Mitigating Congestion in Complex Transportation Networks via Maximum Entropy

TL;DR

This paper introduces a novel, low-cost method for reducing congestion in complex transportation networks by maximizing entropy rate, which is based on local information and improves traffic efficiency.

Contribution

It analytically links entropy rate to congestion, proposing a practical capacity allocation method that outperforms previous degree-biased strategies.

Findings

Effective congestion mitigation in various network models

Method outperforms degree-biased approaches

Applicable with local, observable network information

Abstract

In this paper, we reveal the relationship between entropy rate and the congestion in complex network and solve it analytically for special cases. Finding maximizing entropy rate will lead to an improvement of traffic efficiency, we propose a method to mitigate congestion by allocating limited traffic capacity to the nodes in network rationally. Different from former strategies, our method only requires local and observable information of network, and is low-cost and widely applicable in practice. In the simulation of the phase transition for various network models, our method performs well in mitigating congestion both locally and globally. By comparison, we also uncover the deficiency of former degree-biased approaches. Owing to the rapid development of transportation networks, our method may be helpful for modern society.