Abstract:
The main purpose of this research is to develop a set
of econometric Air-Rail competition models which are
sufficiently sensitive to measure the effects upon
demand of policy decisions, with regard to such variables
as frequency of services and fares.
Existing Modal Competition Models have, rather uncritically,
applied Multiple Regression analysis in considering
only one aspect of the market, namely the demand
for travel, ignoring therefore the effects of the supply
upon the demand. The emergence of the so called "Simultaneous
Equations Bias", due to the two-way dependency
between the demand and the level of service factor expressing
the supply, renders the application of the 015
(Ordinary Least Squares) inappropriate, and hence,
yields biased, inconsistent, and inefficient OLS coefficients.
The models, developed in this study, depart from all
existing Modal Competition Models, and overcome some of
their drawbacks. They are formulated as Multi-equation
Supply/Demand Modal Competition Models. They introduce
the frequency of services variable not only in the
demand, but also in the supply equation expressing the
level of supply in response to changes in other variables.
In order to derive unbiased, more conSistent,
and more efficient coefficients, sophisticated statistical
techniques, such as 2315 and JSLS (Two-Stage
Least Squares and Three-Stage Least Squares) • are applied
as a means of calibration.
The elasticities obtained are consistent with the Supply
and demand Microeconomic Theory. The frequency of services
appears as the most powerful explanatory variable
in Air demand; whereas fare and income are the most
powerful variables in Rail demand equation. This leads to the conclusion that Air mode is mainly higher income
groups and/or business oriented market; and Rail mode
lower income groups and/or personal oriented market.
Furthermore, Air and Rail are competing on a fare basis
in short routes; while they do not show close substitutes
for each other in longer ones.
The high significance of the frequency of services, in
Air demand, outlines its importance as a factor influencing
the demand, and therefore, provides the Airlines
management with the capability of improving the demand
by acting upon the endogenous factor. This is of great
interest in the scheduling fleet process.
Similarly, the significance of Rail fare variable offers
the Railways management the possibility of acting upon
the demand through this controlable variable, for an
efficient pricing policy. Rail journey time elasticities,
derived from these models, are very close to the
elasticities assumed by British Railways Board, in
their Passenger Traffic Model, 1980.
The statistical results indicate that the elasticities
derived are useful for both analysis and forecasting
purposes.