To Drive or Fly: Will Driverless Cars Significantly Disrupt Commercial Airline Travel?

In the past, commercial airlines and automobiles have shared a symbiotic relationship and rarely compete directly with each other except for very short flights. However, with driverless vehicles on the horizon, many of which will be made available to the average American consumer within a few years, the airline industry may find that they are now facing a competitor that is unlike anything they have seen in the past. In the current paper, we analyze some of the issues that the airline industry will encounter, and provide consumer survey data that shows that at least 10% of the flying public will switch to driverless vehicles once they realize the advantages that driverless cars offer over commercial flight. These numbers may snowball as the airline industry contracts, particularly for airlines that use the hub and spoke model. We discuss the implications of these potential changes

Optimization of Hybrid Hub-and-Spoke Network Operation for Less-Than-Truckload Freight Transportation considering Incremental Quantity Discount

This paper presents a mixed integer linear programming model (MILP) for optimizing the hybrid hub-and-spoke network operation for a less-than-truckload transportation service. The model aims to minimize the total operation costs (transportation cost and transfer cost), given the determined demand matrix, truck load capacity, and uncapacitated road transportation. The model also incorporates an incremental quantity discount function to solve the reversal of the total cost and the total demand. The model is applied to a real case of a Chinese transportation company engaged in nationwide freight transportation. The numerical example shows that, with uncapacitated road transportation, the total costs and the total vehicle trips of the hybrid hub-and-spoke network operation are, respectively, 8.0% and 15.3% less than those of the pure hub-and-spoke network operation, and the assumed capacity constraints in an extension model result in more target costs on the hybrid hub-and-spoke network. The two models can be used to support the decision making in network operations by transportation and logistics companies

Modeling of the Market of Internal Russian Passenger Air Transportation

In the article the market of passenger air transportation is modelled, as theoretical model the system of the equations demand-supply is  used. Results of assessment of model on data on domestic market  for 2013 show that demand is a little more sensitive to the change in price, than the supply. In addition, according to the received estimates, demand is expected above with a growth of number and the income of the  population and during the periods of the increased tourist activity whereas growth of airport collecting significantly reduces the offer of airlines. In the work, the case analysis of separate effects in  the market of passenger air transportation is carried out also. Results demonstrate that in the absence of other shocks  leaving of Transaero from the market had to lead to increase in  prices on the corresponding routes for 4,6%. In addition, calculation  of potential dynamics of a passenger traffic depending on realization  of one of three scenarios of the taxation in the oil sector has been carried out

Application of Data Mining in Air Traffic Forecasting

The main goal of the study centers on developing a model for the purpose of air traffic forecasting by using off-the-shelf data mining and machine learning techniques. Although data driven modeling has been extensively applied in the aviation sector, little research has been done in the area of air traffic forecasting. This study is inspired by previous research focused on improving the Federal Aviation Administration (FAA) Terminal Area Forecasting (TAF) methodology, which historically assumed that the US air transportation system (ATS) network structure was static. Recent developments use data mining algorithms to predict the likelihood of previously un-connected airport-pairs being connected in the future, and the likelihood of connected airport-pairs becoming un-connected. Despite the innovation of this research, it does not focus on improving the FAA’s existing methodology for forecasting future air traffic levels on existing routes, which is based on relatively simple regression and growth models. We investigate different approaches for improving and developing new features within the existing data mining applications in air traffic forecasting. We focus particularly on predicting detailed traffic information for the US ATS. Initially, a 2-stage log-log model is applied to establish the significance of different inputs and to identify issues of endogeneity and multi-colinearity, while maintaining the simplicity of current models. Although the model shows high goodness of fit, it tested positive for both mentioned issues as well as presenting problems with causality. With the objective of solving these issues, a 3-stage model that is under development is introduced. This model employs logistic regression and discrete choice modelling. As part of future work, machine learning techniques such as clustering and neural networks will be applied to improve this model’s performance

Non-cooperative game theory in measuring strategic interactions between airline joint-venture alliances

Purpose: The paper proposes a research method for measuring strategic interactions between airline joint-venture alliances that compete with each other. Design/Methodology/Approach: The proposed method is based on the non-cooperative game theory with a Nash-Cournot equilibrium. It consists in the development of a model that compares economic performance of airline long-haul, intercontinental operations in two consecutive scenarios, before and after joining an alliance. Findings: A model of strategic interactions between airline joint-venture alliances can be successfully based on the logic of the Nash-Cournot equilibrium. Furthermore, the game theory is an effective tool for analysing economic performance of airline joint business agreements. Practical Implications: The method can be used in measuring bottom line performance of long-haul airline joint business agreements world-wide. For example, on the EU–US airline market, the method can be used in the analysis of the following alliances: United Airlines – Lufthansa Group; American Airlines – International Airlines Group – Finnair; Delta Air Lines – Air France KLM – Virgin Atlantic. Originality/Value: This is a novel approach to research of advanced airline alliance strategies.peer-reviewe

Network Structure and Design in the Deregulated U.S. Airline Industry: an Argument for Re-Regulation?

This paper develops a model to explain and analyze the evolution of network structure (connectivity)and design (flight frequency, aircraft size, prices) in the post-deregulation U.S. airline industry. We show that legacy carriers choice of Hub-and-Spoke networks and the emergence of low cost carriers (LCCs) operating Point-to-Point networks were optimal choices. We demonstrate that LCCs need not necessarily charge lower prices, and their entry impacted legacy carriers’ prices in all markets, even those where there is no direct competition. We show that in response to entry, legacy carriers optimally lower flight frequency, leading to longer wait times between flights for which passengers are compensated by lower prices; conversely, if the entrant later exits, legacy carriers raise flight frequency and therefore prices, which may erroneously appear to be predatory pricing when in fact it is the consequence of optimal network redesign. Finally, we demonstrate that even though low cost carriers lower prices, total social welfare with competing network structures can also be lowered. In other words, the poor financial performance of legacy carriers is not due to their inefficiency per se but due to an efficient Hub-and-Spoke network undermined by competition from inefficient Point-to-Point networks. We argue that social welfare may have been, and still can be, higher if entry and exit in air passenger travel industry is regulated.Networks, Airlines, Regulation

Airline Network Choice and Configuration

As an increasing number of countries liberalize their skies, some airlines, notably carriers in the Middle East, have been able to extend their hub-and-spoke networks beyond domestic borders. This allows them to serve international destinations without going through traditional gateway hubs, so that they can compete with airline alliances relying on the traditional dual-gateway, or the so-called “dog-bone” networks. This paper proposes a stochastic model to investigate the competition between airlines running traditional dog-bone and hub-and-spoke networks in a liberalizing inter-continental market. The proposed model considers the interactions among three types of stakeholders, namely a regulator that aims to maximize the expected social welfare by designating the locations of new gateways; airlines that maximize profits by optimizing the service offerings and airfares; passengers that minimize their own travel disutility. Such a model is applied to analyze the Europe - China aviation market, so that the comparative advantages of different networks can be examined and quantified. The modeling results provide evidence-based recommendations on airline competition and airport development, and infrastructure investment needs in markets being liberlized

Modeling Airline Frequency Competition for Airport Congestion Mitigation

Demand often exceeds capacity at congested airports. Airline frequency competition is partially responsible for the growing demand for airport resources. We propose a game-theoretic model for airline frequency competition under slot constraints. The model is solved to obtain a Nash equilibrium using a successive optimizations approach, wherein individual optimizations are performed using a dynamic programming-based technique. The model predictions are validated against actual frequency data, with the results indicating a close fit to reality. We use the model to evaluate different strategic slot allocation schemes from the perspectives of the airlines and the passengers. The most significant result of this research shows that a small reduction in the total number of allocated slots translates into a substantial reduction in flight and passenger delays and also a considerable improvement in airlines' profits

An explanatory approach to modeling the fleet assignment in the global air transportation system

Airlines’ fleet assignment heavily affects the economic and ecological performance of the global air transportation system (ATS). Consequently, it is inevitable to include potential changes of the fleet assignment when modeling and assessing future global ATS scenarios. Therefore, this article presents a novel explanatory approach to modeling the fleet assignment in the global ATS. The presented approach is based on formulating and solving an optimization problem, which describes the fleet assignment in the ATS through a suitable combination of objective function and constraints. While the objective function combines both the airline and the passenger perspective on the fleet assignment, the constraints include additional operational and technological aspects. In comparison to the available global fleet assignment models in the literature, which rely on statistical approaches, the advantages of the presented approach via an optimization problem lie in the overall scenario capability and the consideration of explicit aircraft types instead of simplifying seat categories. To calibrate and validate our model, we use 10 years of historic flight schedule data. The results underline the strengths and weaknesses of the presented approach and indicate potential for future improvement