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.     

Comparing the impact of future airline network change on emissions in India and the United States

In this paper we use simulation to analyze how flight routing network structure may change in different world regions, and how this might impact future traffic growth and emissions. We compare models of the domestic Indian and US air transportation systems, representing developing and mature air transportation systems respectively. We explicitly model passenger and airline decision-making, capturing passenger demand effects and airline operational responses, including airline network change. The models are applied to simulate air transportation system growth for networks of 49 airports in each country from 2005 to 2050. In India, the percentage of connecting passengers simulated decreases significantly (from over 40% in 2005 to under 10% in 2050), indicating that a shift in network structure towards increased point-to-point routing can be expected. In contrast, very little network change is simulated for the US airport set modeled. The simulated impact of network change on system CO₂ emissions is very small, although in the case of India it could enable a large increase in demand, and therefore a significant reduction in emissions per passenger (by nearly 25%). NO_x emissions at major hub airports are also estimated, and could initially reduce relative to a case in which network change is not simulated (by nearly 25% in the case of Mumbai in 2025). This effect, however, is significantly reduced by 2050 because of frequency competition effects. We conclude that network effects are important when estimating CO₂ emissions per passenger and local air quality effects at hub airports in developing air transportation systems.     

The environmental effects of airline carbon emissions taxation in the US

This study investigates how air traffic emissions taxes may impact carbon emissions in the US. The magnitude of emissions savings in the US domestic airline industry that would result from lower demand for air travel as taxes are levied and air fares increase is estimated. At the same time, the air-automobile substitution effect is considered and it is argued that some air travelers may divert to automobiles, thus increasing automobile carbon emissions. Both the analysis of the aggregate US domestic airline industry and the study of a sample of US domestic route markets indicate that potentially sizeable increases in automobile traffic and related emissions may substantially reduce the environmental benefits of air travel carbon emissions taxes. In some instances, carbon emissions may even increase in short-haul markets. Sensitivity analyses are performed to demonstrate the robustness of these findings.     

An aggregate demand model for intercity passenger travel in Sri Lanka

Inspite of the inherent weaknesses in aggregate demand models, they continue to be used in everyday applications, especially in developing countries. The largely data intensive disaggregate model preclude its application in many cases. This paper attempts the formulation and calibration of an aggregate total demand model for estimating inter-district passenger travel by public transport in Sri Lanka. In its process, an investigation is made of the common problems in the aggregate approach while examining possible remedial measures to improve the accuracy and (hence) the usability of the aggregate model. It is argued that commonly used variables and functional forms are inappropriate for making accurate estimates in developing countries. Consequently, the model calibration is shown to incorporate variables representing urbanisation, under-development, transfers, a mode-abstract cost function and intrinsic features. The necessity for functional form for each variable to be based on behavioral assumptions that are tested using the Box-Cox transformation for ensuring the best fit of the data is also observed. Although, the model form was calibrated for Sri Lanka, the model is generalised in order for its applications to other countries as well as, both, inter-district and intercity travel demand estimation.     

Freight transportation demand: A survey of recent econometric studies

This paper surveys econometric studies of freight transportation demand which have been published since the mid-1970s. It describes the variables, data sources, and estimation procedures utilized by the studies. In addition, it summarizes their statistical results. The studies included in this survey typically accounted for freight rates and service characteristics (e.g., transit time and reliability). Data sources often varied across the studies.Based on the data they utilized, the surveyed studies are classified as either aggregate or disaggregate. The data in the aggregate studies consist of information on total flows by modes at the regional or national level, while the data in the disaggregate studies pertain to individual shipments. The earlier aggregate studies estimated linear logit models. It has been pointed out that when they are estimated on aggregate data these models are subject to certain shortcomings. To avoid these shortcomings, more recent aggregate studies have estimated flexible forms such as translog functions. The disaggregate studies surveyed in this paper used either logit or probit models.Statistical results often varied with the commodities analyzed, making it somewhat difficult to generalize the findings of the different studies. One finding common to several studies is that freight rates have a significant impact on shipment decisions. This paper discusses certain theoretical and empirical limitations of the surveyed studies. It also offers suggestions for future research in freight transport demand. Freight demand models can be used to examine various effects of the recent deregulation in freight transportation.     

The asymmetric effects of service quality on passengers' choice of carriers for international air travel

The asymmetric effects of service quality on international travelers' airline choice behavior were investigated in this paper. The linear structural equation modeling system was first applied to quantify passengers' perceptions of service quality provided by airlines, which were then used as the input variables of the asymmetric response model developed to capture international travelers' airline decision. Their asymmetric responses to various service quality of airlines were assumed to be either gains or losses with respect to their reference points. The results showed that the asymmetric response model performs better than the traditional logit models that capture only symmetrical effects.     

A derived demand model of regional highway diesel fuel use

A regional, econometric model of heavy truck diesel fuel use is derived based on the theory of production. Input demand functions for new trucks and diesel fuel are specified and estimated. A simple, logistic scrappage model is estimated and used to estimate total heavy truck stocks and diesel-engine heavy truck stocks. Demand equations based on the AIDS almost ideal demand system flexible form cost function are estimated for new heavy truck demand and regional highway diesel fuel demand. New heavy truck demand is found to be elastic with respect to GNP, inelastic with respect to own price, and appears extremely sensitive to short term GNP trends. The short run price elasticity of diesel fuel demand is found to be very small.     

A model to forecast aircraft operations at general aviation airports

Forecasting the demand for aviation activities is an important task in economic planning. Traditionally, forecasting the demand for general aviation airports has been done using trend and ratio analyses. Both techniques ignore the changes in many external factors that influence aviation activities. This paper develops an econometric model that relates the number of aircraft operations at a general aviation airport to some socioeconomic characteristics and supply variables. The model was estimated using data for 82 airports in the state of Georgia. The results of estimating the model suggest that the demand is inelastic with respect to total employment. The results also show that presence of aviation related services such as avionics, charter flights, rental, repair and crop dusting are important factors in determining aviation activities. The demand seems to be responsive to the location of the county as a tourist/recreational destination.     

Integrated airline planning: Robust update of scheduling and fleet balancing under demand uncertainty

The airline schedule planning problem is defined as the sequence of decisions that need to be made to obtain a fully operational flight schedule. Historically, the airline scheduling problem has been sequentially solved. However, there have already been many attempts in order to obtain airline schedules in an integrated way. But due to tractability issues it is nowadays impossible to determine a fully operative and optimal schedule with an integrated model which accounts for all the key airline related aspects such as competitive effects, stochastic demand figures and uncertain operating conditions. Airlines usually develop base schedules, which are obtained much time in advance to the day of operations and not accounting for all the related uncertainty. This paper proposes a mathematical model in order to update base schedules in terms of timetable and fleet assignments while considering stochastic demand figures and uncertain operating conditions, and where robust itineraries are introduced in order to ameliorate miss-connected passengers. The proposed model leads to a large-scale problem which is difficult to be solved. Therefore, a novel improved and accelerated Benders decomposition approach is proposed. The analytical work is supported with case studies involving the Spanish legacy airline, IBERIA. The presented approach shows that the number of miss-connected passengers may be reduced when robust planning is applied.     

Airline network choice and market coverage under high-speed rail competition

While the existing literature has focused on the short-term impacts, this paper investigates the long-term impacts of high-speed rail (HSR) competition on airlines. An analytical model is developed to study how an airline may change its network and market coverage when facing HSR competition on trunk routes. We show that prior to HSR competition, an airline is more likely to adopt a fully-connected network and cover fewer fringe markets if the trunk market is large. Under HSR competition, the airline will, for a given network structure, have a greater incentive to cover more fringe (regional or foreign) markets if the trunk market is large, or the airline network is close to hub-and-spoke. Further, the airline will, for any given market coverage, move towards a hub-and-spoke network when the trunk market is large, or the number of fringe markets covered by the airline network is large. Both effects are more prominent when the decreasing rate of airline density economies is large. We further show that HSR competition can induce the airline to adopt network structure and market coverage that are closer to the socially optimal ones, thereby suggesting a new source of welfare gain from HSR based on its long-term impacts on airlines. Implications for operators, policy makers and specific countries (such as China) are also discussed.     

Uncertain demand,modal competition and optimal price-capacity adjustments in air transportation

A new microeconomic model for the operation of an airline facing modal competition with uncertain total demand is developed to analyze optimal price capacity combinations. The novelty is the treatment of the capacity restriction, which is not viewed as affecting negatively individual preferences (e.g. probability of a full flight), but does influence aggregate utility. A mode choice model is used to represent unrestricted individual preferences assuming full availability (phone call demand); air capacity is treated as a variable that acts on the actual choice set. Restricted choices and total demand stochasticity are integrated in welfare calculations (users' benefits and profits). Numerical examples are given and results are analyzed in terms of load factors fare levels, and sensitivity to the stochasticity of requests.This research was partially funded by FONDECYT, Chile, Direction Génerale de l'Aviation Civile, France, the Andes Foundation and the Fulbright Commission.     

A multiregional optimization model for allocating trasnportation investments

This paper presents a multiregional optimization model which explicitly considers the direct and indirect relationships between regional growth and investments in transportation infrastructure. Consumption, demand and investments for each sector and region are derived endogenously. Trade flows are simulated by a gravity function and transportation network investment decisions are represented by 0–1 integer variables. Despite its complex structure the model can be estimated by applying in two stages the Benders Partitioning Algorithm. The model is applied to Greece to obtain a comprehensive investment plan for the transportation system.     

Airline market share and customer service quality: a reference-dependent model

Traditional models that explain the nature of the relationship between customer service quality and airline demand assume that the relationship can be approximated by using smooth or differentiable curves. Suzuki and Tyworth, however, recently argued that this assumption may not be valid, and that, if it is invalid, the model performance can be improved by using non-smooth functions to represent the relationship (Suzuki, Y., Tyworth, J.E., 1998. A theoretical framework for modeling sales-service relationships in the transportation industry. Transportation Research E 34 (2), 87–100). We use their framework to develop a model that represents the relationship between service quality and market share in the airline industry and then empirically compare its performance with conventional airline demand models. The results indicate that the relationship is characterized by a non-smooth curve and that our model provides a significantly better goodness of fit than other conventional demand models.     

Management's role in U.S. rail passenger decline, 1920–1941

Opinions vary as to whether the decline of American railroad passenger service can be attributed primarily to consumer choice or partly to structural impedances to the supply. Results are reported from testing the hypothesis that railroad management supplied service inappropriate in the new motor era: that management catered to a small, high-priced market, whereas it should have catered to a mass, low-priced market. An aggregate demand model with non-linear elasticity characteristics is estimated on railroad traffic between a sample of American cities for 1933. The model is sensitive to speed, fare and headway variables under the control of railroad management and reveals that there was a unique fare for a service of a given speed that maximized gross revenues. The observed fare for most of the 187 cases in the study was near or below the optimal fare, showing that rail managers judged their markets well, at least in the short run.     

Optimization of grid bus transit systems with elastic demand

Current analytic models for optimizing urban bus transit systems tend to sacrifice geographic realism and detail in order to obtain their solutions. The models presented here shows how an optimization approach can be successful without oversimplifying spatial characteristics and demand patterns of urban areas and how a grid bus transit system in a heterogeneous urban environment with elastic demand is optimized. The demand distribution over the service region is discrete, which can realistically represent geographic variation. Optimal network characteristics (route and station spacings), operating headways and fare are found, which maximize the total operator profit and social welfare. Irregular service regions, many‐to‐many demand patterns, and vehicle capacity constraints are considered in a sequential optimization process. The numerical results show that at the optima the operator profit and social welfare functions are rather flat with respect to route spacing and headway, thus facilitating the tailoring of design variables to the actual street network and particular operating schedule without a substantial decrease in profit. The sensitivities of the design variables to some important exogenous factors are also presented.     

Modeling the competition among air-travel itinerary shares: GEV model development

This study reports the results of aggregate air-travel itinerary share models estimated using data from all East West markets in the United States and Canada. These models predict airline ridership at the itinerary level and aid carriers in long and intermediate term decision-making. Official and comprehensive schedule and bookings data is used to estimate generalized extreme value models capturing the inter-itinerary competition dynamic along three dimensions: time of day, carrier and level-of-service (nonstop, direct, single-connect, double-connect). Models incorporate one, two or three of these dimensions simultaneously. Model structures considered include multinomial logit and variations of the nested logit model (two-level nested logit, two-level weighted nested logit, three-level nested logit, three-level weighted nested logit and nested weighted nested logit). Independent variables for the models measure various itinerary service characteristics such as level-of-service, connection quality, carrier attributes, aircraft type, and departure time. Additionally, the advanced models yield inverse logsum and/or weight parameter estimates capturing the underlying competitive dynamic among air-travel itineraries. The results are intuitive, and the advanced models outperform the more basic specifications with regard to statistical tests and behavioral interpretations, giving insight into the competitive dynamic of air-carrier itineraries.     

Environmental benefits from better freight transport management: freight traffic in a VAR model

Most macro empirical studies on the price elasticity in the freight transport sector focus on the demand for transport. However, most of the external costs from the freight transport sector are more closely linked with the traffic volumes. In the paper an explicit distinction between traffic and transport demand is made by regarding traffic as an input in the shippers’ production of transport services, while transport demand is derived from firms’ production of output. An empirical analysis based on aggregate time series is carried out in a VAR model, where the stationary long run relationships are estimated using the so-called ‘Johansen Procedure’. The estimated price elasticity with respect to traffic (−0.81) is considerably higher than the elasticity with respect to transport (−0.47).     

Analysis of airline operating strategies under stochastic demand

Variability in the demand for air travel is studied with respect to stochastic and time varying components which affect the distribution of passengers who will want to travel on a particular day. The problem is then cast as one of optimizing the total capacity offered in a given time period using operating net revenues as the objective. The distribution of demand is assumed to be normal, and passengers lost on those days when demand is high are assumed to travel on another airline or a different mode. An airline must therefore balance the costs of increased capacity against the benefits of carrying more people. Tradeoffs are studied parametrically, with particular emphasis on the effect the variance of the distribution of demand has on different relationships. Also shown is a simple procedure for estimating actual market mean and variance given the mean and variance of the number of passengers actually flown, which has a truncated distribution.     

Estimating the demand for urban bus travel

The disadvantages of conventional transportation study models, in particular their large data requirements and their weaknesses in dealing with changes in trip generation rates have led to a need for a simple model that can quickly and at low cost examine alternative public transport strategies.This paper investigates simple economic models of bus demand, examines alternative variables that can be used and discusses some alternative model forms. It demonstrates the results of a model using data from twelve urban bus operators in Britain and compares the results with those from other types of study. The model utilises fare and service quality elasticities to explain the decline in passengers on urban bus services, and derives an average elasticity with respect to fare changes of –0.31 and with respect to service quality changes of +0.62. It is estimated that fare rises accounted for 13% of the 43% decline in passengers over the last fifteen years, vehicle mileage reductions for 14.3% and that only 15.7% was due to such factors as rising car ownership which are often given as the cause of declining bus patronage.The results, by showing that passengers are far more sensitive to changes in service than they are to fare rises, are a useful guide to the broader public transport policy issues, and the paper concludes that the model does provide a useful method of forecasting public transport demand at a strategic level. Further work is needed, however, to establish more accurate forecasts for different types of passenger and studies are now being undertaken to establish these and to construct an operational forecasting model that can be applied with only limited data requirements     

Evaluating optimal multi-hub networks in a deregulated aviation market with an application to Western Europe

This paper develops, evaluates and ultimately aids in the choosing of an optimal, single allocation, hub-and-spoke network for an airline working in a deregulated market. An integer linear program evaluates potential hub network combinations, whose profits are then determined using a non-linear mathematical program. International gateway airports and regional hubs, profit, frequency and aircraft size are the decision variables. An adapted, conjugate-gradient projection algorithm is developed and the models are subsequently applied to Western Europe.