Information Technology Reference
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simulation, as well as advanced forecasting (e.g. auto-correlative, multi-regressive)
and statistical techniques; and, very important, (3) most of all this information can
be better highlighted by using alternative, more adequate forms of visualization
such as well-designed graphs and maps; also, some information (e.g. spatial-
temporal trends) can be exclusively evidenced by specific forms of visualization. It
is widely recognized that it is easier to detect a pattern from a “picture” than from a
textual or numeric explanation.
From the above, it is obvious that one of the most important problems of ITS is the
analysis and management of information. This problem is becoming further relevant
due to permanent increase on the availability of data and their inexpensive storage and
processing power as a result of the wide implementation of modern IS/IT systems.
On the other hand, ITS planners and decision-makers need to foresee the perform-
ance of systems (existing or desired to be implemented), and therefore they need to
use suitable simulation tools. The integrated framework helps DRT managers (TDC
coordinators, system designers and analysts) at their different levels of decision.
At the strategic level, planners are supposed to set up the objective, aim, and the
broader strategy and policies of the system. For example, in a recent work on the de-
sign and planning of demand-responsive transportation system, Dias et al. [5] report
the following set of decisions at this level: How the system should be operating in the
long-term in order to be viable and sustainable in the three basic terms: economic,
social and environmental? What are the main objectives of its existence? Which type
of services must it offer? At what level(s) of flexibility? At what price levels (whether
taking account or not potential subsidisation)?
At the tactical level, decisions aim to devise ways to implement the transport sys-
tem according to the strategy previously defined. The analyst component of the model
monitors and analyses current performance of the system, tries to identify hidden
patterns of operational data, and continually tries to devise better solutions to tackling
operational problems; some solutions are automatically devised and incorporated into
the ODSS component (e.g., a recurrent set of day to day fixed travel patterns are iden-
tified and a shortcut route planning procedure automatically generates a fixed service
plan); however, the most part of solutions requires the investigation and validation of
human analysts before their practical implementation. This planning stage is crucial
for the design of the transport scheme and several authors had identified the most
critical decisions. For example, for the case of demand-responsive transport [16-17]):
Which exact level(s) of flexibility should be adopted, in terms of routes,
timetables, frequencies, time-windows (e.g., arrivals at stops)?
Which pre-booking mechanisms and rules should be adopted?
Which resources will be used (fleet, drivers, informatics, operations centre
and staff)?
Which fare structure should be implemented?
Which level of integration with public transport network (schedule
buses/train network, etc.)?
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