Representing travel cost variation in large-scale models of long-distance passenger transport

Abstract

In this paper, we show that cost variation for long-distance travel is often substantial and we discuss why it is likely to increase further in the future. Thus, the current practice in large-scale models, to set one single travel cost for a combination of origin, destination, mode, and purpose, has potential for improvement. To tackle this issue, we develop ways of accounting for cost variation in model estimation and forecasting. For public transport, two approaches are proposed. The first method focusses on improving the average fare, whereas the second approach incorporates a submodel for choice of fare alternative within a demand model structure. Only the second method is consistent with random utility theory. For car, cost variation is related to long run decisions such as car type choice. Handling car cost variation therefore implies considering car type choice. This long-term choice can be considered using a car fleet model.

Modelling long-distance travel demand by combining mobile phone and survey data

“…The travel cost by car will vary by a number of factors, the first one being the number of travellers per car, which can be adjusted for using demand data. Other factors which affect the car per kilometre cost are differences in energy efficiency between different car models, the type of fuel used (there is a significant price difference between electricity and combustion engine fuels), the type of car access (car ownership, private leasing or company car, which affect both prices and taxes), and also depreciation (Kristoffersson et al, 2021).…”

Mode choice modelling of long-distance passenger transport based on mobile phone network data

“…First, it varies due to differences in energy efficiency between different car models, as well as the type of fuel used (there is a significant price difference between electricity and combustion engine fuels). The per kilometre cost also depend on the type of car access (car ownership, private leasing or company car which affect both prices and taxes), and also by depreciation (Kristoffersson et al, 2021). The average per kilometre cost of car travel in Sweden is unknown.…”

“…It is worth mentioning that for all modes of transport there is variation in actual prices between origin-destination pairs, even within the same mode. Kristoffersson et al (2021) studied trips between the three largest cities in Sweden and concluded that there is a high variation in travel costs for car, train and air trips, with the highest variation found in air travel (with a coefficient of variation of 60%). As we only use a representative value of the price that (hopefully) reflects the average price, this likely causes a downward attenuation Chapter 3.…”

Mode Choice Latent Class Estimation on Mobile Network Data

“…For car trips, the supply data includes travel time (t) and travel distance by OD pair. It is well known that the marginal cost of car travel is subject to great variation between trips and vehicles (Kristoffersson et al, 2020), due to variation in energy efficiency, fuel type, driving style, traffic environment, and the type of car access (car ownership, private leasing or company car influence the marginal cost of car use, both through prices and taxes, and depreciation). We therefore approximate the marginal cost of car use.…”