empirical assessment of the end-around taxiway's ...docs.trb.org/prp/13-5272.pdfmassidda a.,...

Massidda A., Mattingly S. 1

TRANSPORTATION RESEARCH BOARD 1

92nd Annual Meeting – January 13-17, 2013 2

3

TITLE: Empirical Assessment of the End-Around Taxiway’s Operational Benefits at 4

Dallas/Fort Worth International Airport Using ASDE-X Data 5

6

PAPER NUMBER: 13-5272 7

8

AUTHORS: *Antonio Massidda, Faculty Research Associate 9

Department of Civil Engineering 10

University of Texas at Arlington 11

P.O. Box 19308, Arlington, TX 76019-0308 12

Phone: 214-208-0808 13

E-mail: [email protected] 14

15

Stephen P. Mattingly, Associate Professor 16

Department of Civil Engineering 17

University of Texas at Arlington 18

P.O. Box 19308, Arlington, TX 76019-0308 19

Phone: 817-272-2859 20

E-mail: [email protected] 21

* - corresponding author 22

23

KEY WORDS: End-Around Taxiway, Perimeter Taxiway, Airport, Runway Crossing, Runway 24

Safety, Throughput, Delay, Capacity, Taxi Time, Airfield Planning, Dallas/Fort Worth, DFW, 25

ASDE-X, ADS-B, Database 26

27

WORD COUNT: 5,050 words + 4 tables + 5 figures = 7,300 28

29

ABSTRACT 30

At the present time, only a few airfields in the world have an End-Around Taxiway (EAT). 31

Since December 2008, an EAT serves Dallas/Fort Worth International Airport’s (DFW) runways 32

17L, 17C, and 17R, with the purpose of reducing the number of runway crossings and therefore 33

improving safety and capacity. 34

This paper describes the results of the research project (funded by the FAA and 35

performed by the authors) to assess the safety impacts of DFW’s EAT in terms of reduction in 36

number of runway crossings. In addition, this paper empirically defines the enhancement in 37

departure and arrival throughput achieved after the construction of the EAT. These assessments 38

are based on data from DFW’s Surface Detection Equipment – Model X (ASDE-X) database. 39

This study has found that the EAT has improved runway safety, increased capacity and 40

reduced departure delay at DFW, although for several reasons its usage is essentially limited to 41

runway 17L arrivals. The EAT has eliminated on average 51% crossings on runway 17C daily 42

and over 83% percent of runway 17L arrivals use the EAT or cross runway 17C using low-risk 43

taxiways. In fact, the EAT has nearly eliminated all mid-runway 17C crossings due to 17L 44

arrivals. 45

The EAT has exceeded the expected enhancements of departure and arrival capacity. 46

Compared to pre-EAT operations, the ASDE-X data reveals that both departure and arrival 47

TRB 2013 Annual Meeting Paper revised from original submittal.


demand have increased at DFW. However, EAT operation has allowed the daily mean arrival 48

and departure maximum throughput rates to increase by 40% and 25%, respectively, while the 49

mean daily maximum departure delay has decreased by 38%. 50

51

1 INTRODUCTION 52

The End-Around Taxiway (EAT) is an innovative runway safety infrastructure concept that the 53

Federal Aviation Administration (FAA) has introduced in recent years at some of the busiest 54

airports in the United States. The primary EAT goal is to reduce the number of runway 55

crossings, thereby reducing the opportunities for runway incursions; as a result, on these 56

runways, which experience fewer crossings, additional expected operational benefits from an 57

EAT include (1) an increase in departure rate and therefore a reduction in departure queue delay. 58

At the present time, Hartsfield-Jackson Atlanta International Airport (ATL) and 59

Dallas/Fort Worth International Airport (DFW) have one EAT each; San Francisco International 60

Airport (SFO), Houston George Bush Intercontinental Airport (IAH), and Los Angeles 61

International Airport (LAX) have considered implementing EATs. In Europe, only Amsterdam 62

Schiphol (EHAM) and Frankfurt Main (EDDF) have an EAT (2). 63

The original plan for DFW’s EAT system included a total of four EATs, each located in 64

one of the airport quadrants (1). FAA performed an early study of this EAT system in 1998 (1); 65

five years later, in cooperation with DFW’s management and air traffic control (ATC), the FAA 66

conducted a human-in-the-loop simulation at the NASA Ames Research Center (ARC), which 67

included a control tower simulator and a full flight simulator (1). In this simulation, actual DFW 68

air traffic controllers and pilots interacted in a simulation of DFW operations without and with 69

the proposed EAT system (1). The results of this simulation were used as an operational 70

baseline for the subsequent EAT planning process. 71

The FAA Airport Obstruction Standards Committee (AOSC), in 2005, per DFW request 72

evaluated the compatibility of an EAT located on the Southeast quadrant with departure 73

operations. The U.S. Standard for Terminal Instrument Approach Procedures (TERPS), which 74

required protection of the 40:1 Obstacle Clearance Surface (OCS) from penetrations by the tails 75

of taxiing aircraft (3), provided the technical reference for this assessment. Given the slope of 76

the OCS and a distance of about 0.5 miles from the runways’ 17C and 17R thresholds to the 77

EAT (Figure 1), the AOSC observed that aircraft with tails up to 65 feet (Group V) would not 78

penetrate the departure surface (3). 79

80

81 82

FIGURE 1 Sketch of EAT compatibility with OCS. (17) 83



As a result, the AOSC approved unrestricted operations on the Southeast EAT, but 84

limited to South flow airport configuration (3). 85

In 2007, one of the authors estimated that the probability to penetrate the OCS for an 86

aircraft taxiing on the EAT was nearly zero, based on transponder height data analysis (4). 87

DFW and FAA started the construction of the Southeast EAT in 2006, and it opened in 88

December 2008; as shown in Figure 2, this EAT is essentially formed by extensions of the north-89

south taxiways “P”, “M”, “JS”, and by taxiway “ES” that links these extensions along their south 90

ends. This EAT serves the three parallel runways on the East side of the airport 35L/17R, 91

35C/17C, and 35R/17L. 92

93

94 95

FIGURE 2 DFW EAT, runways 35L/17R, 35C/17C, and 35R/17L. (5) 96

97

The current operating procedure for this EAT, issued by FAA DFW ATC in April 98

2011(6), states that during VFR conditions the EAT should be preferred for the arrivals leaving 99

runway 17L via taxiway “ER” (unless ATC approves an exception), and for runway 17C arrivals 100

only when the wait time to cross runway 17R is expected to be more than five minutes. 101

In recent years, the FAA installed Airport Surface Detection Equipment – Model X 102

(ASDE-X) at many of the nation’s busiest airports, including DFW (7). The primary purpose of 103

ASDE-X is to prevent runway incursions by enhancing the situational awareness of tower 104

controllers through the visualization of real time localization and identification of aircraft and 105

vehicles on an electronic map of the airport, which also includes airborne aircraft flying the final 106

approach segment. The ASDE-X data can be stored in a database for further processing and 107

analysis, and it can be enriched by merging data acquired by different sources, such as automatic 108

dependent surveillance broadcast (ADS-B), and radar regarding airborne operations beyond 109

ASDE-X’s radius of reception. As a result, this combined data provides valuable information 110

pertaining to aircraft operations; for example, the database integrates several characteristics of 111

individual flights, such as call sign, tail number, gates, specific surface movements (e.g. 112

runways, taxiways, and taxi times), and navigation fixes used. This study uses the DFW ASDE-113

X database created and maintained by NASA/FAA North Texas Research Station (NTX). The 114

specific ASDE-X data used are presented in the data analysis section. 115

The importance of research using ASDE-X data continues to increase; however, at this 116

time, there is limited runway and surface safety research based on this data. Typical research 117

using ASDE-X data address impacts associated with surface movement characteristics, new 118

approach and departure procedures, and surface management systems at a single airport. 119

Engelland (2) uses the DFW ASDE-X database to quantify EAT usage volume and to compare 120

the taxi times for arrivals using the EAT with those crossing the runways. Simaiakis et al. (8) 121

develop a pushback rate control strategy to reduce delay and maximize throughput for departures 122



based on an analysis of the Boston Logan International Airport ASDE-X database. Balakrishnan 123

et al. (9) develop a taxi route plan that optimizes surface operations using Integer Programming. 124

ASDE-X data is proving to be critically desirable for assessing the impacts of airport 125

infrastructure and operational policy changes. 126

Given the increasing congestion of the National Airspace System and at major airports, 127

the problems of airfield capacity and throughput have received significant attention from several 128

researchers and the FAA. Several approaches for estimating or calculating airfield capacity and 129

throughput have been proposed. In one such study, Hansen et al. (10), proposes a method for 130

empirically analyzing the runway throughput. This study uses this methodology for analyzing 131

the impacts of the EAT on arrival and departure throughput (more details are provided in section 132

3). While all previous studies on EAT impacts have been based on simulation results, this 133

research seeks to evaluate the actual EAT impacts on surface operations using DFW’s ASDE-X 134

data. 135

Section two describes the approach used for conducting a before and after evaluation of 136

surface operations at DFW. In section three, the ASDE-X data is used to analyze the EAT’s 137

impacts on runway crossings, departure and arrival throughput, and departure delay. The final 138

section summarizes the overall impacts of the EAT on safety and operations, and proposes 139

additional research involving the EAT and ASDE-X data. 140

141

2 Research Approach 142

This research used the ASDE-X database to compare observed runway crossings before/after the 143

EAT construction. In addition, the study assesses the impacts on demand, delay, and throughput 144

for runway 17R departures, and on arrival throughput for runways 17C and 17L. The study uses 145

the Surface Operations Data Analysis and Adaptation (SODAA) tool, developed by Mosaic 146

ATM, Inc. to access the database and export the information to custom spreadsheets developed 147

by the research team. The authors develop SODAA queries to retrieve taxi routes for runway 148

17L arrivals during South flow operating conditions. 149

Data sets are randomly selected from both the before and after periods based on required 150

minimum sample size estimates (see 11 for more information). The research team defines the 151

Before EAT time period from April 2008 to November 2008, which corresponds to the entire 152

pre-EAT operations portion of the ASDE-X database. To control for seasonal effects, the After 153

EAT period begins in April 2011 at the same time as the current EAT usage policy (1) and 154

extends for the same length of time as the before period, until November 2011. The researchers 155

have verified (using online historical aviation meteorological information, see 11) that during 156

every randomly selected day only regular South flow operations were conducted and that the 157

weather conditions were compatible with visual flight rules (VFR) (1). 158

A preliminary analysis of Before EAT data shows that the volume of runway 17C arrivals 159

that use the EAT appear negligible. This confirms the findings of the aforementioned 160

Engelland’s study (2). Therefore, the research team compares the distribution of runway 17C 161

crossings observed before and after the EAT. However, the effects of runway 17R crossings are 162

considered during the departure delay and throughput analyses. 163

164

3 Data Analysis 165

This section uses observed operations retrieved from the DFW ASDE-X database to assess the 166

impact of the EAT. The changes in the runway crossing distribution relate to both the 167



operational and safety benefits associated with the EAT while the throughput and delay analysis 168

characterize both the capacity and operational improvements. 169

170

3.1 Runway Crossing and EAT Operation Distributions 171

During south flow configuration at DFW, the locations of taxiways “Y”, “Z”, and “EJ” increase 172

the potential severity of a runway incident due to runway incursion; in fact, aircraft landing on 173

runway 17C reach those points at considerable speed, leaving less time for avoiding a collision, 174

and increasing the potential severity. Taxiway “EL” poses similar concerns for both arrival 175

operations on runway 17C and departure operations on runway 17R. Taxiways B and ER are 176

located south of the designated land and hold short point for runway 17C; therefore, those 177

taxiways are often used while conducting land and hold short operations (LAHSO). Figure 3, a 178

detail from the airport diagram, shows those runways, taxiways, and the EAT. 179

180 181

FIGURE 3 Runways 17R, 17C, intersecting taxiways, and the EAT. (5) 182

183

3.1.1 Methodology 184

The taxiway intersections with runway 17C are aggregated into three groups based on the 185

similarity of their safety impacts: North, Middle, and South. For the After EAT case only, the 186

EAT, represented by taxiway “ES”, is added as a fourth group. The taxiway groups are shown in 187

Table 1. 188

Table 1 Taxiway Groups 189

190

Before

EAT

After

EAT Taxiways

Taxiway

Groups

North

Y

Z

EJ

EK

K8

Middle EL

EM

South

B

A

EQ

ER

N/A EAT ES

191



The Before EAT runway crossing distribution is obtained by combining runway 192

crossings from twelve randomly selected data sets (days) with a total number of 772 operations 193

(only runway crossings), and the After EAT frequency distribution is obtained by combining 194

runway crossings from fifteen randomly selected data sets (days), with a total number of 807 195

operations (runway crossings and EAT operations). The study confirms the suitability of these 196

sample sizes by using the methodology proposed by Nisen et al. (13); for the minimum sample 197

size determination, α and β are set at 0.05 and a meaningful change is set at ten percent. With 198

these values, the minimum required sample size is 477 operations, which both the Before EAT 199

and After EAT cases surpass. The researchers conduct validation tests (see 10 for more 200

information) to provide additional verification that the randomly selected samples adequately 201

represent their respective sampling frames. 202

The Before EAT distribution is compared with the After EAT distribution using the 203

Pearson Chi Square test (also known as “goodness of fit”). This test evaluates if a change in 204

runway crossing distribution has occurred. The tested null and alternative hypotheses for this 205

goodness of fit test are: 206

207

• H0 = the After EAT distribution is not significantly different than the Before EAT 208

distribution (FA North = FB North, FA Middle = FB Middle, FA South = FB South) 209

210

• H1 = the After EAT distribution is significantly different than the Before EAT distribution 211

(FA North ≠ FB North or FA Middle ≠ FB Middle, or FA South ≠ FB South) 212

213

Where: 214

FA North/Middle/South, = After case frequency for the category 215

216

FB North/Middle/South, = Before case frequency for the category 217

218

The Chi Square values are calculated with the following: 219

220

��

�

�� (1)

221

Where: 222

χ2 = Pearson's cumulative test statistic 223

224

Afteri = After value of category “i” 225

226

Beforei= Before value of category “i” 227

228

j = Number of categories 229

230

3.1.2 Results 231

Table 2 compares the frequencies of taxiway group usage between the Before EAT and the After 232

EAT case. 233

234



Table 2: Comparative Assessment Runway 17C Crossing Before and After the EAT 235

236

Taxiway

Groups

Frequency

Before EAT

Frequency

After EAT Change (%)

North 0.18 0.15 -17%

Middle 0.44 0.02 -95%

South 0.38 0.32 -16%

EAT N/A 0.51 N/A

237

When comparing only the North, Middle and South distributions, the Chi Square test 238

statistic is equal to 446, which is an extremely high value; this statistic corresponds to a p-value 239

that is essentially zero. 240

241

The safety benefits from DFW’s EAT are very clear. This demonstrates that a significant 242

difference exists between the Before and the After case runway crossing frequency distributions, 243

and that the EAT has a positive safety impact on runway crossings. After the EAT, the number 244

of runway crossings reduced on all taxiway groups; on average, the EAT removes fifty-six 245

crossings per day. Most importantly, the “Middle” group’s crossings, which had the highest 246

severity risk in case of a runway incursion, have been significantly reduced and virtually 247

eliminated. In a hypothetical safety assessment process, part of the FAA Safety Management 248

System, the EAT functioned as a risk mitigation strategy to reduce the risk associated with those 249

crossings, based on the FAA risk matrix (14). 250

Within the South group, taxiway “B” accounted for about three quarters of the group’s 251

crossings during the Before EAT case, but after the EAT construction, its frequency decreased to 252

only four percent. This reduces the risk of collisions when land and hold short operations 253

(LAHSO) are conducted on runway 17C. In fact, while “B” is located just 235 ft beyond the 254

LAHSO stop bar, taxiway “ER” crosses that runway’s South threshold, is located more than 255

2,700 ft beyond. 256

After the EAT, 83% of runway 17L arrivals either used the EAT or crossed runway 17C 257

at the low risk “South” intersection. Overall, the risk associated with surface operations appears 258

to have decreased significantly because of the EAT. 259

260

3.2 Departure and Arrival Throughput 261

In addition to providing safety benefits, the FAA and DFW expected the EAT to improve 262

operations and increase capacity. At the time of the EAT’s initial conception, DFW experienced 263

severely constrained operations due to high demand. Throughput represents a commonly used 264

measure of effectiveness for assessing capacity constrained operations. While throughput lacks a 265

standard calculation methodology, its ability to measure the rate of operations makes it extremely 266

useful for assessing the impacts of infrastructure and policy changes. This study uses the 267

methodology discussed in the next section for determining departure and arrival throughput. 268

Figure 4 presents an example of the observed pattern of EAT usage and departure and arrival 269

operations for one day. One of the expected, but clear patterns is that the departure rate and 270



arrival rate do not peak at the same time, since arrivals generate runway crossings for 17R that 271

periodically interrupt departure operations. 272

273

274 275

FIGURE 4 Departure, arrival, and EAT operations. 276

277

The EAT usage pattern shows that the ATC uses it to allow uninterrupted operations on the 278

departure and arrival runways, during high demand. The impacts of this usage strategy on the 279

departure and arrival throughput are assessed in section 3.2.2. 280

281

3.2.1 Methodology 282

The reduction in runway crossings and the change in their distribution achieved by the EAT’s 283

construction shows that the EAT likely has a positive impact on both arrival and departure 284

operations by reducing their interruption due to crossing aircraft. As mentioned in the 285

introduction, the researchers use the methodology proposed by Hansen et al. to calculate hourly 286

departure and arrival throughput (10) for runways 17R and 17C, respectively. This study 287

replaces the data from the Airline Service Quality Program (ASQP), used by that study, with 288

DFW’s ASDE-X data. Since the ASQP data refers to only the ten largest airlines in the U.S. at 289

the time, which accounted for sixty percent of the flights at DFW (10), the throughput rates 290

calculated by the older study slightly underestimated the total rates (10). Since the ASDE-X 291

database includes all flights at that airport compared to older databases, its data may be used to 292

develop more accurate assessments of airport operational performance. While ASDE-X data 293

gaps may affect accuracy, however, the authors have developed strategies to close the few 294

observed gaps (report). 295

Although the aforementioned study estimated DFW’s arrival throughput for all runways 296

in an aggregate form, the same methodology may be used to determine arrival and departure 297

throughput for an individual runway. To calculate a maximum rate, Hansen’s study found the 298

shortest time in which N flights arrived at their gates. That number had to be “large enough to 299

yield a stable average, but small enough to avoid lulls in demand” (10); as a result, these 300

researchers set N equal to thirty flights. The “hourly arrival rate based on the shortest time 301

interval for thirty arrivals at the airport is defined as the Daily Maximum Throughput Rate 302

0

5

10

15

20

25

30

35

40

45

50Runway 17C and

17L Arrivals

17R Departures

EAT Operations



(DMTR).” This study replicates this methodology using the DFW ASDE-X data, which 303

provides the departure and arrival times, runway entry, exit, and time in queue. For more 304

detailed information on how this data is processed and interpreted by the SODAA tool, the 305

reader can refer to the user manual. Using the same method for calculating the DMTR, this 306

study calculated the Daily Maximum Departure Demand (DMDD) and the Daily Maximum 307

Average Departure Delay (DMAD). 308

A sample size of five for the before and after cases is adequate for assessing the impact of 309

the EAT on throughput. Five days are selected using Simple Random Sampling from the before 310

and after cases; once again, the researchers verify that VFR conditions exist for the selected 311

days. When VFR conditions do not exist the day is discarded and another day is randomly 312

selected to replace it. Each randomly selected day represents a single data point in the analysis 313

because the maximum daily throughput values are used. The mean daily maximums (DMTR, 314

DMDD and DMAD) for the before case are compared with the same values after the EAT’s 315

construction. A t-test is used to evaluate if EAT has a significant impact on the throughput and 316

delay. 317

318

3.2.2 Results 319

Departure Throughput 320

For the departure throughput, this study focuses on runway 17R, the primary departure runway 321

on the East side during South flow, to determine if the EAT increases departure capacity as 322

measured by departure DMTR. The reduction in runway crossings that result from use of the 323

EAT should have a positive impact on capacity; however, this effect is somewhat muted because 324

the EAT is not used by the vast majority of the arriving aircraft (93%). The study also 325

determines Runway 17R’s DMDD and DMAD. The departure demand differs from the 326

departure throughput because the first is the rate at which aircraft reach the departure queue, 327

which is determined by the software’s logic based on aircraft position, while the second is the 328

rate at which the runway serves the departing aircraft. Table 3 shows the results of the departure 329

throughput analysis for runway 17R and provides the significance level observed for the change 330

in mean values between the before and after cases. 331

332

Table 3 Comparison of Departure Performance for Runway 17R Before and After the 333

EAT 334

335

Runway 17R

Daily Maximum

Throughput Rate

DMTR

(departures/hr)

Runway 17R

Daily Maximum

Departure Demand

DMDD

(aircraft/hr)

Runway 17R

Daily Maximum

Average Departure

Delay

DMAD

(minutes)

Mean Std.

Deviation Mean

Std.

Deviation Mean

Std.

Deviation

Before EAT 39.14 3.51 37.88 2.53 10.95 6.35

After EAT 48.83 3.74 47.34 3.40 6.80 1.00

Significance Level 0.002 0.001 0.222

336



When comparing the mean values for the DMTR and DMDD observed before and after 337

the EAT for runway 17R departures, the extremely low p-values demonstrate that a significant 338

increase has occurred after the implementation of the EAT; the reduction in crossings appears to 339

provide a direct operational benefit and permits runway 17R to accommodate higher demand. 340

The simulation conducted during EAT development (1) estimated an average increase of the 341

total departure rate by 18 aircraft per hour when considering all departure runways. Given that 342

DFW ATC, essentially, uses two runways for departures during South flow, the results of that 343

simulation can be divided by two to consider only one departure runway, by assuming that 344

departures may be equally distributed between East and West side runways. This study observes 345

an average increase in DMTR of 9.69 aircraft per hour, which appears to validate the simulation 346

study results. The high variability in the DMAD makes its assessment more difficult. While a 347

thirty-eight percent reduction is observed, a much larger sample size is required to verify this 348

result statistically. Departure delays should be investigated in more depth in future research. 349

350

Arrival Throughput 351

This analysis measures the increase in average Daily Maximum Arrival Throughput Rate 352

(DMATR) for runway 17C as a result of the fewer crossings observed after the EAT; in addition, 353

the combined arrival throughput for runways 17C and 17L is calculated (see Table 4). 354

355

Table 4 Comparison of the Arrival Throughput Rate Before and After the EAT for 356

Runway 17C Alone and Combined with 17L 357

358

Daily Maximum Arrival Throughput Rate

(DMATR)

(departures/hr)

Runway 17C Runways

17C and 17L

Mean Std.

Deviation Mean

Std.

Deviation

Before EAT 31.87 0.88 39.96 3.15

After EAT 34.05 0.97 56.52 5.49

Significance Level 0.004 0.001

359

Although the increase of the average DMATR observed after the EAT for runway 17C is 360

statistically significant, its magnitude is limited. In average, only about two more arrivals per 361

hour are observed. However, when considering runways 17C and 17L combined, their average 362

DMATR increased dramatically after the EAT; these runways are now serving over sixteen more 363

arrivals. To exclude that these results are due to a lull in departure demand during the DMATR 364

time interval, the researchers compared the observed demand and throughput rates for runway 365

17R and found that they had increased after the EAT. Before the EAT, the mean departure 366

demand during runway 17C DMATR was 26 aircraft per hour, while after the EAT the mean 367

demand increased to 30.2; when considering runways 17C and 17L combined, the mean 368

departure demand increased from 25 to 26.6. 369

370



4 CONCLUSIONS AND FUTURE RESEARCH 371

This research compares ASDE-X data for operations at DFW before and after the 372

implementation of the EAT, which represents the current ATC operating procedures. The 373

findings clearly indicate that the EAT at DFW achieves its goals of improving runway safety and 374

airfield capacity. Firstly, it improves the safety of East side surface operations by reducing the 375

number of crossings on runway 17C, used for arrivals, and by relocating other crossings away 376

from more dangerous locations. Secondly, the East side runways’ capacity increases because the 377

arrival and departure operations can be better sequenced and coordinated with the conflicting 378

surface operations. 379

This study finds that the EAT is used by between 47% and 55% of runway 17L arrivals; 380

however, only between 6% and 8% of all the East side runways 17C and 17L arrivals use the 381

EAT. These EAT usage levels correspond to an average daily reduction of 56 runway crossings. 382

In addition, the EAT has been a catalyst for implementing operational procedures that mitigate 383

runway crossing risk due to their locations, considering the FAA risk assessment matrix (14). 384

The EAT operating procedure adopted by DFW ATC allows using the low risk taxiway “ER” to 385

cross runways 17C and 17R on in lieu of using the EAT. Therefore, virtually all of the average 386

48 runway crossings per day observed before the EAT runway crossings at the midfield taxiway 387

EL, which posed the most safety concerns among all crossing due to its location and the potential 388

conflict with departure and arrival operations at high speed, have been eliminated. Also, when 389

conducting LAHSO on runway 17C, this observed increase in use of taxiway “ER” has mitigated 390

the risk by increasing the minimum potential distance between a landing aircraft and a crossing 391

aircraft from about 235 feet to about 2,700 feet. 392

This study demonstrates the improvement in runway capacity due to EAT operations by 393

quantifying their daily maximum throughput. The observed mean DMTR after the EAT, is 394

significantly larger than the mean DMTR observed before the EAT. The departure capacity 395

finding is consistent with the simulation performed in 2003 by FAA and DFW in cooperation 396

with NASA (1); however, the simulation failed to identify the improvement in arrival capacity 397

identified in this study. The ASDE-X data reveals its flexibility for use in empirically validating 398

simulated or modeled results. Although this study uses an earlier study’s methodology for 399

determining the daily maximum throughput, the results of these two studies are not comparable 400

because in the meantime the Area Navigation (RNAV) departure and arrival procedures, which 401

have improved DFW’s terminal airspace efficiency (15), have been implemented. 402

Although the observed mean DMAD is not statistically significant, the improvement in 403

departure delay appears to be tangible. In fact, despite the increase in observed departure 404

demand after the EAT, the mean DMAD has reduced from eleven minutes to about seven 405

minutes. For all these reasons, this retrospective empirical study demonstrates the improvement 406

in operational efficiency that result from the reduction in runway crossings. 407

These safety and capacity benefits by themselves show promise for justifying the EAT’s 408

construction, and if the number of runway 17C arrivals using the EAT would increase, the 409

observed effectiveness in improving surface operation safety and in increasing arrival and 410

departure capacity would likely increase further. 411

Finally, study provides opportunities for future research based on ASDE-X data; these 412

projects may include comparisons between actual airfield performance with the estimated results 413

of the models typically used by the FAA (16), and other models under development. This study 414

can be extended to evaluate the entire airfield performance, and the factors impacting the 415



throughput, such as the fleet mix (8) can be examined more closely to determine their importance 416

and magnitude. 417

In an effort to seek future research on EAT performance, the authors compared the taxi 418

times for aircraft landing on runway 17L and using different routes to reach terminal C, as shown 419

in Figure 5. 420

421

422 423

Figure 5 Examples of taxi routes. 424

425

Taxi time for the aircraft routed via taxiway “EL” was 8 minutes and 33 seconds, while 426

the aircraft using the EAT took 9 minutes and 50 seconds, eight seconds less than the taxi time 427

for the aircraft using taxiway “ER”. Although these are only three examples of taxi times and do 428

not prove any assumption, they show that this EAT route can have similar taxi times to runway 429

crossing taxi routes. Most importantly, these examples confirm that EAT operations deserve 430

further investigation to assess its impacts on the arrival delay, which is a critical performance 431

measure for all air carriers. 432

Based on a detailed analysis of taxi times, the operational costs for air carriers and the 433

environmental impacts associated with EAT use can be estimated to complete the assessment of 434

this innovative infrastructure. The results of all of these studies, which integrate the safety and 435

operational benefits together, may also be used by the FAA and the airports to evaluate or to 436

validate the cost-effectiveness of the EATs. 437

438

439

ACKNOWLEDGEMENTS 440

441

FAA Southwest Region Office, Fort Worth, TX 442

443

FAA/NASA North Texas Research Station, Fort Worth, TX 444

445

Mosaic ATM, Inc., Leesburg, VA 446

447

Taxiway EL

Taxiway ER

EAT



REFERENCES 448

1) VFR 2030: Vision of The Future. Realized. Dallas/Fort Worth International Airport, 2009 449

update. 450

2) Engelland S., Analysis of Perimeter Taxiway Operations. Presented at the 10th AIAA 451

Aviation Technology, Integration, and Operations (ATIO) Conference. 452

3) FAA Airport Obstructions Standards Committee (AOSC) Decision Document #06 Summary 453

Approved: June 8, 2005 Dallas / Fort Worth (DFW) End-Around Taxiway System 454

4) Satyamurti, S. D., Mattingly, S.P., Flight Track Data Analysis to Estimate Aircraft Height 455

Above a Perimeter Taxiway System. Presented at 87th

Annual Meeting of the Transportation 456

Research Board, Washington, D.C., 2008. 457

5) Federal Aviation Administration, Airport Diagrams online database, accessed on May 1, 458

2012. www.faa.gov/airports/runway_safety/diagrams/. 459

6) N DFW ATCT 7110.518 – Southeast End-Around Taxiway Operations and RY 17R/17C 460

Departures 461

7) Airport Surface Detection Equipment, Model X (ASDE-X), Federal Aviation Administration 462

www.faa.gov/air_traffic/technology/asde-x/. Accessed May 12, 2012. 463

8) Simaiakis, Ioannis; Balakrishnan, Hasmsa; Khadilkar, Harshad; Reynolds, Tom G.; 464

Hansman, R. John; Reilly, B.; Urlass, S. Demonstration of Reduced Airport Congestion 465

Through Pushback Rate Control. http://18.7.29.232/handle/1721.1/60882. Accessed on May 466

22, 2012 467

9) Balakrishnan H., Jung Y., A Framework for Coordinated Surface Operations Planning at 468

Dallas-Fort Worth International Airport. American Institute of Aeronautics and Astronautics 469

Conference. Hilton Head, NC, August 2007. 470

10) Hansen, M., Tsao, J., Huang, S., Wei, W., Empirical Analysis of Airport Capacity 471

Enhancement Impacts: A Case Study of DFW Airport. Presented at 78th Annual Meeting of 472

the Transportation Research Board, Washington, D.C., 1999. 473

11) Mattingly S., Massidda A., Preliminary Study on the Safety Impact of the End-Around 474

Taxiway at the Dallas/Fort Worth International Airport using FAA/NASA Electronic Data. 475

Final Report, May 2012. 476

12) Weatherunderground.com weather history, http://www.wunderground.com/history/. 477

Accessed from December 2011 to July 2012. 478

13) Nisen J., A simple method of computing the sample size for Chi-square test for the 479

equality of multinomial distributions. Computational Statistics and Data Analysis, Elsevier, 480

2008. 481

14) Federal Aviation Administration, Order 5200.11. Effective 08/30/2010. 482

15) Mayer, R., Evaluation of RNAV Departure Operations at Dallas Fort-Worth International 483

Airport. Presented at the 25th Digital Avionics Systems Conference, 2006 IEEE/AIAA. 484

Portland, OR, 15-19 Oct. 2006. 485

16) Federal Aviation Administration, Advisory Circular (AC) 150/5060-5 (Change 2), 486

Airport Capacity and Delay. 487

17) Davis, William “Perimeter Taxiways and Improved Surface Safety,” Office of Runway 488

Safety, FAA, Washington D.C., 2002 489

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