D5 Annex report WP 4 - ETIS plus

D5 Annex WP 4: ETIS DATABASE METHODOLOGY AND DATABASE USER
MANUAL – PASSENGER DEMAND
If one assumes that the individuals will always maximise their utilities, the denominator of the
above equation will be constant for a specific origin A. Hence, as the number of trips starting in
A is also constant, the denominator could be expressed as a function of a constant k. The
resulting relationship will be
n = g ( k ) f ( X B
, C
AB
)
and could be estimated by different means. The share of a single relation AB could be obtained
by transforming, i.e. standardise, all computed ‘utilities’ according to the first equation.
The total number of trips on relation AB is the product of the computed share and the
aggregated mobility figures from the first step:
T AB, p = T A, p × Share(B | i A )
The combination of a generation process based on individual trips and a distribution like this
possesses the advantage that the total number of trips is strictly limited by the first step.
4.6 Model adjustments and matrix constraints
As models are only representations of reality, their results have to be checked against a
sequence of information that are (in order of increasing degree of details)
· Common sense
· Detailed knowledge of the underlying data
· Awareness of “modellers’ ignorance”
· Basic figures (like per capita values)
· More detailed statistics (e.g. on border crossings or about tourism)
· Travel link counts (on roads, at airports, etc.)
· Individual consumer’s behaviour
Common sense refers to the general structure that should show up in the generated patterns.
One would expect that larger population figures will usually imply higher flows than smaller
numbers of potential travellers. Data knowledge is crucial for the secure and appropriate use of
information. If e.g. a survey sample covers only people older than 12 years, the raised figures
have to be adjusted to be comparable to other information about total populations.
‘Modellers’ ignorance‘ means the situation that one knows about the influence of certain
determinants on the intended model output, but the lack of available data make it impossible to
consider these impacts appropriately in the modelling/ estimation process. A well­known
example is the significant impact of disposable income on travel­related decisions. The absence
on sufficient measures for this determinant forces modellers to use some proxies like per capita
GDP that are biased in various ways and do not express exactly the intended impacts.
Document3
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