Resilient operation of transportation networks via variable speed limits

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.

[1]  Munther A. Dahleh,et al.  Robust Distributed Routing in Dynamical Networks—Part I: Locally Responsive Policies and Weak Resilience , 2013, IEEE Transactions on Automatic Control.

[2]  I. Dobson,et al.  A LOADING-DEPENDENT MODEL OF PROBABILISTIC CASCADING FAILURE , 2005, Probability in the Engineering and Informational Sciences.

[3]  André de Palma,et al.  Does providing information to drivers reduce traffic congestion , 1991 .

[4]  Carlos F. Daganzo,et al.  Fundamentals of Transportation and Traffic Operations , 1997 .

[5]  Adilson E Motter Cascade control and defense in complex networks. , 2004, Physical review letters.

[6]  Peter Elias,et al.  A note on the maximum flow through a network , 1956, IRE Trans. Inf. Theory.

[7]  Magnus Egerstedt,et al.  Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs , 2015, IEEE Transactions on Network Science and Engineering.

[8]  Markos Papageorgiou,et al.  Optimal Motorway Traffic Flow Control Involving Variable Speed Limits and Ramp Metering , 2010, Transp. Sci..

[9]  Munther A. Dahleh,et al.  Resilience of locally routed network flows: More capacity is not always better , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[10]  Ennio Cascetta,et al.  Transportation Systems Engineering: Theory and Methods , 2001 .

[11]  Asuman E. Ozdaglar,et al.  Informational Braess' Paradox: The Effect of Information on Traffic Congestion , 2016, Oper. Res..

[12]  Tim Roughgarden,et al.  How bad is selfish routing? , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[13]  Munther A. Dahleh,et al.  Robust Distributed Routing in Dynamical Networks–Part II: Strong Resilience, Equilibrium Selection and Cascaded Failures , 2013, IEEE Transactions on Automatic Control.

[14]  Calin Belta,et al.  Controlling a network of signalized intersections from temporal logical specifications , 2015, 2015 American Control Conference (ACC).

[15]  Kenneth A. Dawson,et al.  Bootstrap Percolation , 2009, Encyclopedia of Complexity and Systems Science.

[16]  Vasco M. Carvalho,et al.  The Network Origins of Aggregate Fluctuations , 2011 .

[17]  D. Newth,et al.  Optimizing complex networks for resilience against cascading failure , 2007 .

[18]  Giacomo Como,et al.  Throughput Optimality and Overload Behavior of Dynamical Flow Networks Under Monotone Distributed Routing , 2013, IEEE Transactions on Control of Network Systems.

[19]  Éva Tardos,et al.  Which Networks are Least Susceptible to Cascading Failures? , 2011, 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science.

[20]  Munther A. Dahleh,et al.  Volatility of Power Grids Under Real-Time Pricing , 2011, IEEE Transactions on Power Systems.