Running the algorithm over and over again turns it into a classic case of parallel processing, which is exactly the trick our (relatively slow, at the neuronal level) brains employ to outwit computers on most tasks. It's also the very definition of "swarm intelligence", in which groups of animals operating according to simple rules can solve very tough problems.
Other biomimetic algorithms exist, some of which simulate ants, bees or swarms of insects. Further afield, there are algorithms that simulate genes and natural selection, and even primitive nervous systems. They're all part of a much broader class of solutions that seek the best possible solution to any particular optimisation problem.
Knowing that these solutions can operate on real-world problems doesn't mean they've been incorporated, yet. At this stage, the research by Qin Liu and Jianmin Xu has yet to go beyond the modelling stage. But it's reasonable to expect that as the components of cities become ever more interconnected, centralised traffic systems will incorporate whichever combination of optimisation algorithms – out of the thousands available – will be best at reducing urban China's infamous traffic.