Traffic delay reduction via dynamic lane reversal and rerouting distributed algorithms

Evan Gravelle and Sonia Martínez
Proceedings of the 22nd. Int. Symposium on Mathematical Theory of Networks and Systems (MTNS 2016), MN, USA, July 2016

Abstract:

Traffic congestion is a major source of delays in modern road networks. Motivated by this, in this paper we propose two distributed algorithms to reduce delays: a dynamic lane reversal algorithm and a rerouting algorithm. When there is a large density of vehicles on one side of a road and a small density on the other, time can be saved by reallocating lanes from the less dense side to the more dense side, which motivates dynamic lane reversal. When a road has greater density than nearby roads, time can be saved by redirecting flow into the least congested roads, this motivates dynamic rerouting. Given a communication system between infrastructure and vehicles on the road, the local state of the network can be approximated and utilized by the algorithms to minimize travel time. Equilibrium conditions for the system are analyzed, convergence of the lane reversal algorithm to a critical point is proved, and overall performance is examined in simulation.

File: main.pdf

Bib-tex entry:

@InProceedings{EG-SM:16-mtns},
author = {E. Gravelle and S. Mart{\'\i}nez},
booktitle = {22nd. Int. Symposium on Mathematical Theory of Networks and Systems (MTNS 2016)},
title = {Traffic delay reduction via dynamic lane reversal and rerouting distributed algorithms},
month = {July},
year = {2016},
address ={Minneapolis, MN}
}