Latest revision as of 21:36, 3 February 2021

Abstract

While many models of traffic flow predict the instabilities commonly observed-particularly at higher traffic densities-there are few suggestions for suppressing them. A method is described here for suppressing instabilities, thereby reducing gas consumption, accidents, wear and tear on vehicles and roadways as well as travel times while increasing traffic throughput. The method uses information about the following vehicle as well as the leading vehicle. Using information from both sources allows the gain of feedback to be reduced below one, thus eliminating the instability characteristic of “car following.” The needed inputs to the control system can be provided by machine vision (or radar or lidar). Previous proposals for smoothing traffic flow instabilities do not use information about the vehicles behind-“car following” cruise control methods, for example, focus only on the vehicle ahead. The method presented here is based on information flowing both downstream and upstream, in distinction to traditional approaches where information flows only upstream.

Original document

The different versions of the original document can be found in:

• http://people.csail.mit.edu/bkph/articles/Suppressing_Traffic_Flow%20Instabilities_IEEE_ITS_2013.pdf,

https://dblp.uni-trier.de/db/conf/itsc/itsc2013.html#Horn13,

https://ieeexplore.ieee.org/document/6728204,

http://ieeexplore.ieee.org/document/6728204,

https://trid.trb.org/view.aspx?id=1352654,

https://doi.org/10.1109/ITSC.2013.6728204,

https://academic.microsoft.com/#/detail/2081632288

• http://xplorestaging.ieee.org/ielx7/6712176/6728201/06728204.pdf?arnumber=6728204,

http://dx.doi.org/10.1109/itsc.2013.6728204

• Instabilities_IEEE_ITS_2013.pdf http://people.csail.mit.edu/bkph/articles/Suppressing_Traffic_Flow Instabilities_IEEE_ITS_2013.pdf

• [ ]