枢纽机场航线网络优化研究

枢纽机场航线网络优化主要解决由于航线网络结构与功能定位不匹配而导致的机场连通性低、航线网络同质化竞争严重、运行效率低下的问题。通过改进引力模型对城市对间的客流量进行预测,以此为预测的客流量为依据之一,以提高机场连通性为目的,构建航线网络优化模型,并进行求解。实现提高枢纽机场连通性、构建符合功能定位的层级网络的目标。并以位于我国中部,具有"连接南北,贯穿东西"地理优势的西安咸阳国际机场为例进行分析。由于国际航线受客观因素较多,本文主要研究国内客运航线,国际及货运不在本文研究之列。     

基于功能定位的国际航空枢纽评价

针对我国国际枢纽机场未按照设计之初功能定位发展的现状,运用AHP法构建国际枢纽机场综合评价指标体系,将国际航空运输规模指标、国际节点网络连通度指标、枢纽功能指标、综合交通指标纳入体系当中。首先对国内外大型国际枢纽机场进行评价,其次对我国机场布局规划中的10个国际枢纽机场进行评价。结果表明我国国际枢纽机场国际业务发展、航空枢纽建设与国外对标机场有一定差距,与自身战略规划有所偏差,针对薄弱指标项基于自身优势给出发展建议。最后基于一市两场与机场群的视角,分析了如何根据自身功能定位进行协同发展问题。     

Modelling the resilience,friability and costs of an air transport network affected by a large-scale disruptive event

This paper deals with developing a methodology for estimating the resilience, friability, and costs of an air transport network affected by a large-scale disruptive event. The network consists of airports and airspace/air routes between them where airlines operate their flights. Resilience is considered as the ability of the network to neutralize the impacts of disruptive event(s). Friability implies reducing the network’s existing resilience due to removing particular nodes/airports and/or links/air routes, and consequently cancelling the affected airline flights. The costs imply additional expenses imposed on airports, airlines, and air passengers as the potentially most affected actors/stakeholders due to mitigating actions such as delaying, cancelling and rerouting particular affected flights. These actions aim at maintaining both the network’s resilience and safety at the acceptable level under given conditions.Large scale disruptive events, which can compromise the resilience and friability of a given air transport network, include bad weather, failures of particular (crucial) network components, the industrial actions of the air transport staff, natural disasters, terrorist threats/attacks and traffic incidents/accidents.The methodology is applied to the selected real-life case under given conditions. In addition, this methodology could be used for pre-selecting the location of airline hub airport(s), assessing the resilience of planned airline schedules and the prospective consequences, and designing mitigating measures before, during, and in the aftermath of a disruptive event. As such, it could, with slight modifications, be applied to transport networks operated by other transport modes.     

Reprint of “Modelling the resilience,friability and costs of an air transport network affected by a large-scale disruptive event”

This paper deals with developing a methodology for estimating the resilience, friability, and costs of an air transport network affected by a large-scale disruptive event. The network consists of airports and airspace/air routes between them where airlines operate their flights. Resilience is considered as the ability of the network to neutralize the impacts of disruptive event(s). Friability implies reducing the network’s existing resilience due to removing particular nodes/airports and/or links/air routes, and consequently cancelling the affected airline flights. The costs imply additional expenses imposed on airports, airlines, and air passengers as the potentially most affected actors/stakeholders due to mitigating actions such as delaying, cancelling and rerouting particular affected flights. These actions aim at maintaining both the network’s resilience and safety at the acceptable level under given conditions.Large scale disruptive events, which can compromise the resilience and friability of a given air transport network, include bad weather, failures of particular (crucial) network components, the industrial actions of the air transport staff, natural disasters, terrorist threats/attacks and traffic incidents/accidents.The methodology is applied to the selected real-life case under given conditions. In addition, this methodology could be used for pre-selecting the location of airline hub airport(s), assessing the resilience of planned airline schedules and the prospective consequences, and designing mitigating measures before, during, and in the aftermath of a disruptive event. As such, it could, with slight modifications, be applied to transport networks operated by other transport modes.     

Congestion spill effects of Heathrow and Frankfurt airports on connection traffic in European and Gulf hub airports

We develop two stage fixed-effects single-spill and double-spill models for congestion connection spills of London Heathrow and Frankfurt airports on 9 hub airports in Europe and the Gulf. Our panel data covers connection traffic from 1997 to 2013 for Heathrow and 1997 to 2011 for Frankfurt. The single-spill results support strongly that the connection spills from Heathrow’s capacity limitations do strengthen competing hub airports of major alliance groups and to a lesser degree one Gulf hub. The double-spill model for Heathrow and Frankfurt shows nearly asymmetric overall spill characteristics between the two airports. Our results underline the influence of airline network strategies on congestion spills as European airline networks are shaped by alliances and umbrella mergers. Thus, the airline network perspective in airport capacity expansion decisions needs to play a greater role, as indicated by our asymmetric results for overall spill effects between Heathrow and Frankfurt airports.     

Performance based clustering for benchmarking of US airports

Managing service operations is gaining significant attention in both academic and practitioner circles. In this broad area, performance evaluation and process improvement of airlines and air carriers has been the focus of several studies. Although efficient airport operations are critical for improved performance of airlines and air carriers, few studies have focused on airport performance measurement. This study evaluates the operational efficiencies of 44 major US airports across 5 years using multi-criteria non-parametric models. These efficiency scores are treated by a clustering method in identifying benchmarks for improving poorly performing airports. Efficiency measures are based on four resource input measures including airport operational costs, number of airport employees, gates and runways, and five output measures including operational revenue, passenger flow, commercial and general aviation movement, and total cargo transportation. The methodology presented here can be generalized to other industries and institutions.     

A Heuristic Algorithm for the Allocation of Airport Runway System Capacity

Abstract

This paper develops a heuristic algorithm for the allocation of airport runway capacity to minimise the cost of arrival and departure aircraft/flight delays. The algorithm is developed as a potential alternative to optimisation models based on linear and integer programming. The algorithm is based on heuristic (‘greedy’) criteria that closely reflect the ‘rules of thumb’ used by air traffic controllers. Using inputs such as arrival and departure demand, airport runway system capacity envelopes and cost of aircraft/flight delays, the main output minimises the cost of arrival and departure delays as well as the corresponding interdependent airport runway system arrival and departure capacity allocation. The algorithm is applied to traffic scenarios at three busy US airports. The results are used to validate the performance of the proposed heuristic algorithm against results from selected benchmarking optimisation models.     

Effects of corruption on efficiency of the European airports

The effect of corruption on airport productive efficiency is analyzed using an unbalanced panel data of major European airports from 2003 to 2009. We first compute the residual (or net) variable factor productivity using the multilateral index number method and then apply robust cluster random effects model in order to evaluate the importance of corruption. We find strong evidence that corruption has negative impacts on airport operating efficiency; and the effects depend on the ownership form of the airport. The results suggest that airports under mixed public–private ownership with private majority achieve lower levels of efficiency when located in more corrupt countries. They even operate less efficiently than fully and/or majority government owned airports in high corruption environment. We control for economic regulation, competition level and other airports’ characteristics. Our empirical results survive several robustness checks including different control variables, three alternative corruption measures: International Country Risk Guide (ICRG) corruption index, Corruption Perception Index (CPI) and Control of Corruption Index (CCI). The empirical findings have important policy implications for management and ownership structuring of airports operating in countries that suffer from higher levels of corruption.     

Counting the different efficient paths for transportation networks and its applications

This paper deals with an interesting problem about how to efficiently compute the number of different efficient paths between an origin‐destination pair for a transportation network because these efficient paths are the possible paths used by drivers to some extent. Based on a novel triangle operation derived, it first presents a polynomial‐time combinatorial algorithm that can obtain the number of different simple paths between any two nodes for an acyclic network as well as the total travel cost of these paths. This paper proceeds to develop a combinatorial algorithm with polynomial‐time complexity for both counting the different efficient paths between an origin‐destination pair and calculating the total travel cost of these paths. As for applications, this paper shows that the preceding two algorithms can yield the lower and upper bounds for the number of different simple paths between an origin‐destination pair, while it has already be recognized that a polynomial‐time algorithm getting such a number does not exist for a general network. Furthermore, the latter algorithm can be applied for developing a heuristic method for the traffic counting location problem arising from the origin‐destination matrix estimation problems.     

Determination and Applications of Environmental Costs at Different Sized Airports – Aircraft Noise and Engine Emissions

With the increasing trend of charging for externalities and the aim of encouraging the sustainable development of the air transport industry, there is a need to evaluate the social costs of these undesirable side effects, mainly aircraft noise and engine emissions, for different airports. The aircraft noise and engine emissions social costs are calculated in monetary terms for five different sized airports, ranging from hub airports to small regional airports. The number of residences within different levels of airport noise contours and the aircraft noise classifications are the main determinants for accessing aircraft noise social costs. The environmental impacts of aircraft engine emissions include both aircraft landing and take-off and 30-minute cruise. The social costs of aircraft emissions vary by engine type and aircraft category, depending on the damage caused by different engine pollutants on the human health, vegetation, materials, aquatic ecosystem and climate. The results indicate that the relationship appears to be curvilinear between environmental costs and the traffic volume of an airport. The results and methodology of environmental cost calculation could be applied to the proposed European wide harmonised noise charges as well as the social cost benefit analysis of airports.     

The integration of Taiwanese and Chinese air networks for direct air cargo services

The two sides of the Taiwan Strait perform mutually dependent but complementary activities in the global manufacturing supply-chain. As a result, trade between Taiwan and China grew in double digits annually in the 1990s. With growing economic ties, direct air links are inevitable. In this research, we analyzed government documents and interviewed the air cargo carriers and airlines that currently serve the Taiwan–China air cargo market. This information enabled us to tabulate the trade, estimate the airport-to-airport air cargo demand and calibrate the international and domestic freight tariffs. We used a connectivity measurement and classified Chinese airports into national, regional and local classes in a hub-and-spoke air cargo network. We developed a mathematical model and a branch-and-bound algorithm. The results showed that at least two transit airports are economically necessary for a Taiwan–China air link. Shanghai and Xiamen were always the top two transit airports. The third airport would be Changsha if the decision becomes three air-links. These links are different from the top three passenger transit airports, Fuzhou, Xiamen and Shanghai, even though the cost saving is moderate.     

An aggregate demand model for air passenger traffic in the hub-and-spoke network

In this paper, we build an aggregate demand model for air passenger traffic in a hub-and-spoke network. This model considers the roles of airline service variables such as service frequency, aircraft size, ticket price, flight distance, and number of spokes in the network. It also takes into account the influence of local passengers and social-economic and demographic conditions in the spoke and hub metropolitan areas. The hub airport capacity, which has a significant impact on service quality in the hub airport and in the whole hub-and-spoke network, is also taken into consideration.Our demand model reveals that airlines can attract more connecting passengers in a hub-and-spoke network by increasing service frequency than by increasing aircraft size in the same percentage. Our research confirms the importance of local service to connecting passengers, and finds that, interestingly, airlines’ services in the first flight leg are more important to attract passengers than those in the second flight segment. Based on data in this study, we also find that a 1% reduction of ticket price will bring about 0.9% more connecting passengers, and a 1% increase of airport acceptance rate can bring about 0.35% more connecting passengers in the network, with all else equal. These findings are helpful for airlines to understand the effects of changing their services, and also useful for us to quantify the benefits of hub airport expansion projects.At the end of this paper, we give an example as an application to demonstrate how the developed demand model could be used to valuate passengers’ direct benefit from airport capacity expansion.     

Determinants of airports’ environmental effects

Aviation is a fast growing sector with increasing environmental concerns linked to aircraft emissions at airports and noise nuisance. This paper investigates the factors affecting the annual environmental effects produced by a national aviation system. The environmental effects are computed using certification data for each aircraft-engine combination. Moreover, we also take into account for the amount of environmental effects that is internalized at the airport, mainly through noise regulation. We study a dataset covering information on Italian airports during the period 1999–2008. We show that a 1% increase in airport’s yearly movements yields a 1.05% increase in environmental effects, a 1% in aircraft size (measured in MTOW) gives rise to a 1.8% increase and a 1% increase in aircraft age generates a 0.69% increase in environmental effects. Similar results but with smaller magnitudes are observed if airport internalization is considered. Our policy implications are that the tariff internalizing the total amount of externality is about euro 180 per flight, while the tariff limiting only pollution is about euro 60 and the one reducing noise is about euro 110. Moreover, our airport examples show that managers should prefer to address additional capacity by increasing frequency rather than aircraft size, since the former strategy is more environmental friendly.     

The punctuality performance of aircraft rotations in a network of airports

The aim of this paper is to investigate the influence of aircraft turnaround performance at airports on the schedule punctuality of aircraft rotations in a network of airports. A mathematical model is applied, composed of two sub-models, namely the aircraft turnaround model (turnaround simulations) and the enroute model (enroute flight time simulations). A Markovian type model is featured in the aircraft turnaround model to simulate the operation of aircraft turnarounds at an airport by considering operational uncertainties and schedule punctuality variance. In addition, stochastic Monte Carlo simulations are employed to carry out stochastic sampling and simulations in both the aircraft turnaround model and the enroute model. Results of simulations show the robustness of the aircraft rotation model in capturing uncertainties from aircraft rotations. The propagation of knock-on delays in aircraft rotations is found to be significant when the short-connection-time policy is used by an airline at its hub airport. It is also found that the proper inclusion of schedule buffer time in the aircraft rotation schedule helps control the propagation of knock-on delays and, therefore, stabilize the punctuality performance of aircraft rotations.     

A multi-criteria evaluation of solutions and alternatives for matching capacity to demand in an airport system: the case of London

This paper applies multi-criteria decision-making (MCDM) methods to the evaluation of solutions and alternatives for matching airport system airside (runway) capacity to demand. For such a purpose, ‘building a new runway’ is considered as the solution and candidate airports of the system as alternatives for implementing the solution. The alternative airports are characterized by their physical/spatial, operational, economic, environmental, and social performance represented by corresponding indicator systems which, after being defined and estimated under given operating scenarios, are used as evaluation attributes/criteria by the selected MCDM methods. Two MCDM methods – Simple Additive Weighting and Technique for Order of Preference by Similarity to Ideal Solution – are applied to the case of the London airport system to rank and select the preferred alternative from three candidate airports – Heathrow, Gatwick, and Stansted – for where a new runway could be built.     

The time slot allocation problem under uncertain capacity

This paper presents two stochastic programming models for the allocation of time slots over a network of airports. The proposed models address three key issues. First, they provide an optimization tool to allocate time slots, which takes several operational aspects and airline preferences into account; second, they execute the process on a network of airports; and third they explicitly include uncertainty. To the best of our knowledge, these are the first models for time slot allocation to consider both the stochastic nature of capacity reductions and the problem’s network structure. From a practical viewpoint, the proposed models provide important insights for the allocation of time slots. Specifically, they highlight the tradeoff between the schedule/request discrepancies, i.e., the time difference between allocated time slots and airline requests, and operational delays. Increasing schedule/request discrepancies enables a reduction in operational delays. Moreover, the models are computationally viable. A set of realistic test instances that consider the scheduling of four calendar days on different European airport networks has been solved within reasonable – for the application’s context – computation times. In one of our test instances, we were able to reduce the sum of schedule/request discrepancies and operational delays by up to 58%. This work provides slot coordinators with a valuable decision making tool, and it indicates that the proposed approach is very promising and may lead to relevant monetary savings for airlines and aircraft operators.     

Living with aircraft noise: Airport proximity,aviation noise and subjective wellbeing in England

Airport expansion is an issue of intense public debate due to the potential impacts on climate change and the quality of life of affected local communities. This paper is the first study to analyse the relationships between airports and multiple subjective wellbeing measures, by merging national-level population statistics with noise measurement maps for seventeen English airports. The presence of daytime aviation noise was found to consistently negatively impact on five subjective wellbeing measures. We found a marginal negative association with every additional decibel of aircraft noise. We found no significant association between wellbeing and living within night-time noise contours or living in close airport proximity. We conclude that living under air traffic flight paths has a negative effect on peoples’ overall and momentary wellbeing, equivalent to around half the effect of being a smoker for some wellbeing measures. The subjective wellbeing method findings support wider revealed preference literature showing lower market demand in areas affected by aviation noise.     

Comparative performance analysis of European airports by means of extended data envelopment analysis

Data envelopment analysis (DEA) has become an established approach for analyzing and comparing efficiency results of corporate organizations or economic agents. It has also found wide application in comparative studies on airport efficiency. The standard DEA approach to comparative airport efficiency analysis has two feeble elements, viz. a methodological weakness and a substantive weakness. The methodological weakness originates from the choice of uniform efficiency improvement assessment, whereas the substantive weakness in airport efficiency analysis concerns the insufficient attention for short‐term and long‐term adjustment possibilities in the production inputs determining airport efficiency. The present paper aims to address both flaws by doing the following: (i) designing a data‐instigated distance friction minimization (DFM) model as a generalization of the standard Banker–Charnes–Cooper model with a view to the development of a more appropriate efficiency improvement projection model in the Banker–Charnes–Cooper version of DEA and (ii) including as factor inputs also lumpy or rigid factors that are characterized by short‐term indivisibility or inertia (and hence not suitable for short‐run flexible adjustment in new efficiency stages), as is the case for runways of airports. This so‐called fixed factor case will be included in the DFM submodel of the DEA. This extended DEA—with a DFM and a fixed factor component—will be applied to a comparative performance analysis of several major airports in Europe. Finally, our comparative study on airport efficiency analysis will be extended by incorporating also the added value of the presence of shopping facilities at airports for their relative economic performance. Copyright © 2012 John Wiley & Sons, Ltd.     

Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.