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which themselves use their measurements in order to continuously influence
trac flow by means of local interactions, could be used to remedy this defi-
ciency. The primary objective for driver assistance systems so far is to increase
either driving safety or driving comfort. As an enhancement, in recent years
initial proposals have been presented for ADAS which are to optimize traf-
fic flow globally by means of the interaction of autonomous vehicles without
depending on a centralized trac control center [7],[8]. These systems con-
trol the longitudinal vehicle behavior in order to optimize trac flow and
therefore can be considered as trac assistance systems (TAS). For dimen-
sioning and analysis of the effects of TAS on trac flow simulative research
is required.
In Section 2 the description of the system model is presented, which is ap-
plied to trac modelling. Section 3 of this paper will show that investigations
associated with TAS call for new requirements which have to be reflected.
The review of the current state of the art in section 4 clearly shows that these
requirements have not been considered in the last several decades as simula-
tion investigations were designed with different objectives in mind. For the
investigation of TAS the simulation model has to be valid on both the mi-
croscopic and macroscopic level. In order to reach this goal, it is necessary to
perform a calibration and validation of the simulation model in use. Section
5 presents the new two-level approach for calibration and validation which
aims to overcome the aforementioned deficiencies. Section 6 presents a data
acquisition concept to obtain the necessary measurement data for fulfilling
the determined requirements. Section 7 shows first results for calibration of
the car-following behavior and the validation on macroscopic level for the
headway distribution. The paper closes with a conclusion and an outlook for
further research.
2
A system model to tra
c modelling
A system can be described with its properties
state, function, structure
and
behavior
[10] which are interrelated specifically [11].
Furthermore systems can be characterized by an
abstraction hierarchy
.
They are composed of a sum of parts which again can be decomposed into
a sum of parts. Seen in detail those parts again show a certain complexity
in terms of the system properties of
state, function, structure
and
behavior
.
With reference to a specific level of abstraction a system has a superordinate
and several subordinate systems. The particular system itself serves as a
superordinate of subordinate system for other levels of abstraction. Following
this principle of decomposition several layers of abstraction arise.
As soon as a system comes into existence by means of the combination
of its parts, new properties emerge which have not been visible before and
can not be explained by means of the properties of its isolated parts. This
phenomenon is referred to as
emergence
. Emergence can be explained by
means of the system structure. The elementary properties of systems previ-
