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

Diesel demand in the road freight sector in the UK: Estimates for different vehicle types

Demand elasticity for petrol or diesel is an important policy parameter, both from energy security and global warming perspective. Despite an abundance of literature on petrol demand, there are few studies on diesel demand, and even fewer on demand by different vehicle types. This paper aims to model diesel demand for different freight duty vehicle types (e.g. heavy vs. light goods vehicles and rigid vs. articulated trucks) in the UK. We argue that the switch to diesel from petrol engines in the light vehicles sectors could have biased earlier petrol or diesel demand elasticities in Europe, and show that it was indeed the case for the light goods vehicle sector. Results show that both light and heavy goods vehicles have similar income elasticities, although within the heavy duty sector, articulated trucks are more elastic than rigid trucks. Overall, heavy goods vehicles were responsive to fuel prices, but light goods vehicles were not. Within the heavy duty sector, rigid trucks showed statistically significant price elasticity, but articulated trucks did not respond to changes in fuel prices. Our results show that price-based policies to curb fuel consumption from the light or heavy goods vehicles are unlikely to be effective

Decomposing the drivers of aviation fuel demand using simultaneous equation models

Decomposition analysis is a widely used technique in energy analysis, whereby the growth in energy demand is attributed to different components. In this paper the decomposition analysis is extended in a system econometric modelling framework in order to understand the drivers of each of the components in the decomposition analysis. The growth in aviation fuel demand is decomposed into five components: population, passenger per capita, distances per passenger, load factor and fuel efficiency, and then seemingly unrelated regression methods is applied in order to model each of these. Results show that the fuel demand in the US air transport sector most closely follows the trend of passenger per capita. The growth in fuel demand is slowed by improvements in fuel efficiency and usage efficiency (load factor). Increases in income affects both passengers per capita and distances per passenger. However, increases in travel costs have opposite effects on passenger per capita (decreases) and distance per passenger (increases). Increases in jet fuel prices improves both the load factor and fuel efficiency

The Innovation Interface: Business model innovation for electric vehicle futures

There is huge potential to link electric vehicles, local energy systems, and personal mobility in the city. By doing so we can improve air quality, tackle climate change, and grow new business models. Business model innovation is needed because new technologies and engineering innovations are currently far ahead of the energy system’s ability to accommodate them. This report explores new business models that can work across the auto industry, transport infrastructure and energy systems

Fully automated vehicles: A cost of ownership analysis to inform early adoption

Vehicle automation and its uptake is an active area of research among transportation academics. Early adoption rate also influences the results in other areas, e.g. the potential impacts of vehicle automation. So far, most of the interest in the uptake of fully automated, driverless vehicles has focused on private vehicles only, yet full automation could be beneficial for commercial vehicles too. This paper identifies the vehicle sectors that will likely be the earliest adopters of full automation. Total costs of ownership (TCO) analysis is used to compare the costs (and benefits) of vehicle automation for private vehicles among different income groups and commercial vehicles in the taxi and freight sectors in the UK. Commercial operations clearly benefit more from automation since the driver costs can be reduced substantially through automation. Among the private users, households with the highest income benefit more from automation because of their higher driving distances and higher perceived value of time, which can be used more productively through full automation

The effects of e-ridehailing on motorcycle ownership in an emerging-country megacity

The impact of app-based e-hailing or ridesourcing services on various transport metrics is an area of active research, and research so far have focused on the car-based (or four-wheeled vehicle based) services only. In many cities in the developing and emerging countries, motorcycle-based passenger e-hailing services have become very popular in the last few years, but the implications of these have not been studied before. This study investigates the effects of motorcycle-hailing apps in Dhaka – a megacity in Bangladesh – on the size of its motorcycle fleet. We employ segmented multiple regression on timeseries data to show that there was a statistically significant increase in motorcycle ownership in Dhaka as a result of the motorcycle-hailing services. The findings were also supported by a visual intervention analysis. By the end of 2018, there were 7.45% more motorcycles in Dhaka than there would have been if these app-based e-hailing services were not available. We conclude with potential implications of these increases in motorcycle numbers and future research directions

An examination of the effects of ride-hailing services on airport parking demand

The emergence of ride-hailing services offered by the Transportation Network Companies (TNCs) globally are substantially affecting how we travel by car, and as such, use parking facilities. Airport parking, which is a substantial source of revenue for the airports, is no different and anecdotal evidence suggest a reduction in parking revenue in airports in recent years as a result of the popularity of ride-hailing services. This research investigates the effects of the entry of ride-hailing services on airport parking patronage using three large airports (John F Kennedy International, Newark Liberty International and LaGuardia) in the US as a case study. Intervention analysis technique was used on time-series monthly parking data at these airports, controlling for passenger numbers. Results indicate there was a statistically significant reduction in the numbers of cars parked at all three airports since the introduction of ride-hailing services, confirming the anecdotal evidence. Findings have implications not only for airport business and revenue models, but also for wider effects of ride-hailing and automation

Fully automated vehicles: the use of travel time and its association with intention to use

Traditionally, time spent travelling has been seen as a ‘cost’ to the traveller. Autonomous or fully automated vehicles (FAVs) can free the driver of the driving task and allow engagement in other worthwhile activities inside the FAVs, which can transform how people travel. However, there is little understanding about how travel time can be used and how worthwhile this time can be in FAVs; and whether this is related to the intention to use FAVs. This paper addresses these questions through a multi-country questionnaire survey, with a sub-sample of chauffeur-driven car users to mimic time use in FAVs. Responses show that users are likely to engage in other non-driving activities while riding in FAVs, and these differ according to trip purpose and direction. Time spent travelling in FAVs is perceived to be more useful than in current modes of transport. Interest in using FAVs is directly correlated with perceived usefulness of time in autonomous vehicles. There is a strong correlation between intended activities in FAVs and current activities by primary car users in chauffeur-driven cars, providing some validation to the stated intention responses. Results have important implications for policy-making, time use and value-of-time research, as well as vehicle interior design

Help or hindrance? The travel, energy and carbon impact of highly automated vehicles

Experts predict that new automobiles will be capable of driving themselves under limited conditions within 5-10 years, and under most conditions within 10-20 years. Automation may affect road vehicle energy consumption and greenhouse gas (GHG) emissions in a host of ways, positive and negative, by causing changes in travel demand, vehicle design, vehicle operating profiles, and choices of fuels. In this paper, we identify specific mechanisms through which automation may affect travel and energy demand and resulting GHG emissions and bring them together using a coherent energy decomposition framework. We review the literature for estimates of the energy impacts of each mechanism and, where the literature is lacking, develop our own estimates using engineering and economic analysis. We consider how widely applicable each mechanism is, and quantify the potential impact of each mechanism on a common basis: the percentage change it is expected to cause in total GHG emissions from light-duty or heavy-duty vehicles in the U.S. Our primary focus is travel related energy consumption and emissions, since potential lifecycle impacts are generally smaller in magnitude. We explore the net effects of automation on emissions through several illustrative scenarios, finding that automation might plausibly reduce road transport GHG emissions and energy use by nearly half – or nearly double them – depending on which effects come to dominate. We also find that many potential energy-reduction benefits may be realized through partial automation, while the major energy/emission downside risks appear more likely at full automation. We close by presenting some implications for policymakers and identifying priority areas for further research