  Due to urbanization and the increase of individual mobility, in most
metropolitan areas around the world congestion and inefficient traffic
management occur. Highly necessary intelligent traffic control systems, which
are able to reduce congestion, rely on measurements of traffic situations in
urban road networks and freeways. Unfortunately, the instrumentation for
accurate traffic measurement is expensive and not widely implemented. This
thesis addresses this problem, where relatively inexpensive and easy to install
loop-detectors are used by a geometric deep learning algorithm, which uses
loop-detector data in a spatial context of a road network, to estimate queue
length in front of signalized intersections, which can be then used for
following traffic control tasks. Therefore, in the first part of this work a
conventional estimation method for queue length (which does not use machine
learning techniques) based on second-by-second loop-detector data is
implemented, which uses detected shockwaves in queues to estimate the length
and point of time for the maximum queue. The method is later used as reference
but also as additional input information for the geometric deep learning
approach. In the second part the geometric deep learning algorithm is
developed, which uses spatial correlations in the road network but also
temporal correlations in detector data time sequences by new attention
mechanisms, to overcome the limitations of conventional methods like excess
traffic demand, lane changing and stop-and-go traffic. Therefore, it is
necessary to abstract the topology of the road network in a graph. Both
approaches are compared regarding their performance, reliability as well as
limitations and validated by usage of the traffic simulation software SUMO
(Simulation of Urban MObility). Finally, the results are discussed in the
conclusions and further investigations are suggested.
