Multi-objective routing and scheduling for airport ground movement

Recent research on airport ground movement introduced an Active Routing framework to support multi-objective trajectory-based operations. This results in edges in the airport taxiway graph having multiple costs such as taxi time, fuel consumption and emissions. In such a graph, multiple edges exist between two nodes reflecting different trade-offs among the multiple costs. Aircraft will have to choose the most efficient edge from multiple edges in order to traverse from one node to another respecting various operational constraints. In this paper, we introduce a multi-objective routing and scheduling algorithm based on the enumerative approach that can be used to solve such a multi-objective multi-graph problem. Results using the proposed algorithm for a range of international airports are presented. Compared with other routing and scheduling algorithms, the proposed algorithm can find a representative set of optimal or near optimal solutions in a single run when the sequence of aircraft is fixed. In order to accelerate the search, heuristic functions and a preference-based approach are introduced. We analyse the performance of different approaches and discuss how the structure of the multi-graph affects computational complexity and quality of solutions

Preference-based evolutionary algorithm for airport surface operations

In addition to time efficiency, minimisation of fuel consumption and related emissions has started to be considered by research on optimisation of airport surface operations as more airports face severe congestion and tightening environmental regulations. Objectives are related to economic cost which can be used as preferences to search for a region of cost efficient and Pareto optimal solutions. A multi-objective evolutionary optimisation framework with preferences is proposed in this paper to solve a complex optimisation problem integrating runway scheduling and airport ground movement problem. The evolutionary search algorithm uses modified crowding distance in the replacement procedure to take into account cost of delay and fuel price. Furthermore, uncertainty inherent in prices is reflected by expressing preferences as an interval. Preference information is used to control the extent of region of interest, which has a beneficial effect on algorithm performance. As a result, the search algorithm can achieve faster convergence and potentially better solutions. A filtering procedure is further proposed to select an evenly distributed subset of Pareto optimal solutions in order to reduce its size and help the decision maker. The computational results with data from major international hub airports show the efficiency of the proposed approach

Preference-based evolutionary algorithm for airport runway scheduling and ground movement optimisation

As airports all over the world are becoming more congested together with stricter environmental regulations put in place, research on optimisation of airport surface operations started to consider both time and fuel related objectives. However, as both time and fuel can have a monetary cost associated with them, this information can be utilised as preference during the optimisation to guide the search process to a region with the most cost efficient solutions. In this paper, we solve the integrated optimisation problem combining runway scheduling and ground movement problem by using a multi-objective evolutionary framework. The proposed evolutionary algorithm is based on modified crowding distance and outranking relation which considers cost of delay and price of fuel. Moreover, the preferences are expressed in a such way, that they define a certain range in prices reflecting uncertainty. The preliminary results of computational experiments with data from a major airport show the efficiency of the proposed approach

Optimal speed profile generation for airport ground movement with consideration of emissions

Emissions during the ground movement are mostly calculated based on International Civil Aviation Organisation (ICAO) emission databank. The fuel flow rate is normally assumed as a constant, hence the emission index. Therefore, no detailed discrimination of power settings during ground movement is considered to account for different emissions at different power settings. This may lead to a suboptimal and often unrealistic taxi planning. At the heart of the recently proposed Active Routing (AR) framework for airport ground movement is the unimpeded optimal speed profile generation, taking into account both time and fuel efficiency. However, emissions have not been included in the process of generating optimal speed profiles. Taking into account emissions in ground operations is not a trivial task as not all emissions can be reduced on the same path of reducing time and fuel burn. In light of this, in this paper, a detailed analysis of three main emissions at the airports, viz. CO, Total Hydrocarbon (HC), and NOx, are carried out in order to obtain a minimum number of conflicting objectives for generating optimal speed profiles. The results show that NOx has a strong linear correlation with fuel burn across all aircraft categories. For the heavy aircraft, HC and CO should be treated individually apart from the time and fuel burn objectives. For medium and light aircraft, a strong correlation between HC, CO and time has been observed, indicating a reduced number of objectives will be sufficient to account for taxi time, fuel burn and emissions. The generated optimal speed profiles with consideration of different emissions will have impact on the resulted taxiing planning using the AR and also affect decisions regarding airport regulations

Extracting Multi-objective Multigraph Features for the Shortest Path Cost Prediction: Statistics-based or Learning-based?

Efficient airport airside ground movement (AAGM) is key to successful operations of urban air mobility. Recent studies have introduced the use of multi-objective multigraphs (MOMGs) as the conceptual prototype to formulate AAGM. Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs, however, previous work chiefly focused on single-objective simple graphs (SOSGs), treated cost enquires as search problems, and failed to keep a low level of computational time and storage complexity. This paper concentrates on the conceptual prototype MOMG, and investigates its node feature extraction, which lays the foundation for efficient prediction of shortest path costs. Two extraction methods are implemented and compared: a statistics-based method that summarises 22 node physical patterns from graph theory principles, and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space. The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction, while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs. Three regression models are applied to predict the shortest path costs to demonstrate the performance of each. Our experiments on randomly generated benchmark MOMGs show that (i) the statistics-based method underperforms on characterising small distance values due to severe overestimation, (ii) a subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns, and (iii) the learning-based method consistently outperforms the statistics-based method, while maintaining a competitive level of computational complexity

A Rolling Window with Genetic Algorithm Approach to Sorting Aircraft for Automated Taxi Routing

With increasing demand for air travel and overloaded airport facilities, inefficient airport taxiing operations are a significant contributor to unnecessary fuel burn and a substantial source of pollution. Although taxiing is only a small part of a flight, aircraft engines are not optimised for taxiing speed and so contribute disproportionately to the overall fuel burn. Delays in taxiing also waste scarce airport resources and frustrate passengers. Consequently, reducing the time spent taxiing is an important investment. An exact algorithm for finding shortest paths based on A* allocates routes to aircraft that maintains aircraft at a safe distance apart, has been shown to yield efficient taxi routes. However, this approach depends on the order in which aircraft are chosen for allocating routes. Finding the right order in which to allocate routes to the aircraft is a combinatorial optimization problem in itself. We apply a rolling window approach incorporating a genetic algorithm for permutations to this problem, for real-world scenarios at three busy airports. This is compared to an exhaustive approach over small rolling windows, and the conventional first-come-first-served ordering. We show that the GA is able to reduce overall taxi time with respect to the other approaches

SIMULATION OF INTEGRATED TRANSPORT SYSTEM

Článok sa zaoberá popisom simulačného modelu pre simuláciu pohybu osôb v prestupnom uzle železničná stanica Košice v rámci IDS.In this paper it is presented the simulation model for modelling the passenger flow in tranfer node of Košice railway station within the integrated transport

Towards greener airport ground movement

This presentation gives an overview of airport ground movement research carried out at the School of Engineering, University of Lincoln. In particular, the following researched areas are presented: accurate taxi time prediction, multi-objective ground movement optimisation

A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties

Airport ground movement remains a major bottleneck for air traffic management. Existing approaches have developed several routing allocation methods to address this problem, in which the taxi time traversing each segment of the taxiways is fixed. However, taxi time is typically difficult to estimate in advance, since its uncertainties are inherent in the airport ground movement optimisation due to various unmodelled and unpredictable factors. To address the optimisation of taxi time under uncertainty, we introduce a chance-constrained programming model with sample approximation, in which a set of scenarios is generated in accordance with taxi time distributions. A modified sequential quickest path searching algorithm with local heuristic is then designed to minimise the entire taxi time. Working with real-world data at an international airport, we compare our proposed method with the state-of-the-art algorithms. Extensive simulations indicate that our proposed method efficiently allocates routes with smaller taxiing time, as well as fewer aircraft stops during the taxiing process

Towards a more realistic, cost effective and greener ground movement through active routing: part 1 - optimal speed profile generation

Among all airport operations, aircraft ground movement plays a key role in improving overall airport capacity as it links other airport operations. Moreover, ever increasing air traffic, rising costs and tighter environmental targets create a pressure to minimise fuel burn on the ground. However, current routing functions envisioned in Advanced Surface Movement, Guidance and Control Systems (A-SMGCS) almost exclusively consider the most time efficient solution and apply a conservative separation to ensure conflict free surface movement, sometimes with additional buffer times to absorb small deviations from the taxi times. Such an overly constrained routing approach may result in either a too tight planning for some aircraft so that fuel efficiency is compromised due to multiple acceleration phases, or performance could be further improved by reducing the separation and buffer times. In light of this, Part 1 and 2 of this paper present a new Active Routing framework with the aim of providing a more realistic, cost effective and environmental friendly surface movement, targeting some of the busiest international hub airports. Part 1 of this paper focuses on optimal speed profile generation using a physics based aircraft movement model. Two approaches based respectively on the Base of Aircraft Data (BADA) and the International Civil Aviation Organization (ICAO) engine emissions database have been employed to model fuel consumption. These models are then embedded within a mutli-objective optimization framework to capture the essence of different speed profiles in a Pareto optimal sense. The proposed approach represents the first attempt to systematically address speed profiles with competing objectives. Results reveal an apparent trade-off between fuel burn and taxi times irrespective of fuel consumption modelling approaches. This will have a profound impact on the routing and scheduling, and open the door for the new concept of Active Routing discussed in Part 2 of this paper