# Hub Airport End-Around Taxiway Construction Planning Development: A Review
## 2 Research Methodology and Literature Review
### Contribution of EAT-Increasing Runway Capacity, Reducing Delay
The taxing time is based on [PERSON], [PERSON], [PERSON], and [PERSON] [6; 56], and the calculation of taxi time involves measuring the time it takes for an aircraft to taxi from the moment it leaves the runway after landing until it reaches the edge of the apron.Table 2 shows the taxiing time via traditional crossing and via EATs. The length of the traditional crossing route is not compared, as the traditional crossing taxi route is generally determined by the distance between runways and the distance from the terminal to the runway. Therefore, it is not meaningful to compare different airports. Figure 3 shows an example calculation of EAT length for DTW (Detroit). The analysis selected and compared the lengths of the most frequently used taxi routes, which also represent the primary usage patterns. Different taxiing usage modes should be considered in the initial design, including whether an aircraft, after landing, can taxi in front of another aircraft about to take off (REAT mode, Section 4.3). The taxiing speed is piloted by the aircraft operator, and without air traffic control interventions on the EAT, it enables faster movement compared to traditional runway crossings. Our analysis of major hub airports DFW, DTW, and ATL using ASDE-X data has consistently shown EAT taxiing speeds approximately 30% faster, increasing from 10-12 knots to 16-20 knots [38; 57]. Therefore, when evaluating taxi times, judging solely on routing distance is insufficient, and the variance in speeds must be weighed, as higher velocity on the EAT offsets additional mileage. Moreover, delays for runway crossings must be factored in, whereas the EAT allows uninterrupted taxiing. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ICAO CODES** & **Runway** & **Runway** & **Taxing Time** & **Taxing Time** & **Distance from** & \\
**Number** & **Flow** & **via EAT (min)** & **Taxing Time** & **the Runway End** & **EAT Length** \\  & **Number** & **Flow** & **via EAT (min)** & **Crossing (min)** & **or Threshold to** & **EAT Length** \\ \hline DFW & 17L & South & 10.49 & 8.35 & 800 & 2435 m \\ DTW & 22R & South & 5.95 & 5.18 & 815 & 1797 m \\ ATL & 26R & East & 4.57 & 3.21 & 460 & 1233 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of EAT situations at DFW, DTW, and ATL. Using the 2006 Houston George Bush Intercontinental Airport (IAH) master plan as a case study, [PERSON] and [PERSON] et al. [38; 57; 58] used the SIMMOD software to simulate the impact of the presence or absence of EAT on taxi-in and taxi-out times. Based on the simulated time impact, the Emissions and Dispersion Modeling System (EDMS) was used to analyze the effects of EAT on different pollutants such as CO, hydrocarbons, and CO\({}_{2}\)[58; 59]. It was found that EAT had the most significant contribution to reduced CO emissions [59]. However, based on the estimated taxing distance and speed, the fuel consumption of using EAT was still higher than that of traditional taxiing [60]. In conclusion, the increase in taxiing distance caused by EAT is inevitable, but its fuel consumption and emissions are related to taxing speed, turning, acceleration, and deceleration. Therefore, definitive conclusions regarding EAT's fuel and environmental impact remain uncertain. This also brings challenges to the construction of EAT. However, it is worth noting that the difference in taxiing distance using EAT is significant, and therefore, optimizing the layout of EAT may further reduce taxiing distance and benefit fuel consumption and taxiing time.