One price for all? Price discrimination and market captivity: Evidence from the Italian city-pair markets

This paper tests whether, and to what extent, airlines exploit market captivity by using price discrimination strategies. The Italian passenger market is particularly fit for this purpose, given the high differentials in the degree of the inter-modal competition amongst domestic connections. Results show that, ceteris paribus, airlines adopt a different pricing behaviour depending on the degree of inter-modal market captivity. First, in highly concentrated markets with respect to air competitors, airlines price higher when the inter-modal competition is limited. This proves that inter-modal market captivity strengthens the effect of market power. Second, the inter-temporal price discrimination leads to a J-shaped distribution of fares over time, which is more pronounced when the inter-modal competition is effective. This suggests that airlines need to adopt a pricing technique that allows for a greater market segmentation in order to compete successfully with high-speed rail transport and to extract a larger part of passengers’ surplus. These results are relevant in terms of transport-investment implications and competition policy. The indirect benefits that investments in rail infrastructure would yield through downward pressures on competing airline fares should be embedded in any cost-benefit analysis of high-speed networks investments and in any policy evaluation of measures that aim to reduce the territorial gaps in infrastructure endowment and accessibility.     

On the relationship between airport pricing models

Airport pricing papers can be divided into two approaches. In the traditional approach the demand for airport services depends on airport charges and on congestion costs of both passengers and airlines; the airline market is not formally modeled. In the vertical-structure approach instead, airports provide an input for an airline oligopoly and it is the equilibrium of this downstream market which determines the airports’ demand. We prove, analytically, that the traditional approach to airport pricing is valid if air carriers have no market power, i.e. airlines are atomistic or they behave as price takers (perfect competition) and have constant marginal operational costs. When carriers have market power, this approach may result in a surplus measure that falls short of giving a true measure of social surplus. Furthermore, its use prescribes a traffic level that is, for given capacity, smaller than the socially optimal level. When carriers have market power and consequently both airports and airlines behave strategically, a vertical-structure approach appears a more reasonable approach to airport pricing issues.     

Empirical estimation of price and income elasticities of air cargo demand: The case of Hong Kong

This paper estimates the price and income elasticities of air cargo demand and examines how they may change after the 2008 financial crisis. Using a set of time series data, we simultaneously estimate the aggregated demand and supply functions of air cargo at Hong Kong International Airport (HKIA). We find that during the entire sampling period of 2001–2013, the price elasticity for air cargo transport demand at HKIA ranges from −0.74 to −0.29, suggesting that air cargo demand in Hong Kong reacts negatively to price (as expected) but does not appear to be very sensitive to price. The income elasticity ranges from 0.29 to 1.47 and appears sensitive to seasonality adjustment approaches. However, in terms of the speed of changes, air cargo demand changes much faster than overall economy, indicating the presence of a pro-cyclical pattern of air cargo traffic with respect to the overall economy. Our analysis shows that air cargo demand becomes more sensitive to changes in both price and income after 2008.     

Airport congestion pricing and passenger types

We consider a public and congested airport served by airlines that may have market power, and two types of travelers with different relative values of time. We find that in the absence of passenger-type-based price discrimination by airlines, it can be useful to increase the airport charge so as to protect passengers with a great relative time value from excessive congestion caused by passengers with a low relative time value. As a result, the socially efficient airport charge can be substantially higher than what we learned from the recent literature on congestion pricing with non-atomistic airlines.     

The impact of advance purchase deadlines on airline consumers’ search and purchase behaviors

Airlines frequently use advance purchase ticket deadlines to segment consumers. Few empirical studies have investigated how individuals respond to advance purchase deadlines and price uncertainties induced by these deadlines. We model the number of searches (and purchases) for specific search and departure dates using an instrumental variable approach that corrects for price endogeneity. Results show that search and purchase behaviors vary by search day of week, days from departure, lowest offered fares, variation in lowest offered fares across competitors, and market distance. After controlling for the presence of web bots, we find that the number of consumer searches increases just prior to an advance purchase deadline. This increase can be explained by consumers switching their desired departure dates by one or two days to avoid higher fares that occur immediately after an advance purchase deadline has passed. This reallocation of demand has significant practical implications for the airline industry because the majority of revenue management and scheduling decision support systems currently do not incorporate these behaviors.     

Elasticities of fuel and traffic demand and the direct rebound effects: An econometric estimation in the case of Norway

We estimate the elasticities of fuel and travel demand with respect to fuel prices and income in the case of Norway. Furthermore, we derive the direct rebound effects that explain the degree to which a fuel price increase is “offset” in the form of greater fuel use and/or travel due to improvements in vehicle fuel efficiency. For this purpose, we use and compare two alternative econometric approaches: the error correction model (ECM) and the dynamic model. Our initial assumption is that one should not be indifferent with respect to the approach used to derive elasticities. The data used are for the period 1980–2011. Our results indicate the following: (1) the dynamic model fits the data better than the ECM model does; (2) the estimated elasticities of fuel demand with respect to price and income are −0.26 and 0.06 in the short run and −0.36 and 0.09 in the long run. For travel demand, the respective elasticities are −0.11 and 0.06 in the short run and −0.24 and 0.13 in the long run, implying inelastic demands for fuel and travel demand; and (3) rebound effects indicate that 0.26% and 0.06% of fuel savings as a result of fuel price increase will be offset in the form of more fuel use in the short run and in the long run, respectively, if fuel efficiency increases by 1%. Our policy recommendations are that policies should not be indifferent to the methods used to derive elasticities. We contend that it is crucial to seriously consider rebound effects in policy making because basic elasticity estimates exaggerate the impact of fuel price increases.     

Review and meta-analysis of U.K. time elasticities of travel demand

In contrast with reviews of values of time and price elasticities, the literature contains little by way of detailed reviews of travel time based choice and demand elasticities. This paper reports the most extensive meta-analysis of time-based demand elasticities yet undertaken, supplemented with a review of literature not previously in the public domain. The meta-analysis is based upon 427 direct elasticities covering travel time, generalised journey time (GJT) and service headway and drawn from 69 UK studies. The elasticities are found to vary, as expected, across attributes, and quite strong effects have been detected according to distance. We provide interesting insights into the relationship between long and short run elasticities and elasticities obtained from static models and choice models based on actual and hypothetical preferences. Significantly, the results seem to indicate that the duration for the long run demand impact to work through depends upon the periodicity of the model estimated. There is little variation apparent by journey purpose, source of the evidence, nor over time or by region/flow type, whilst travel time elasticities for high speed rail are not materially different from conventional contexts. The findings support some official elasticity recommendations and conventions but challenge others, and can be used to provide time-based elasticities where none exist or to assess new empirical evidence.     

The influence of quality and price on the demand for urban transport: the case of university students

The aim of this work is to estimate a transport demand function for university students in the Bilbao area and to obtain the main variables that condition this demand. To do this, we use a nested logit model. First, we estimate price and time elasticities, and second the potential effects of changing the available supply of public transport in order to draw new collective transport users away from private vehicles.     

Implementation of cordon pricing on urban network with practical approach

The road pricing is regarded as a transport policy to realize the efficient use of urban network. The network analysis with variable demand has been often applied to describe the network level of congestion pricing on the urban network. In the study, the cordon pricing system is analyzed to implement in the urban area with practical approach. It is assumed that the congestion tolls are collected in crossing the cordon lines on the network. Therefore, the scale of cordon zones and the values of congestion tolls would be determined simultaneously to produce the maximum social net benefit. The combinatorial optimization with unit price is formulated. The genetic algorithm (GA) is applied as a practical method to provide the solutions for the combinatorial optimization problem. As the pattern of cordon pricing is determined, the performance of system is estimated to confirm the applicability. It is concluded that the cordon pricing can be applied with the practical approach.     

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.     

Fuel demand,road transport pollution emissions and residents’ health losses in the transitional China

In recent years, China’s rapid economic growth resulted in serious air pollution, which caused substantial losses to economic development and residents’ health. In particular, the road transport sector has been blamed to be one of the major emitters. During the past decades, fluctuation in the international oil prices has imposed significant impacts on the China’s road transport sector. Therefore, inspired by Li and Zhou (2005), we propose an assumption that China’s provincial economies are independent “economic entities”. Based on this assumption, we investigate the China’s road transport fuel (i.e., gasoline and diesel) demand system by using the panel data of all 31 Chinese provinces except Hong Kong, Macau and Taiwan. To connect the fuel demand system and the air pollution emissions, we propose the concept of pollution emissions elasticities to estimate the air pollution emissions from the road transport sector, and residents’ health losses by a simplified approach consisting of air pollution concentrations and health loss assessment models under different scenarios based on real-world oil price fluctuations. Our framework, to the best of our knowledge, is the first attempt to address the transmission mechanism between the fuel demand system in road transport sector and residents’ health losses in the transitional China.     

Cost and time damping: evidence from aggregate rail direct demand models

There is a significant body of evidence from both disaggregate choice modelling literature and practical travel demand forecasting that the responsiveness to cost and possibly to time diminishes with journey length. This has, in Britain at least, been termed ‘Cost Damping’, and is recognised in guidance issued by the UK Department for Transport. However, the consistency of the effect across modes and data types has not been established. Cost damping, if it exists, affects both the forecasting of demand and our understanding of behaviour. This paper aims to investigate the evidence for cost and time damping in rail demand using aggregate rail ticket sales data. The rail ticket sales data in Britain has, for many years, formed the basis of analysis of a wide range of impacts of rail demand. It records the number of tickets sold between station pairs, and it is generally felt to provide a reasonably accurate reflection of travel demand. However, the consistency of these direct demand models with choice modelling and highway demand model structures has not been investigated. Rail direct demand models estimated by ticket sales data indicate only slight variation in the fare elasticity with distance, as is evidenced in the largest meta-analysis of price elasticities conducted to date (Wardman in J Transp Econ Policy 48(3):367–384, 2014). This study of UK elasticities shows strong variation between urban and inter-urban trips, presumably a segmentation at least in part by purpose, but less remaining variation by trip length. A lack of variation by length supports the hypothesis of cost damping, because constant cost sensitivity would imply that fare elasticity would increase strongly with distance, because of the increasing impact of higher fares at longer distances. In this paper we indicate that rail direct demand models have some consistency of behavioural paradigm with utility based choice models used in highway planning. We go on to use rail demand data to estimate time and fare elasticities in the context of various cost damping functions. Our empirical contribution is to estimate time elasticities on a basis directly comparable with cost elasticities and to show that the phenomenon of cost damping is strongly present in ticket sales data. This finding implies that cost damping should be included in models intended for multimodal analysis, which may otherwise give incorrect predictions.     

Pricing and competition in a shipping market with waste shipments and empty container repositioning

In this paper, we study a shipping market with carriers providing services between two locations. Shipments are classified into two categories: goods and waste. Trade imbalance allows low-valued waste to be shipped at bargain rates. If imbalance persists, empty containers must be repositioned from a surplus location to a shortage location. Carriers decide prices, which will affect the demand. We build a monopoly and a duopoly model to find the optimal pricing strategy for carriers. We also analyze how the profit of a carrier is affected by price sensitivity, cost structure and competition intensity.     

High‐speed rail cost recovery time based on an integer optimization model

With increasing gasoline prices, electric high‐speed rail (HSR) systems represent one means to mitigate overexposure to volatile prices. However, additional research is needed related to funding this infrastructure. In this paper, we develop a new integer optimization model to address this problem and use a hypothetical case study to demonstrate the approach. The objective of the approach is to minimize the time period in which the cost of HSR construction and operation can be recovered. This is an iterative process based on an integer optimization model, whose objective function is to determine the optimum recovery time (ORT), by setting the HSR ticket price and frequency. Embedded in the optimization model is a multinomial logit model for calculating the demand for HSR as a function of these decision variables, thus capturing the effects of level of service on market share. In particular, the optimization model accounts for the role of different types of subsidies toward HSR construction (one‐time subsidies at construction, annual subsidies, and subsidies depending on frequency). This method can also help determine whether an HSR system should be built or how much subsidy should be provided given a fixed expected cost recovery time. By integrating the logit model into the objective function evaluation, the effects of ticket price and service frequency on service demand can be directly captured. Copyright © 2014 John Wiley & Sons, Ltd.     

Estimating fuel demand elasticities to evaluate CO2 emissions: Panel data evidence for the Lisbon Metropolitan Area

This paper estimates fuel demand models for the Lisbon Metropolitan Area (AML) and uses the demand elasticities obtained to predict future levels of road transport CO₂ greenhouse gas emissions. Data for the municipalities constituting the AML and the period 1993–2010 are analysed using static and dynamic panel data models to measure the relative importance of fuel price, income, vehicle stock, the price of public transport, and the availability of urban and suburban rail networks on fuel demand. To the best of our knowledge, this is the first study in the Portuguese context to produce fuel demand elasticities for a specific metropolitan area, as opposed to the estimation of country-level aggregate elasticities. Our findings indicate that the elasticity of fuel demand with respect to fuel price ranges between −0.48 and −0.72 in the short run and between −1.19 and −1.82 in the long run. Income elasticities are found to range between 0.51 and 0.54 in the short run and between 1.26 and 1.37 in the long run. The elasticity of fuel demand with respect to vehicle stock (keeping population constant) is 0.57 in the short run and 1.43 in the long run. There is only weak evidence of a reduction in fuel demand as a result of a decrease in the price of public transport, and no effect of greater availability of rail networks. Based on the elasticities estimated, we predict road transport CO₂ emissions for the AML according to different macroeconomic scenarios. The results indicate that the emissions target is only achieved in the scenario of poor economic performance. In the presence of medium and strong economic growth, fuel prices would need to increase by about 7% and 11% per year respectively in order to meet the emissions target.     

Forecasting demand for high speed rail

It is sometimes argued that standard state-of-practice logit-based models cannot forecast the demand for substantially reduced travel times, for instance due to High Speed Rail (HSR). The present paper investigates this issue by reviewing the literature on travel time elasticities for long distance rail travel and comparing these with elasticities observed when new HSR lines have opened. This paper also validates the Swedish long distance model, Sampers, and its forecast demand for a proposed new HSR, using aggregate data revealing how the air–rail modal split varies with the difference in generalized travel time between rail and air. The Sampers long distance model is also compared to a newly developed model applying Box–Cox transformations. The paper contributes to the empirical literature on long distance travel, long distance elasticities and HSR passenger demand forecasts. Results indicate that the Sampers model is indeed able to predict the demand for HSR reasonably well. The new non-linear model has even better model fit and also slightly higher elasticities.     

The time dimension of parking economics

A model of demand for parking, evolving over time, is proposed. The model features both extensive (whether to park) and intensive (for how long to park) margins of parking demand, allows multidimensional heterogeneity of parkers, and evolution of demand throughout the day. I show that the optimal price for parking is proportional to the rate of arrival of new parkers and is inversely related to the square of the occupancy rate, which is different from previously discussed pricing methods. I show that the primary purpose of pricing is to regulate departures, rather than arrivals, of parkers. I also find that asymmetric information about parkers’ characteristics does not prevent the parking authority from achieving the social optimum. A numerical example compares the optimal policy against the alternatives.     

Substitution between working at home and out-of-home: The role of ICT and commuting costs

This paper analyzes the trade-off between working at home and out-of-home, ICT and commuting time. To this end, we develop a microeconomic demand system, which explicitly incorporates both time and income constraints. Commuting time is considered as the price to be paid for working out-of-home and a decrease in earnings as the price for working at home. For the latter, we find that working at home leads to a (marginally significant) reduction of the wage rate of about 19%, but this gap largely disappears when ICT is used for at home work. To examine the relation between out-of-home and at home work empirically, we estimate a translog indirect utility function, from which we are able to estimate substitution and price elasticities between working at home and out-of-home for The Netherlands. The results show that changes in ICT and commuting time display rather weak substitution effects on working out-of-home and at home, respectively. Moreover, individual characteristics – especially age and education – seem to be more important for the choice between working at home and out-of-home than ICT availability or commuting time.     

The acceptability of road pricing in Vienna: the preference patterns of car drivers

The acceptability of road pricing has attracted considerable attention among researchers over the last decade, as is evident in the amount of literature about transport and environmental economics. The general conclusion from these studies has been that there is low acceptability for road pricing among car users. In this paper, we add more knowledge to the existing literature by conducting an acceptability study of road pricing in Vienna, where such a study has never been conducted before. We used a replication study approach where a previous approach used in the EU research project AFFORD (acceptability of fiscal and financial measures and organisational requirements for demand management) was replicated for Vienna and further supplemented with a conjoint analysis. In order to examining whether the Vienna study confirms previous findings. We investigated the acceptability of two concrete policy packages factors influencing this acceptability, and preference patterns that can be used in designing a road pricing policy for Vienna. The survey reveals a higher acceptability if road pricing schemes lead to perceived personal benefits. According to the multivariate analyses, the “personal outcome expectations”, “social norm” and “perceived effectiveness” variables account for more than 50 % of the criterion variance and therefore these are the most influential factors. Road pricing schemes can be an effective transport management instrument for a city particularly if they are associated with direct investment in public transport and public infrastructure. Thus, personal benefits can be perceived more easily and direct effects can be expected.