Forecasting the evolution of fast-changing transportation networks using machine learning

HIGHLIGHTS

SUMMARY

For mature transportation networks, structure changes are primarily due to the addition and removal of edges, the addition and removal of nodes being much less significant. In the past, the addition of edges has been studied mainly in the context of missing link prediction20,21 and network growth models22, whereas the removal of edges has been studied in contexts, such as network percolation23,24, attack and error tolerance25, dismantling strategies26, catastrophic failures27, synchronization and phase-transitions28, pruning processes based on removal of underutilized links29, and cascading failures30, to name a few. Using ML algorithms . . .

 



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