2011
DOI: 10.1002/cplx.20392
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Self‐organizing traffic lights at multiple‐street intersections

Abstract: The elementary cellular automaton following rule 184 can mimic particles flowing in one direction

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Cited by 74 publications
(81 citation statements)
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“…In urban street networks, spill backs can lead to hypercongestion when queues become so long that flows through intersections are reduced. Traffic management may reduce such problems by, e.g., using traffic lights that adapt to the length of the queues on the different roads that lead into intersections (Gershenson, 2005;de Gier et al, 2011;Gershenson and Rosenblueth, 2012). Then priority can be given to flows such that intersection capacity is better utilized while spillbacks can be minimized.…”
Section: Turning Hypercongestion Into Queuesmentioning
confidence: 99%
“…In urban street networks, spill backs can lead to hypercongestion when queues become so long that flows through intersections are reduced. Traffic management may reduce such problems by, e.g., using traffic lights that adapt to the length of the queues on the different roads that lead into intersections (Gershenson, 2005;de Gier et al, 2011;Gershenson and Rosenblueth, 2012). Then priority can be given to flows such that intersection capacity is better utilized while spillbacks can be minimized.…”
Section: Turning Hypercongestion Into Queuesmentioning
confidence: 99%
“…000 0 0 0 001 0 0 0 010 0 1 0 011 1 1 1 100 1 1 0 101 1 1 0 110 0 1 0 111 1 1 1 This city traffic model is conservative, i.e., the density of vehicles ρ (proportion of cells with a value of one) is constant in time. It is straightforward to extend such a model to an hexagonal grid, allowing for more complex intersections [33].…”
Section: Traffic Modelmentioning
confidence: 99%
“…Thus, several decentralized control schemes have been proposed so far [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. A typical example is the selforganizing traffic signal (SOTL) method [10][11][12][13][14][15][16][17][18][19], where each traffic signal is controlled by several local rules such that the traffic signals can immediately adapt to the current situation. Other examples of decentralized control schemes are based on coupled oscillators [20][21][22]29], neural networks [23], near-future prediction [28], and so on.…”
Section: Collective Dynamics 1 A5:1-18 (2016)mentioning
confidence: 99%
“…Control scheme 2: Green wave method [8,[10][11][12][13][14] The green wave method is the same as control scheme 1 in that traffic signals are switched for every period T 0 without any sensory feedback. However, the offset is defined so that cars moving from south to north and from west to east at their maximum velocity are not trapped by red signals.…”
Section: Comparison With Other Control Schemesmentioning
confidence: 99%
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