Information Technology Reference
In-Depth Information
which means that
consists of 200 pointers pointing to vehicles. Memory for the
pointers is immediately allocated; memory for the vehicles is only allocated when it is
used. For example, when the code decides to put a vehicle into cell number
Road
ii
, the code
fragment may look as
Road[ii] = new veh ; // memory allocation for vehicle
Road[ii]->ID = SomeID ;
Road[ii]->Speed = 0. ;
Movement is still done in a relatively standard way:
speed = int(Road[ii]->Speed) ;
Road[ii+speed] = Road[ii] ;
Road[ii] = NULL ;
The big advantage of this is that all information belonging to the vehicle is always being
moved with it.
Clearly, many improvements to the above are possible or even recommended, such
as using vectors instead of arrays, making clean definitions of constants such as “200”,
making IDs constant, explicitely defining constructors and destructors, etc.
The currently most important application of the agent technology in transportation
simulations is that agents know where they are going. More precisely, it is possible to
give each agent its full route, for example consisting of a sequence of nodes. A related but
different area of research is to generate those strategic decisions for the agents. All this
results in additional computational modules which are part of a complete transportation
simulation package (e.g. [26]).
5
Limits of the CA Technology and Relations to Other Methods
More Realistic Representations.
A standard problem with CA methods is that they
may be difficult to calibrate against realistic values. Take for example the STCA as
described above in Sec. 2. The length of a cell is straightforward: this needs to be the
space a vehicle occupies in a jam in the average. The time step is traditionally taken as
1 sec, which is justified by reaction time arguments [27]. This implies that speeds come
in increments of 7.5 m/s; 5 cells per second
=
37.5 m/s
=
135 km/h is a convenient
maximum speed. The remaining free parameter,
, is now selected such that the
maximum flow comes out at 2000 veh/sec; this results in
p
noise
. Lane changing
rules can be calibrated similarly, and can even reproduce the density inversion which
happens on German freeways when they are close to capacity [28].
So far so good. The problems start if for some reason the above is not good enough.
For example, the existing speed classes are not fine enough to resolve a difference
between a 55mph and a 50mph speed limit, a common occurence in the U.S. Similarly,
although the fundamental diagram comes out plausibly, acceleration of vehicles turns
out to be too high, which is a problem for emissions calculations.
And it is difficult to resolve those problems via a clever choice of the probability
p
noise
p
noise
=0
.
2
leads to lower acceleration (which is desired),
but also to lower throughput (which is not desired). A possible way out is to have
. For example, increasing
p
noise
p
noise
dependent on the velocity: A small
p
noise
at low velocities together with a large
p
noise
