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6 CONCLUSIONS
This work outlined a generic approach for a collabo-
rative validation of locally detected incidents in sim-
ple urban road networks. The network topology is
taken into account as well varying degrees of knowl-
edge about the detected incidents. Integrated into the
OTC, the approaches showed potential to improve the
accuracy and resulting success of local incident detec-
tion. As a limitation, the evaluation is based only on
assumptions about the underlying AID.
This is a first attempt at an improved detection in
OTC and next steps can be outlined: An evaluation
with real (simulated) traffic and incident situations.
Also, the AID must be carried out by the OTC as out-
lined in Section 2. Finally, the various threshold must
be optimised, e. g., by using machine learning tech-
niques. All this will multiply the test cases and possi-
ble conclusions dramatically which make a complete
test scenario unfeasible. Finally, the reinforcement
learning capabilities of the OTC should be applied,
to ensure the SASO capabilities of the system.
ACKNOWLEDGEMENTS
This research was supported by the Deutsche
Forschungsgemeinschaft, DFG, in the context of the
project “Zwischenfall-bewusstes resilientes Verkehrs-
management f
¨
ur urbane Straßennetze (InTURN)” un-
der grant TO 843/5-1. We acknowledge this support.
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