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Strategic planning requires policy inputs that should be based upon a deep understanding of user patterns. Quite often governmental bodies are faced with setting policies that have to fit within certain resource constraints. Quantifying the effect of different policies in terms of a model not only helps the decision making process but also increases policy transparency in the eyes of the public.
For example, data mining the pattern of cross border traffic can be important for planning of immigration and transport capacity planning. Growth of database size can also pose a technological challenge that requires solutions using distributed, hierarchical and parallel database technologies.
Another example is the building of an intelligent traffic control system that can adjust and coordinate neighboring traffic lights to minimize congestion in the worst affected area. The model for predicting traffic throughput must react to data collected from roadside monitors in real time. The performance of such simulation-based models can be increased substantially by algorithm parallelization.
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