United States Department of Transportation
i
Adapting risk management and computational intelligence network optimization techniques to improve traffic throughput and tail risk analysis.
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2014-04-01
[PDF-331.15 KB]
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Abstract:Risk management techniques are used to analyze fluctuations in uncontrollable variables and keep those fluctuations from impeding
the core function of a system or business. Examples of this are making sure that volatility in copper and aluminum prices do not force
an aircraft manufacturer to abruptly shut down manufacturing and making sure a failed bank or state does not cause an entire financial
system to fail. Computer network optimization techniques involve many nodes and routes communicating to maximize throughput of
data while making sure not to deadlock high priority or time sensitive data. This project will involve exploring possible remappings of
these application spaces from risk and computer networks to traffic. Some of these possible mappings include mapping flash crashes
and black swans to traffic jams, bank failure to construction or traffic accidents, data packets to vehicles, network routers to traffic
lights and other intersection policies. Due to the large data and large solution/ state/ policy spaces computational intelligence
techniques are a natural fit for traffic as they are for risk management and computer network optimization.
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