# Learning the optimally coordinated routes from the statistical mechanics of polymers

## TL;DR

This paper introduces a novel route coordination method inspired by polymer physics and statistical mechanics, demonstrating significant travel time savings in simulations of real and synthetic networks, thus offering an effective solution for traffic congestion.

## Contribution

It applies the polymer analogy and spin glass theory to develop a route coordination algorithm that incorporates congestion functions, improving traffic flow management.

## Key findings

- Significant travel time savings in simulated networks.
- Effective reduction of congestion in major city networks.
- Polymer physics-based approach outperforms uncoordinated methods.

## Abstract

Many major cities suffer from severe traffic congestion. Road expansion in the cites is usually infeasible, and an alternative way to alleviate traffic congestion is to coordinate the route of vehicles. Various path selection and planning algorithms are thus proposed, but most existing methods only plan paths separately and provide un-coordinated solutions. Recently, an analogy between the coordination of vehicular routes and the interaction of polymers is drawn; the spin glass theory in statistical physics is employed to optimally coordinate transportation routes. To further examine the advantages brought by path coordination, we incorporate the link congestion function developed by the Bureau of Public Roads (BPR) into the polymer routing algorithm. We then estimate in simulations the traveling time of all users saved by the polymer-BPR algorithm in randomly generated networks and real transportation networks in major cities including London, New York and Beijing. We found that a large amount of traveling time is saved in all studied networks, suggesting that the approach inspired by polymer physics is effective in minimizing the traveling time via path coordination, which is a promising tool for alleviating traffic congestions.