-Surface Movement, Guidance, and Control System (A-SMGCS) data. These algorithms are utilized in this work as a post-hoc analysis tool; the algorithms may be used, however, in a real-time application or integrated into a forecasting tool. The key to leveraging the ASDE-X and A-SMGCS data is the development of robust algorithms which can efficiently process large quantities of raw surveillance data and then integrate it with other available data sources. The work below is based on January 28 data at thirteen airports (approximately 37, operations). The relevance of this work reaches many parts of the aviation community. At Sensis Corporation, it benefits operations research on taxi-time prediction and forecasting and development of advanced surface movement concepts and models. Externally, this work is

2 of interest to airlines (excess fuel costs), the environmental compliance realm (e.g., Emissions and Dispersion Modeling System - EDMS, station air quality staff), the NextGen planning process (e.g., priority ranking of Operational Initiatives, airport-specific efficiency baseline), and the engineers charged with performing cost-benefit studies for airport expansion and construction. Methodology We describe how the recorded surveillance data files are processed to produce the daily estimates of average total and excess taxi-times, as well as dependent quantities such as excess fuel burn and excess emissions. These quantities can be presented at different levels: Per-airport Per-carrier Per-aircraft At the airport or carrier level, the data may be further categorized by dependence of taxi-time on operation (i.e., arrival, departure) and on runway-gate/ramp combination. This section also presents general descriptions of the algorithms used to estimate: OOOI events Excess taxi-time as measured by holding Fuel burn rate as determined by aircraft type Overview of Data Processing and Preparation ASDE-X surveillance data, presently collected at 13 airports, are transferred to Sensis Corporation for processing and storage. The quantity of raw data varies by airport, ranging from one to six gigabytes (compressed) per day. These data, typically stored in half-hour files, fuse input from different sensors. Specifically, the resultant data are the product of output from the multi-dependent surveillance (MDS), surface movement radar (SMR), and terminal area radar (e.g., ASR-9). Figure 1 shows a general flow chart of the steps through which the data are processed and by which the estimates are made. Processing begins by uncompressing the data and extracting relevant fields (position, velocity, identity) from the binary records. In some cases, the ASDE-X fusion subsystem discards data that are relevant to post-analyses; these data are re-associated with the relevant track. Tracks are also joined across the half-hour files. Figure 1. Data Process Flowchart The tracks are processed to improve the data quality (Figure 1, track clean-up). Improved estimates are made for the velocity and altitude fields. These calculations take advantage of the non-real time nature of the processing. Static values such as Mode-S ID, Mode-3A ID, callsign, aircraft type, and transponder type are summarized. Complete and clean tracks are next processed for operations (Figure 1, operation detection). Airport map data are used to determine the runway associated with a track (e.g., arrival to runway 27R). Tracks that occupy a runway region for a sufficient amount of time are analyzed to determine whether or not the velocity profile for a flight matches the profile for an arrival or departure. Matches are further processed to determine the on and off (touchdown and takeoff) times. Airport map data are used in subsequent processing to determine when a track enters regions such as ramp areas, deicing pads, and other user-defined regions. Next, each operation is examined to determine where holding may have taken place. These holding events are henceforth referred to as knots due to the associated tight cluster of surveillance data points. Heuristics have been developed to establish the location and duration of holds, given the limitations of the surveillance sources available.

3 The parsed ASDE-X data are integrated with other external data sources such as the FAA Aircraft Registry and Aviation Routine Weather Report (METAR) data. Automated processes have been designed to retrieve and parse these data as available. In some cases, these additional data sources provide new information; in others, redundant information is provided for validation. The last step of the processing builds an index of operations that includes the most interesting (or commonly-used) parameters. Presently, the index includes operation type (arrival, departure), operation time (e.g., wheels off), aircraft type, callsign, runway, ramp, ramp time, total taxi-time, and total holding time. This information can be aggregated by airline, airport, or any other categorization scheme of interest. Definition of Events The use of an airport surface diagram is instrumental to the characterization of the OOOI events and taxi-time estimates. The airport surface diagram is comprised of closed and conjoint polygons which represent the runways, ramp area, and the taxi-ways. Shown on Figure 2 is the airport surface diagram for the south side of Atlanta-Hartsfield International (ATL) Airport and its runway 27R, as a plan-view figure. For definition purposes, a departure aircraft trajectory is depicted on Figure 2 (red trace). The aircraft departs the ramp area (green polygon) such that the exit time from the ramp (red dot symbol) represents the initiation of the taxi-time through the movement area. We denote this as the out event, which occurs later than the push-back time for the departures; correspondingly, the entry into a ramp polygon of an arrival denotes its in event, which precedes the gate-in time. Consequently, we measure the total taxi-time in the movement area for which the ASDE-X system provides surveillance data. Those taxi trajectories are complete for which there is an out and off event for a departure and an on and in event for an arrival. In other words, the total taxi-time is defined by the times of the appropriate OOOI events. y coordinate location (nmi) R x coordinate location (nmi) Figure 2. OOOI and Taxi Event Definitions out hold off On Figure 2, the off event is depicted with an open, red diamond symbol. The location of hold events (i.e., knots) detected as the aircraft taxis are shown on Figure 2 as black dots. The excess taxi-time is the sum of the duration of the hold events. If the trajectory is complete, the minimum (i.e., unimpeded) taxi-time is the difference in the total taxi-time and the excess taxi-time. In the example depicted in Figure 2, the total taxi-time was 1.4 minutes for DAL168, which left the departure runway 27R at 1/9/8 22:54:26 GMT. Of the 1.4 minute total taxi-time, approximately 21% was due to holding such that the unimpeded taxi-time was 8.2 minutes. General algorithm descriptions OOOI events The total taxi-time estimate in the movement area is based on estimation of the terminating OOOI events for that operation. Because the ASDE-X system data are not intended to provide good quality surveillance coverage in the non-movement area (e.g., ramp, gates), we restrict the out and in events to exit and entry, respectively, from and into the non-movement area. As will be shown, these definitions produce minimum estimates of taxi-time (i.e., movement area only) and its attendant data. For an arrival, the on event is determined by the subset of surveillance locations (x,y) within a runway region (e.g., polygon) such that the ground speed at each location falls below some maximum ground speed. Additionally, the altitude data must fall below some airportspecific surface height. The first occurrence of the set of data limited by these location, speed,

4 and altitude constraints is considered to be the on time. For departures, the speed constraint logic is reversed (i.e., speeds must exceed a minimum ground speed value) and the altitude data must fall within a range about the airportspecific surface height. For departures, the off event is the first occurrence that satisfies all the constraints on location, speed, and altitude. In and out events are defined by the coverage extent of surveillance data and the degree of discretization of the non-movement area (e.g., sub-polygons in the ramp area). In some cases, the combination of good quality and extensive surveillance data with rational ramp polygons generates estimates of out and in times that will be close to the actual pushback and gate-in times. If the ramp polygons are too small, there may be a large number of operations that lack out or in event times, so a trade-off is implied. An in event is defined by the first location in the last ramp polygon entered by an arrival after the occurrence of the on event. An out event is defined by the last location in the first ramp polygon entered by a departure before the off event. Holding ( knots ) events The location and duration of holding by taxiing aircraft on the airport surface is quantified by a set of complementary algorithms. These algorithms are applied to position and ground speed data of aircraft as measured by the surveillance system. Figure 3 shows the trace of a taxiing arrival at Detroit-Wayne County International (DTW) Airport; the taxi-path appears as a heavy, solid line connecting small solid dots. The plan-view aspect of Figure 3 displays: taxi-ways (light gray shading), the ramp area (medium gray shading), and the terminal building (dark gray shading). Also shown on Figure 3 are two holds that were detected by the algorithms: the first lasted about one minute and was likely caused by momentary lack of gate availability (e.g., ramp control permission, equipment availability, local traffic). The second hold is located at the terminal building, lasted for 23 minutes, and contains the gate-in event. The ASDE-X system provides accurate surveillance data in the movement area (i.e., taxi-ways, runways) and not in the non-movement areas (i.e., ramps, gates). In cases where the surveillance system provides good quality data to the gates, our algorithms can be applied in the non-movement area to measure more of the taxi-time and holds. (1min) (23 min) Figure 3. Detection of Holds in Surveillance Data Fuel burn rate estimation Aircraft fuel burn and emissions data are available via a table obtained from the ICAO Engine Exhaust Emissions Databank [1]. The table contains statistics detailing the average rate of fuel burn and emissions of hydrocarbons (HC), carbon monoxide (CO), and nitrous oxides (NO x ). The data are provided per-engine for an idling or taxiing aircraft for 179 aircraft types. For each aircraft processed, two pieces of information must be obtained: an index into the fuel burn table and a total taxi-time (or excess taxi-time). Several methods, implemented in a cascading fashion (See Figure 4) were developed to index the fuel burn table. The best solution is to find a direct match for the ASDE-X provided aircraft type in the fuel burn lookup table. Due to mismatched naming conventions, such a match is found only 14% of the time. The next best solution is to use an intermediate lookup table to translate between the ASDE-X aircraft type and

5 the fuel burn table aircraft type. This table has been generated from several sources ([3], [4]). Figure 4. Fuel Burn Calculation Flow Chart When none of these methods produce an entry into the fuel burn table, an average entry is used. This average is taken from the population presently considered (e.g., Delta ATL arrivals to runway 9L on 1/1/8). The total taxi-time (or excess taxi-time) has already been calculated at this point. In cases where the track is not complete, an average (population-wise, see above) taxi-time is used instead. On-Time Performance Database). Other checks not performed here can examine the validity of assuming the use of one engine for taxiing, number of engines on during holding, and actual fuel burn rates and emissions quantities. Operation Counts for ASDE-X and ASPM Comparisons of the number of operations counts were performed between ASDE-X data and the FAA s ASPM database. The operation count data were taken from the effective arrival and effective departure fields of ASPM, based on quarter-hour counts. The data were measured at the 13 airports for which the surveillance data are currently available and the date range for which the data were compared was from 1/5/8 to 1/1/8. Figure 5 shows the frequency of the difference between ASDE-X and ASPM data for arrivals and departures, with good agreement (arrivals: S e =.92 operation, n = 2481 operations; departures: S e =.91 operation, n = 265 operations). The total and excess fuel burn are the summed products of each individual fuel burn rate and (excess) time spent taxiing. Calculation of the cost of excess fuel burned is based on $5.7/gallon jet-a fuel as a nation-wide average on 4/7/8 ( Emissions calculations are obtained in a similar manner to that employed for the fuel burn calculations. Validation Validation of the methodology previously described should check the quality of its output relative to other sources of information; several comparisons can be performed. For operation counts (e.g., number of arrivals per 15-minute interval), there should be a close match between the data derived from the surveillance system and the information reported by air traffic controllers (e.g., ASPM). It is expected that the total taxi-time derived from surveillance data covering the movement area will be shorter than the taxi-time that encompasses taxiing in the movement and non-movement area (e.g., BTS Figure 5. Operation Count Difference between ASDE-X and ASPM Data Total Taxi-time for ASDE-X and BTS The second analysis compared the total taxitime from the surveillance data with the quantity reported by the BTS. This analysis is preliminary in that it only examines data from ATL on 1/9/8 GMT. Additional work will extend the comparison to more airports and for

6 BTS total taxi time (min) BTS total taxi time (min) more dates. The comparison of the total taxitime is based on data from our methodology and from the BTS Airline On-Time Performance Database. The comparison of the total taxi-time was based on a per-aircraft basis, such that a match on tail number and operation time (within five minutes) was required. Furthermore, only those flights with a complete trajectory (i.e., estimated total taxi-time) were used in the comparison. The total taxi-time estimates provided by the analysis of the surveillance data apply to the movement area and therefore are minimums with respect to the BTS-provided taxi-times. This is demonstrated in the plots of total arrival taxi-time (Figure 6) and total departure taxitime (Figure 7). With to-gate surveillance data, our work will measure more holding in the nonmovement area such that the dispersion in Figure 6 and Figure 7 will shrink ASDE-X total taxi time (min) Figure 6. Per-Aircraft Comparison of BTS and ASDE-X Arrival Total Taxi-time, ATL, 1/9/8 GMT ASDE-X total taxi time (min) Figure 7. Per-Aircraft Comparison of BTS and ASDE-X Departure Total Taxi-time, ATL, 1/9/8 GMT Sample Results We present samples of the automated data processing and taxi-time/fuel burn estimation algorithms. As already noted, this process generates automatic reports and sends the information via web page application. Referring to Table 1, an example of a daily airport summary is depicted for the 13 currently available airports on 1/9/8 GMT. The table reports operation counts per airport on this date, as well as data quality statistics (i.e., percent complete taxi trajectories and percent known fuel burn rates). Given the fuel burn rate per each aircraft, we report a total excess fuel burn quantity for arrival and departure operations at each airport. The excess fuel burn quantity depends on the excess taxi-time, which is the difference between the total and unimpeded taxi-time. Cost of excess fuel burned is reported. Data on estimated emissions (CO, NO x, and HC) are not reported in Table 1 but are calculated. A proposed airport operations efficiency metric is reported as the ratio of excess to total average taxi-time, which allows comparisons between airports. Referring to Table 1, it can be seen that the average total and excess taxi-time of departures are larger than those for arrivals, and in some cases represent large excesses in fuel burned (e.g., ATL departures: 114,2 lbs). Recall that these quantities in Table 1 are minima and represent estimates for the movement area alone. Future work will attempt to quantity non-movement area estimates. Also, we intend to attribute delay to causal factors (e.g., delay in departure queue, delay at spot location). The ratio of excess to total taxi-time shows that for arrivals on this date, MCO and BDL are most efficient; for departures, MCO and SEA have the most efficient operations. Subsequent work will characterize the change in this efficiency metric with time of day or day of week and weather conditions, adding other ranking metrics and attempting causal attribution. The ratios of excess to total taxi-time are plotted for each airport in the date range of 1/6/8 to 1/1/8 versus operation count (see Figure 8).

7 Table 1. Sample Fuel Burn Report for ATL, January 9, 28 GMT operation airport no. of 3 min operations %complete %known fuel average taxi-time excess (lbs) excess excess taxi-time to type name files per day count taxi trajectories burn rate total (min) excess (min) fuel burned fuel cost * total taxi-time ratio ATL ,4 $29,2.38 BDL $6.11 CLT ,6 $4,.23 DTW ,2 $6,7.19 IAD ,2 $4,5.19 MCO ,2 $2,3.1 arrivals MEM ,6 $6,2.22 MKE ,8 $1,3.18 ORD ,4 $12,6.32 PVD $2 N/A SDF ,3 $4,6.26 SEA , $2,9.23 STL ,4 $2,4.18 ATL ,2 $82,6.6 BDL ,8 $4,9.45 CLT , $53,5.7 DTW ,1 $24,7.45 IAD ,4 $24,9.52 MCO , $1,8.38 departures MEM ,8 $28,.45 MKE ,2 $6,7.49 ORD ,6 $3,8.47 PVD ,4 $3,9 N/A SDF , $15,2.62 SEA ,3 $9,6.36 STL ,7 $12,1.44 * Based on $5.7/gallon jet-a fuel, nation-wide average on 4/7/8, N/A Not applicable because the number of 3-minutes files is less than 48 for 1 day

8 This shows that there is a dependence of the excess/total taxi-time ratio on operation count (i.e., busier airports experience more taxi delays), and this holds for arrivals and departures. Figure 8 shows a consistent off-set (i.e., intercept) between the best-fit lines to the data for arrivals and departures. This means that the ratio of departure to arrival taxi efficiency is independent of operation count, with about 1/3 more inefficiency in departure taxiing compared to arrival taxiing across all 13 airports. excess taxi-time / total taxi-time arrivals departures best-fit curve for departures best-fit curve for arrivals daily operations count Figure 8. Dependence on Excess/Total Taxi-time on Operations Counts Figure 9 shows the time series by way of example for average daily estimates for ATL from 1/5/8 to 1/31/8 in terms of number of departures, total taxi-time, excess taxi-time, excess fuel burned, and excess fuel cost. The sample data on Figure 9 show that the excess fuel burned (and cost) is controlled by the average excess taxi-time. Also, note that there is a general dependence of average total taxitime on the daily operation counts, although there are exceptions (e.g., poor weather effects from 1/16/8 2: to 1/18/8 4: GMT with visibility less than 3 miles). Figure 1 shows the total taxi-time for departures from ATL on 1/9/8 GMT, with the companion statistics for unimpeded and excess taxi-time. This figure shows that much of the departure taxi-time in the movement area is due to holding, although reductions in the average excess fuel burned (lbs) excess fuel burned 1s of departure operations date from 1/5/8 excess average taxi-time Figure 9. Time Series for ATL Departures, 1/5/8 to 1/31/8 taxi speed of continually moving aircraft is doubtlessly important, too. A corresponding figure for arrivals at ATL on this date shows that the preponderance of total taxi-in time is not due to holding, but is more likely due to slowing of arrival taxi speeds with increased congestion. In summary, analysis of taxi-times for arrivals and departures at ATL on this date suggests that reviving aircraft experience more holding in the non-movement area and departures experience more holding in the movement area. Figure 1. Taxi-Time Histograms for ATL Departures, 1/9/8 GMT taxi-tim e (m in), 1s departures

9 average taxi-time (min) Quantizing the total, excess, and unimpeded taxi-out time for ATL departures on 1/9/8 GMT (Figure 11) shows these quantities change with time, likely driven by departure schedule. Figure 11 also shows that the unimpeded taxitime (i.e., holding removed) increases with time, which indicates that the average taxi speed decreases. Even if holding is removed, there is still benefit to be gained by controlling the speed variability of moving aircraft. On-going modeling will develop tools with which to predict the taxi-out time for average total and average unimpeded taxi-time average excess taxi-time average total taxi-time average unimpeded taxi-time time as 15-minute interval Figure 11. Partial Time Series of Taxi-times for ATL Departures, 1/9/8 GMT In Table 2, the runway-specific average total, excess, and minimum taxi-times are reported for arrivals and departures at ATL on 1/9/8. These data can be easily further differentiated on the basis of pairings of runway and gate/ramp area, and this is key to development of prediction of models with further refinements in applicability and accuracy. The implication is that accurate predictive models will depend on knowing the runway and gate/spot, which may require separate data sources or predictive models. Table 2. Runway-Specific Average Taxi-Times, ATL on 1/9/8 GMT Operation Runway Operation Average Total Average Excess Average Minimum Type Name Count Taxi-Time (min) Taxi-Time (min) Taxi-Time (min) Arrival 26R L L R L R Departure 27R R L As previously stated, the aircraft-specific data (taxi-time, aircraft type, fuel burn rate) are summarized on the basis of airport (See Table 1) and can be applied to airport configuration (e.g., Table 2). Another tabulation is a summary for airline carriers at an airport. Shown in Table 3 are the taxi-time statistics for three important carriers at ATL, on 1/9/8 GMT for which there were at least 8 operations per carrier (i.e., 8+ arrivals, 8+ departures); the carrier name is replaced with a unique identifier,. The efficiency ratio (average excess to average total taxi-time) is less for arrivals than for departures. The cost to individual carriers of excess taxi-time can be determined and the carrier can apply its proprietary fuel costs to complete the business assessment. Not shown is the variability of the unimpeded taxitime, which may measure congestion and pilot enthusiasm. Table 3. Carrier-Specific Average Taxi-Times, ATL on 1/9/8 GMT Arrivals Total Taxi-Time Excess Taxi-Time Excess / Carrier_id Mean Std Mean Std Total Ratio Departures Total Taxi-Time Excess Taxi-Time Excess / Carrier_id Mean Std Mean Std Total Ratio

10 Summary This work describes the process by which surveillance data are used to estimate total and excess taxi-time in the movement area of an airport. Used in conjunction with aircraftspecific data, we derive estimates of total and excess fuel burn, fuel cost, and emissions. All these estimates are generated on a daily basis for a growing list of airports (13 as of the writing of this paper) for which surveillance data are available. Summary reports can be created and served via a number of platforms (e.g., decision support tool, alert, web application). These reports compare the efficiency performance across airports, airlines, and between flights. Estimates of total and unimpeded taxi-time can be reported as a function of combinations of runway and gate/spot area. As coverage extends to the nonmovement area, the work here can describe holding and delays in the ramp area. We envision that this analysis capability can be extended from a post-hoc (i.e., day-after) analysis to day-of-operations (i.e., control) and forecasting in any surveillance system (e.g., ASDE-X, A-SMGCS). Additional comparisons to other sources of taxi-time data are needed, as well as assessment of other assumptions (e.g., single engine usage for taxiing). Future work will attempt to perform delay attribution. Use of professionally-developed airport surface maps will improve the estimation of in and out times. December 2. Environmental Benefits Associated with CNS/ATM INITIATIVES. [4] Coleman, N.A., 2. Draft Guidelines for Quantifying the Environmental Benefits of An Investment Analysis. Federal Aviation Administration, Investment Analysis & Operations Research (ASD-4). Acknowledgements We gratefully acknowledge the support, direction and review of our work that had been generously provided by our colleagues at Sensis Corporation. In specific, we thank Kevin Lefebvre, Mark Runnels, John Sorensen, and Nichole Wenderlich. References [1] ICAO Engine Exhaust Emissions Databank, Updated 16 July 27, Doc AN/943 [2] Levy, B.S., K.R. Lefebvre, and J. Legge, 27. Quantification and Forecasting of Emissions from Taxiing Aircraft, INO Workshop 27 (6 th EUROCONTROL Innovative Research workshops & Exhibition), Brétigny-sur-Orge, France, December 4-6, 27 [3] European Organisation for the Safety of Air Navigation (EUROCONTROL), EUROCONTROL Experimental Centre (EEC) and Federal Aviation Administration (FAA), Operations Research and Analysis (ASD-43),