LARGE-SCALE PARALLEL GRAPH-BASED SIMULATIONS - MATSim

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Note that this now exactly enforces the storage §
constraint by setting
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to zero
once ¤#" has reached . In addition, the kinematic jam wave speed is given
explicitly
via . There is some interaction between length of a segment, time step, and that needs to
be considered. The network version of the cell transmission model [16] also specifies how
to implement fair intersections. The cell transmission model is implemented under the name
NETCELL [9].
Other link dynamics are, for example, provided by DynaMIT [19], DYNASMART [20] or
DYNEMO [61]. These are based on the same mass conservation equation as Equation
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(2.1), but
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use §
different specifications for . In fact, DynaMIT and DYNASMART calculate vehicle
speeds at the time of entry into the segment depending on the number of vehicles already in
the segment. The number of vehicles that can potentially leave a link in a given time step is, in
consequence, given indirectly via this speed computation. Since this is not enough to enforce
physical queues, physical queuing restrictions are added to this description. DYNEMO varies
a vehicle’s speed continuously along the link based on traffic conditions of the current and the
next segment.
2.4 The Basic Benchmark
A real-world scenario is preferred for the benchmarks throughout this thesis instead of using a
synthetic scenario or using only the theoretical performance predictions.
A theoretical performance prediction gives an idea about what is supposed to be expected.
However, such predictions possibly miss some performance-relevant details that appear only
when the real-world data is used. For example, if an example data set is small enough that it
can fit into a computer’s memory while the real-world data is bigger than the available memory,
the results of theoretical predictions for the small data set will include the cache effects. For
example, if the data set can be kept smaller than the size of the cache, which is a high speed
memory system, there might be a significant speed-up since the data is reached with a higher
speed.
Synthetic scenarios are generated by synthetic data. They are used to make generalizations
about the performance of the real-world scenarios. If a real-world scenario with enough information
to test all the features of a benchmark is not available, a synthetic scenario with full
information is useful. Furthermore, the results are easier to adapt between different scenarios
if it is applicable. On the other hand, if a “possible” real-world data needs to cover a lot of
details, then the generation of a similar synthetic scenario gets harder.
2.5 A Practical Scenario for the Benchmarks
One of the conditions that a traffic flow simulation must fulfill is that the simulation should
be able to run scenarios of a meaningful size within acceptable periods of time. From the
transportation planning point of view, such scenarios are large scale real-world problems, which
include millions of agents and all kinds of traffic.
The street network of Switzerland used in the benchmarks of this thesis was originally
developed for the Federal Office for Spatial Development (ARE) [6]. Then, the network was
extended to include the major European transit corridors for a railway-related study [85]. The
version of the street network used through this thesis is a derivation of the extended network.
It contains 10 564 nodes and 28 622 links.
The nodes of the street network are the intersections of roads and are defined by geographical
coordinates. The links are the roads that connect two nodes. Each link is unidirectional and
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