# City-scale Pollution Aware Traffic Routing by Sampling Max Flows using MCMC

## TL;DR

This paper introduces a novel sampling-based method using MCMC to generate pollution-aware traffic routing policies that effectively reduce severe pollution in city traffic networks.

## Contribution

It presents the first Markov Chain construction for sampling integer max flow solutions in planar graphs with theoretical guarantees, applied to urban traffic routing.

## Key findings

- Significant reduction in pollution hotspots in simulated city traffic.
- Effective balancing of transit time and road capacity utilization.
- Outperforms existing approaches in real-world traffic simulations.

## Abstract

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.