City-Scale Pollution Aware Traffic Routing by Sampling Max Flows Using MCMC

A significant cause of air pollution in urban areas worldwide is the high volume road traffic. Long-term exposure to severe can serious health issues. One approach towards tackling this problem design a pollution-aware traffic routing policy that balances multiple objectives i) avoiding extreme any area ii) enabling short transit times, and iii) making effective use capacities. We propose novel sampling-based for problem. give first construction Markov Chain sample integer max flow solutions planar graph, with theoretical guarantees probabilities depend on aggregate length. designed using diverse samples simulated real-world maps SUMO simulator. observe considerable decrease when experimented large cities across world compared other approaches.