The asymmetric effects of income and fuel price on air transport demand

Forecasts of passenger demand are an important parameter for aviation planners. Air transport demand models typically assume a perfectly reversible impact of the demand drivers. However, there are reasons to believe that the impacts of some of the demand drivers such as fuel price or income on air transport demand may not be perfectly reversible. Two types of imperfect reversibility, namely asymmetry and hysteresis, are possible. Asymmetry refers to the differences in the demand impacts of a rising price or income from that of a falling price or income. Hysteresis refers to the dependence of the impacts of changing price or income on previous history, especially on previous maximum price or income. We use US time series data and decompose each of fuel price and income into three component series to develop an econometric model for air transport demand that is capable of capturing the potential imperfectly reversible relationships and test for the presence or absence of reversibility. We find statistical evidence of asymmetry and hysteresis – for both, prices and income – in air transport demand. Implications for policy and practice are then discussed.     

Asymmetry in transport fuel demand: Evidence from household level data

Models for gasoline demand for transportation activities generally assume that demand is perfectly reversible with respect to gasoline price (and income). The small literature which relaxes the reversibility assumption in gasoline demand argues technological fixation leads to this asymmetry and utilizes aggregate time-series model to find evidence in favour of asymmetry. In this research it is suggested that there could also be behavioural factors behind this asymmetric response, possibly due to the loss aversion nature of human beings as described in the prospect theory. For the first time, household level data was used to understand asymmetry in gasoline demand in response to changes in gasoline price and income. There was statistical evidence that gasoline price and income both can induce asymmetric changes in gasoline demand among households. Specifically, elasticity with respect to rising prices and falling income is larger than the elasticity with respect to falling prices and rising income respectively, which is consistent with loss aversion in gasoline purchase behaviour. There was also some evidence of heterogeneity in the asymmetric responses between urban and rural households. The results have implications for transport-related energy tax policies or subsidies, while the method can be applied directly for non-energy goods as well.     

Estimating flight-level price elasticities using online airline data: A first step toward integrating pricing,demand, and revenue optimization

We estimate flight-level price elasticities using a database of online prices and seat map displays. In contrast to market-level and route-level elasticities reported in the literature, flight-level elasticities can forecast responses in demand due to day-to-day price fluctuations. Knowing how elasticities vary by flight and booking characteristics and in response to competitors’ pricing actions allows airlines to design better promotions. It also allows policy makers the ability to evaluate the impacts of proposed tax increases or time-of-day congestion pricing policies. Our elasticity results show how airlines can design optimal promotions by considering not only which departure dates should be targeted, but also which days of the week customers should be allowed to purchase. Additionally, we show how elasticities can be used by carriers to strategically match a subset of their competitors’ sale fares. Methodologically, we use an approach that corrects for price endogeneity; failure to do so results in biased estimates and incorrect pricing recommendations. Using an instrumental variable approach to address this problem we find a set of valid instruments that can be used in future studies of air travel demand. We conclude by describing how our approach contributes to the literature, by offering an approach to estimate flight-level demand elasticities that the research community needs as an input to more advanced optimization models that integrate demand forecasting, price optimization, and revenue optimization models.     

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.     

Modelling operational, economic and environmental performance of an air transport network

The paper models the operational, economic and environmental performance of an air transport network consisting of airports and air routes connecting them. The operational capacity represents the operational performance. Thresholds on the network’s environmental burdens reflect the environmental performance. The economic performance comprises the network’s profits. Modelling the network performance includes using integer programming techniques to maximise total network profits for given operational capacity and environmental constraints under conditions where environmental externalities are internalised.     

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.     

Crowding in public transport systems: Effects on users,operation and implications for the estimation of demand

The effects of high passenger density at bus stops, at rail stations, inside buses and trains are diverse. This paper examines the multiple dimensions of passenger crowding related to public transport demand, supply and operations, including effects on operating speed, waiting time, travel time reliability, passengers’ wellbeing, valuation of waiting and in-vehicle time savings, route and bus choice, and optimal levels of frequency, vehicle size and fare. Secondly, crowding externalities are estimated for rail and bus services in Sydney, in order to show the impact of crowding on the estimated value of in-vehicle time savings and demand prediction. Using Multinomial Logit (MNL) and Error Components (EC) models, we show that alternative assumptions concerning the threshold load factor that triggers a crowding externality effect do have an influence on the value of travel time (VTTS) for low occupancy levels (all passengers sitting); however, for high occupancy levels, alternative crowding models estimate similar VTTS. Importantly, if demand for a public transport service is estimated without explicit consideration of crowding as a source of disutility for passengers, demand will be overestimated if the service is designed to have a number of standees beyond a threshold, as analytically shown using a MNL choice model. More research is needed to explore if these findings hold with more complex choice models and in other contexts.