City-scale Pollution Aware Traffic Routing by Sampling Max Flows using MCMC
This addresses air pollution and health issues in urban areas by providing a novel routing method, though it is incremental as it builds on existing traffic simulation and optimization techniques.
The paper tackled the problem of urban air pollution from traffic by designing a pollution-aware traffic routing policy that balances pollution avoidance, transit times, and road capacity use, resulting in a considerable decrease in areas with severe pollution in simulations on real-world city maps.
A significant cause of air pollution in urban areas worldwide is the high volume of road traffic. Long-term exposure to severe pollution can cause serious health issues. One approach towards tackling this problem is to design a pollution-aware traffic routing policy that balances multiple objectives of i) avoiding extreme pollution in any area ii) enabling short transit times, and iii) making effective use of the road capacities. We propose a novel sampling-based approach for this problem. We provide the first construction of a Markov Chain that can sample integer max flow solutions of a planar graph, with theoretical guarantees that the probabilities depend on the aggregate transit length. We designed a traffic policy using diverse samples and simulated traffic on real-world road maps using the SUMO traffic simulator. We observe a considerable decrease in areas with severe pollution when experimented with maps of large cities across the world compared to other approaches.