Data-driven trajectory-based analysis and optimization of airport surface movement

Ma, Ji; Zhou, Jian; Liang, Man; Delahaye, Daniel

doi:10.1016/j.trc.2022.103902

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…Various optimization and modeling techniques to address problems related to airport surface movement and reduce delays and waiting times are presented in the literature. In one of these studies, a hybrid approach is presented by combining traffic analysis and optimization to improve surface movement at Beijing Capital International Airport, resulting in reduced taxi-in and taxi-out times ( 13 ). In another study, a non-iterative real-time model to minimize waiting times in runway queues, taking into account factors such as the number of sequenced aircraft and operational restrictions, is proposed ( 14 ).…”

Section: Literaturementioning

confidence: 99%

Evaluation of the Trade-Off between Ground Delays and Intersecting Departures under Various Pilot Acceptance Rate Scenarios

Dönmez

2023

Transportation Research Record: Journal of the Transportation R

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The increasing demand for air traffic at airports necessitates the efficient utilization of ground facilities such as runways and taxiways. Intersecting departures, in which one or more aircraft take off from intersecting points on the runway, is a commonly used approach to increase runway capacity and reduce ground delays and taxi times, as well as noise and air pollution. However, the procedure carries potential risks such as runway incursion and excursion. This creates a trade-off between minimizing the number of intersecting departures and minimizing ground delays. In practice, the decision to perform an intersecting departure is ultimately up to the pilot, resulting in uncertainty in the acceptance rate of these types of takeoffs. In this study, a departure sequencing model was developed for a single-runway airport that considers intersecting departures and various pilot acceptance rate scenarios. The primary objective of the model is to minimize total ground delay, including taxi delays, runway holds, and conflict holds. The secondary objective is to minimize the number of intersecting departures by directing the most operationally critical aircraft to the intersection takeoff. The epsilon constraint method—a multi-objective scalarization method—was used to reveal the trade-offs between the objective functions. The results of the model were compared with a traditional scenario that only allows take offs from the beginning of the runway. As a result, average delay savings ranged from 17.1% to 31.5% in various acceptance rate scenarios, as well as average taxi time savings ranging from 4.9% to 8.4% compared with the traditional scenario.

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Section: Literaturementioning

confidence: 99%

Evaluation of the Trade-Off between Ground Delays and Intersecting Departures under Various Pilot Acceptance Rate Scenarios

Dönmez

2023

Transportation Research Record: Journal of the Transportation R

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“…Similar to how the trajectory of aircraft when they are airborne are explored, this approach is highly dependent on data of specific airports. As such, the models associated with this approach are built on data-driven techniques such as data analytics and visualization [43][44][45][46][47], as well as machine learning [48][49][50][51][52]. The machine learning-based techniques are especially useful for trajectory prediction [53][54][55].…”

Section: A Existing Approaches To Arrival Traffic Modelingmentioning

confidence: 99%

A Comparative Analysis of Terminal Area Navigation and Conventional Standard Arrival Routes with Cellular Automata

Ogedengbe

Wong

Liem

2023

AIAA AVIATION 2023 Forum

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Aircraft arrival at airports is limited by the physical configuration and layout of the terminal maneuvering area (TMA), which might result in a bottleneck. In a bottleneck situation, navigation is made difficult because arriving aircraft will need special considerations to manage congestion and guarantee safety. In addition, the operating cost of airline operators increases with higher fuel consumption encountered in the extra airborne time. Likewise, passengers incur more travel time with the additional arrival delays. Traditional arrival adopts conventional standard arrival route (STAR), with trajectories that are rigidly coordinated by air traffic controller (ATC). However, a more flexible approach to general navigation has also been introduced, namely the area navigation (RNAV), which makes provision for flexibility in aircraft path selection. This flexibility is enabled by abstract navigational elements, namely waypoints that are unlike conventional navigational aids (NAVAIDs). We look at a modeling framework that juxtaposes the key elements of the traditional STAR (procedure-based) navigation and RNAV (performance-based) navigation. We adopt the cellular automata (CA) framework that has the capability to model the dynamic interaction between aircraft, inherent environmental uncertainties, and arrival route as defined by waypoints. To this end, we develop a modeling environment that enables a fair comparison between these two approaches under similar conditions to determine their respective viability. The effect of the number of waypoints in the TMA under different traffic conditions is illustrated using the relationship between TMA occupancy, traffic flow, and the landing rate. Results show that RNAV navigation exhibits more sensitivity to waypoint distribution within the TMA in terms of throughput, compared to STAR navigation. STAR navigation, on the other hand, demonstrates inherent latency that allows for higher capacities, albeit at the risk of initiating abrupt jamming when a high number of waypoints are active. Consequently, we are able to establish guidelines that justify the adoption RNAV over STAR and vice versa. Furthermore, we also identify performance disparities between them, and where each approach is desirable.

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“…T single scene N decision step n = scenario solved (13) where T single scene is computing time for a single scenario; N decision step is the number of decision steps per scenario.…”

Section: Performance Of Mcts Combined With Pnnmentioning

confidence: 99%

“…Deep reinforcement learning has significant advantages in addressing sequential decision problems, enabling substantial reductions in response time and facilitating continuous advisory functions for an aircraft. Zhou proposed a Deep Q Network (DQN) model to realize the dynamic control of aircraft taxiing speed in the taxi intersection area, but only considered the situation at a single taxiway intersection [13]; Shin-Lai designed and trained a DQN composed of convolutional neural networks, which is used to capture flight position and action based on images, to resolve conflicts [14]. Hasnain Ali, based on model-free reinforcement learning, used the Proximal Policy Optimization (PPO) algorithm to find an effective departure metering policy, which can alleviate airport traffic congestion and minimize potential conflicts in the taxiing process [15].…”

Section: Introductionmentioning

confidence: 99%

A Conflict Resolution Strategy at a Taxiway Intersection by Combining a Monte Carlo Tree Search with Prior Knowledge

Sui,

Chen,

Zhou

2023

Aerospace

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With the escalating complexity of surface operations at large airports, the conflict risk for aircraft taxiing has correspondingly increased. Usually, the Air Traffic Controllers (ATCOs) generate route, speed and holding instructions to resolve conflicts. In this paper, we introduce a conflict resolution framework that incorporates prior knowledge by integrating a Multi-Layer Perceptron (MLP) neural network into the Monte Carlo Tree Search (MCTS) approach. The neural network is trained to learn the allocation strategy for waiting time extracted from actual aircraft taxiing trajectory data. Subsequently, the action probability distribution generated with the neural network is embedded into the MCTS algorithm as a heuristic evaluation function to guide the search process in finding the optimal conflict resolution strategy. Experimental results show that the average conflict resolution rate is 96.8% in different conflict scenarios, and the taxiing time required to resolve conflicts is reduced by an average of 42.77% compared to the taxiing time in actual airport surface operations.

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Data-driven trajectory-based analysis and optimization of airport surface movement

Cited by 5 publications

References 22 publications

Evaluation of the Trade-Off between Ground Delays and Intersecting Departures under Various Pilot Acceptance Rate Scenarios

Evaluation of the Trade-Off between Ground Delays and Intersecting Departures under Various Pilot Acceptance Rate Scenarios

A Comparative Analysis of Terminal Area Navigation and Conventional Standard Arrival Routes with Cellular Automata

A Conflict Resolution Strategy at a Taxiway Intersection by Combining a Monte Carlo Tree Search with Prior Knowledge

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