Quantitative Analysis of Uncertainty in Airport Surface Operations

Rappaport, David B.; Yu, Peter; Griffin, Katy; Daviau, Chris

doi:10.2514/6.2009-6987

Cited by 11 publications

(5 citation statements)

References 3 publications

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“…In previous ground movement research, average taxi speeds have been assumed to be influenced only by the aircraft type, however, taxi time research has shown that the aircraft type is not the most influencing factor upon taxi speeds (Ravizza et al 2012;Idris et al 2002;Rappaport et al 2009). Comparisons between ground movement tool results and the status quo at airports have previously been hard to analyse, due to the need for accurate taxi speed data.…”

Section: Taxi Time Predictionmentioning

confidence: 99%

A more realistic approach for airport ground movement optimisation with stand holding

Ravizza

Atkin

Burke

2013

J Sched

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In addition to having to handle constantly increasing numbers of aircraft, modern airports also have to address a wide range of environmental regulations and requirements. As airports work closer and closer to their maximal possible capacity, the operations problems that need to be solved become more and more complex. This increasing level of complexity leads to a situation where the introduction of advanced decision support systems becomes more and more attractive. Such systems have the potential to improve efficient airside operations and to mitigate against the environmental impact of those operations. This paper addresses the problem of moving aircraft from one location within an airport to another as efficiently as possible in terms of time and fuel spent. The problem is often called the ground movement problem and the movements are usually from gate/stands to a runway or vice-versa. We introduce a new sequential graph based algorithm to address this problem. This approach has several advantages over previous approaches. It increases the realism of the modelling and it draws upon a recent methodology to more accurately estimate taxi times. The algorithm aims to absorb as much waiting time for delay as possible at the stand (with engines off) rather than out on the taxiways (with engines running). The impact of successfully achieving this aim is to reduce the environmental pollution. This approach has been tested using data from a European hub airport and it has demonstrated very promising results. We compare the performance of the algorithm against a lower bound on the taxi time and the limits to the amount of waiting time that can be absorbed at stand

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Section: Taxi Time Predictionmentioning

confidence: 99%

A more realistic approach for airport ground movement optimisation with stand holding

Ravizza

Atkin

Burke

2013

J Sched

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“…However, some elements have been considered in the past. Rappaport et al (2009) analysed the effect on taxi times of having to reduce speed for turns and it was shown that aircraft travelling straight forward reached higher average speeds than those with upcoming turns. In addition, Idris et al (2002) performed a statistical analysis of departing aircraft at Boston Logan International Airport with the conclusion that the taxi-out time for each airline/runway configuration combination was highly dependent upon the take-off queue size.…”

Section: Introductionmentioning

confidence: 99%

A combined statistical approach and ground movement model for improving taxi time estimations at airports

Ravizza

Atkin

Maathuis

et al. 2013

Journal of the Operational Research Society

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With the expected continued increases in air transportation, the mitigation of the consequent delays and environmental effects is becoming more and more important, requiring increasingly sophisticated approaches for airside airport operations. Improved on-stand time predictions (for improved resource allocation at the stands) and take-off time predictions (for improved airport-airspace coordination) both require more accurate taxi time predictions, as do the increasingly sophisticated ground movement models which are being developed. Calibrating such models requires historic data showing how long aircraft will actually take to move around the airport, but recorded data usually includes significant delays due to contention between aircraft. This research was motivated by the need to both predict taxi times and to quantify and eliminate the effects of airport load from historic taxi time data, since delays and re-routing are usually explicitly considered in ground movement models. A prediction model is presented here that combines both airport layout and historic taxi time information within a multiple linear regression analysis, identifying the most relevant factors affecting the variability of taxi times for both arrivals and departures. The promising results for two different European hub airports are compared against previous results for US airports.

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“…Studying Variations in taxi speeds showed significant increases in ground delay for departures. The works [25] and [26] reflected the strong impact of taxi time uncertainty on the airport's efficiency.…”

Section: Uncertainties Of Taxiing Timementioning

confidence: 99%

“…And to cope with the uncertainty of taxi time, [28] used an increased temporal separation between aircraft. In [26] authors reflect the strong impact of taxi time uncertainty on the airport's efficiency. [15] Showed that Uncertainty in taxi times can be explicitly recognized and tackled, they Suggest that punctuality figures should be adjusted to use take-off times rather than push-back times, and [27] showed that Adaptive Mamdani fuzzy rule-based system, generating multiple routes for different levels of uncertainty, reduced delays by 10-20% over the original QPPTW, he proved that at Manchester Airport, And [29] at Zurich Airport.…”

Section: The Saved Time By Applying Ctxtmentioning

confidence: 99%

Efficient Management of Aircraft Taxiing Phase by Adjusting Speed Through Conflict-Free Routes

Idrissi

Bikir²,

Mansouri³

2022

Stat., optim. inf. comput.

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Air traffific congestion is considered to be the main problem in air traffific management. It represents a real handicap in the current rising air traffific flflows without a corresponding enhancement in airport infrastructure. This issue leads to more workloads for air traffific controllers, air stakeholders and other airport operations. The following paper aims to minimize the departure aircraft taxiing time in the movement area. This duration can be affected by several factors such as routing, taxiing speed, holding while taxiing ... etc. In this work, we are going to solve the previous problem by using a tactical planning tool: it consists on assigning effificient and nonstop routes to the scheduled traffific on departure. This tool, using a real-time algorithm, will detect in advance routing conflflicts and solve them before aircraft leave stands and approach the hotspots in taxing network. Furthermore, the proposed method will optimize the use of the available ground network by acting on the taxiing speed in order to reduce departures’ delays, fuel consumption and gas emissions.

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Quantitative Analysis of Uncertainty in Airport Surface Operations

Cited by 11 publications

References 3 publications

A more realistic approach for airport ground movement optimisation with stand holding

A more realistic approach for airport ground movement optimisation with stand holding

A combined statistical approach and ground movement model for improving taxi time estimations at airports

Efficient Management of Aircraft Taxiing Phase by Adjusting Speed Through Conflict-Free Routes

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