Resilient operation of transportation networks via variable speed limits
Name
1609.06700.pdf
Description
Accepted version
Size
382.25 KB
Format
Unknown
Checksum (MD5)
ab82d18c410e2b84e5099ffc7cf49019
Author(s)
Yazicioglu, Ahmet Yasin
Roozbehani, Mardavij
Dahleh, Munther A
Date Issued
May 2017
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Yazicioglu, A. Yasin, Roozbehani, Mardavij and Dahleh, Munther A. 2017. "Resilient operation of transportation networks via variable speed limits."
Version
Original manuscript
Abstract
© 2017 American Automatic Control Council (AACC). In this paper, we investigate the use of variable speed limits for resilient operation of transportation networks, which are modeled as dynamical flow networks under local routing decisions. In such systems, some external inflow is injected to the so-called origin nodes of the network. The total inflow arriving at each node is routed to its operational outgoing links based on their current particle densities. The density on each link has first order dynamics driven by the difference of its incoming and outgoing flows. A link irreversibly fails if it reaches its jam density. Such failures may propagate in the network and cause a systemic failure. We show that larger link capacities do not necessarily help in preventing systemic failures under local routing. Accordingly, we propose the use of variable speed limits to operate the links below their capacities, when necessary, to compensate for the lack of global information and coordination in routing decisions. Our main result shows that systemic failures under feasible external inflows can always be averted through a proper selection of speed limits if the routing decisions are sufficiently responsive to local congestion and the network is initially uncongested. This is an attractive feature as it is much easier in practice to adjust the speed limits than to build more physical capacity or to alter routing decisions that are determined by social behavior.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.23919/acc.2017.7963830
