Bus fare and journey time elasticities and diversion factors for transport
Understanding bus demand elasticities — the impact of price, journey time and other variables on passenger demand — is fundamental to demand forecasting, investment decision and policy making. A review of the evidence on bus fare and journey time elasticities and diversion factors, conducted by RAND Europe and SYSTRA for the UK Department for Transport (DfT), found insufficient literature and recommends increased data collection.
Bus demand elasticities indicate whether and to what extent changes to different variables, such as price and journey time, have an impact on the number of people using bus services. They are fundamental to demand forecasting, investment decision and policy making, and have been researched in the bus market since the 1970s. However, evidence on bus demand elasticities are becoming dated, as the nature of the bus market has changed over recent years. This is largely due to changes in the franchising model and the introduction of concessionary fares.
Diversion factors are important in quantifying the impact of changes on one mode of transport on the demand for other modes and for new trips. They are often used to derive cross-elasticities, providing an indirect means of quantifying the impact of a price change on one mode on the demand for another mode, which is difficult to measure directly. In transport appraisals they are used to determine the source and extent of new traffic resulting from an investment or change. In this case, diversion factors represent the proportion of new traffic on a mode that would otherwise have used another mode or that would not have travelled (generated demand).
RAND Europe and SYSTRA were commissioned by the UK Department for Transport (DfT) to undertake a review of the current evidence on bus fare and journey time elasticities and diversion factors.
The study used a rapid-evidence literature review process to compile evidence on bus elasticities and diversion factors, starting with systematic identification of relevant international academic and grey literature through structured database searches. This approach was supplemented by enquiries to experts to identify additional material, such as unpublished studies. A detailed cross-dimensional was undertaken to examine the impact on diversion factors of area type, trip-purpose, the available transport alternatives and research methodology, among other things.
In total, 934 diversion factors were identified and obtained from the literature for ten modes of transport. However, while this is a sizeable amount of data, it is not very large when considering the aim of identifying diversion factors between pairs of modes, including from and to bus, rail, car, light rail / metro, walking and cycling, and taking into account the geographical area.
Most data on diversion factors are available from interventions on bus, rail and car. The data on cycle interventions is limited and there are no data on interventions aimed at pedestrians.
The studies included in the dataset mainly cover urban areas or intercity journeys, with little data for journeys in small towns and rural areas. In the urban areas, nearly 90 per cent of the data is for metropolitan areas with five per cent for urban-conurbations.
Diversion factors are, however, developed for a range of scenarios that could be useful for practitioners. These cover interventions on five modes: bus; car; rail; light-rail/metro; and cycling, which may also impact pedestrians, taxi passengers and generated traffic. Where possible values are differentiated by area type and, for some modes, commute values are also computed.
Very little new evidence on bus fare and bus journey time elasticities were uncovered during the review.
More and better evidence on diversion factors should be collected in order to explore the wide variety of diversion factors of interest — the mode from which traffic would come from, the mode it would be diverted to, differentiated by geographical area and the available travel alternatives.
An increased focus on observed estimates from real transport changes interventions. These data could be compiled, over time, to complement the work that has been done in this study.
The study does not recommend any changes to the overall range of values for bus fare elasticities, as very little evidence was uncovered. However, there should be appropriate consideration of elasticity variations according to the fare charged, local market conditions, area type and other factors that might impact on bus fare elasticities.