Recommendations to Develop the Modelling Approach

Recommendations to further develop the modelling framework described in Chapter 4 and the modelling approaches for each of the sub-models described in Chapter 5 are presented below. They include integration of a more advanced passenger choice model and a fleet turnover model into the modelling framework, and simulation of changes in schedule shape in response to airport capacity constraints.

 It is recommended that a more advanced passenger choice model be integrated into the modelling framework described in Chapter 4. In the existing framework, passenger choice is only modelled between airlines, and only as a function of flight frequency. A key assumption made in the development of the modelling framework described in Chapter 4, and in the network optimisation for each airline described in Section 5.1, is that passenger routing (non-stop or connecting through one of the hubs operated by the airline) is an airline decision. Thus, in the network optimisation, passengers are routed in such a way as to maximise airline profit. In reality passengers choose which itineraries to fly based on a number of criteria, including ticket price, total travel time, how many connections make up the itinerary, and which airline operates the itinerary (frequent flyer programs have increased airline loyalty, meaning that passengers may choose to fly with a specific airline despite the ticket price or travel time being less attractive than for a competitor). Airlines do, however, sell tickets for itineraries that maximise their profit, so in many cases the simplification modelled in this dissertation is adequate. This was demonstrated in Chapter 6, where the model described in Chapter 4 and 5 was validated against observed data. Certain effects are not captured, however, limiting the capability of

Chapter 9

the model to simulate some air transport system responses to environmental constraints. One such effect is passenger choice of itineraries. Inclusion of more advanced passenger choice modelling would enable the distribution of passengers on each itinerary to be calculated as a function of fares and travel time. This would require integration of a more advanced passenger choice model between the Passenger Demand Model and Network Optimisation Models presented in Figure 4-1, and would require development of the Fare Model to simulate fares by itinerary, as opposed to estimation of average fares across all itineraries. Inclusion of passenger demand modelling by itinerary would enable more realistic simulation of the distribution of traffic across airports in a multi-airport system. In the current framework, such distribution is based entirely on airline costs, including landing fees and delay costs. In reality, however, airline choice of airports within a multi- airport system is also a function of the location of the airport relative to urban centres and its accessibility (Bolgeri et al., 2008). These criteria, along with flight delays and other factors affect passenger choice.

Inclusion of a more advanced passenger choice model within the modelling framework to model passenger demand by itinerary would also allow improved modelling of the effects of regional increases in costs. The current modelling framework is capable of simulating airline decisions to shift connecting passengers from a hub airport within the region of increased cost to a hub airport outside the region. This requires, however, that the airline operate hubs both inside and outside the region. For many regions this is not the case, but there may still be a shift in connecting traffic to other regions. This is because an airline that does not operate a hub outside the region of increased cost may be forced to increase fares because it is not able to avoid the cost increase within the region. A connecting passenger response to this increase in fares may be to choose to fly with other airlines that do not operate in the region, and therefore offer lower fares. Inclusion of a more advanced passenger choice model within the modelling framework would allow this effect to be simulated, providing a more complete picture of the effects of regional cost increases than is currently possible.

 It is recommended that a fleet turnover model be integrated into the modelling framework described in Chapter 4. Because modelling of airline fleet turnover is already a developed component of most models in the literature that simulate the environmental impacts of

Recommendations for Future Research

aviation (described in Appendix A), development of another model to simulate fleet turnover was not considered to be a significant contribution to the field of study in this dissertation. Such a fleet turnover model was therefore not included in the framework described in Chapter 4. However, as described in Section 2.2, one airline response to environmental constraints is to upgrade equipment. The entry of this equipment into the fleet and the turnover of the existing fleet are complex functions of the operating costs and performance of the old and new equipment. The development of the technology and its market readiness, however, are also functions of costs (particularly oil price) and environmental constraints. These affect airline demands for improved economic and environmental performances, which increase the pressure on manufacturers to develop suitable technology. Airline responses to environmental constraints by upgrading equipment may also have an impact on other airline responses. It has been demonstrated in Section 7.4, where the effect of introducing radically new technology is investigated, that the relationship between aircraft operating costs and the flight network is complex, and that the introduction of new technology with significantly reduced fuel burn does not necessarily result in a significant reduction in emissions. In order to model the impact of new technology more accurately, it is recommended that a fleet turnover model be included in the modelling framework described in Chapter 4. Such a fleet turnover model may be integrated in different ways. In the simplest case, it would be integrated within the iteration framework, but outside the network optimisation, modelling airline fleet decisions separately to airline network optimisation decisions. A more complex integration would be to include fleet choice between different technologies, with different costs and performance, within the network optimisation, simulating airline optimisation of the flight network and fleet purchase simultaneously. In both cases it is noted that a fleet constraint would be added to the network optimisation described in Chapter 5.1. This has others benefits as fleet constraints limit the change in operations from one year to the next. In the current framework each year is modelled independently, including specification of aircraft types by flight segment to maximise profit. In reality, only aircraft in the available fleet can be utilised, constraining the optimisation. The current implementation therefore neglects constraints limiting the change in operations from year to year. Introduction of a fleet constraint would allow this effect to be modelled.

Chapter 9

 It is recommended that a component be included in the modelling framework that simulates changes in schedule shape in response to airport capacity constraints. As described in Section 2.2, one airline response to increased airport capacity constraints is to flatten the schedule, reducing the degree to which a banked schedule is operated. This response is not modelled in the framework developed in this dissertation. It is however, a response to airport capacity constraints that has been observed (Evans, 2002). It is therefore recommended that the modelling framework be expanded to simulate this effect also. This may be done, for example, by correlating the banking metric described by Evans (2002) to flight delays, and adjusting a schedule to match the required banking metric as delays increase.