Self-Organizing Traffic Lights: A Realistic Simulation

Cools, Seung-Bae; Gershenson, Carlos; D’Hooghe, Bart

doi:10.1007/978-1-4471-5113-5_3

Cited by 156 publications

(97 citation statements)

References 23 publications

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“…Its simplicity allows the clear identification of phase transitions. We have also worked with realistic city traffic models [30,31], obtaining similar results. We are in the process of developing a more realistic city traffic model to bridge both lines of research.…”

Section: Discussionmentioning

confidence: 79%

Deliberative Self-Organizing Traffic Lights with Elementary Cellular Automata

2017

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Self-organizing traffic lights have shown considerable improvements compared to traditional methods in computer simulations. Self-organizing methods, however, use sophisticated sensors, increasing their cost and limiting their deployment. We propose a novel approach using simple sensors to achieve self-organizing traffic light coordination. The proposed approach involves placing a computer and a presence sensor at the beginning of each block; each such sensor detects a single vehicle. Each computer builds a virtual environment simulating vehicle movement to predict arrivals and departures at the downstream intersection. At each intersection, a computer receives information across a data network from the computers of the neighboring blocks and runs a self-organizing method to control traffic lights. Our simulations showed a superior performance for our approach compared with a traditional method (a green wave) and a similar performance (close to optimal) compared with a self-organizing method using sophisticated sensors but at a lower cost. Moreover, the developed sensing approach exhibited greater robustness against sensor failures.

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Section: Discussionmentioning

confidence: 79%

Deliberative Self-Organizing Traffic Lights with Elementary Cellular Automata

2017

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“…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 4: Self-organizing traffic light (SOTL) method [10][11][12][13][14][15][16][17][18][19] The SOTL method was first proposed by Gershenson et al in 2005 [10] and extended in later studies [11][12][13][14]. Here, we adopted the extended version, which consists of the following six rules (the original version [10] consists of only rules 1-3):…”

Section: Comparison With Other Control Schemesmentioning

confidence: 99%

Autonomous Decentralized Control of Traffic Signals that can Adapt to Changes in Traffic

2016

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A major challenge for traffic signal control is adapting to unpredictable changes in traffic. To address this issue, we propose an autonomous decentralized control scheme for traffic signals that is based on physics. More specifically, "virtual impulses" given by red signals or preceding cars, which are defined in a similar manner as the impulses generally used in physics, are calculated at each traffic signal by using an optimal velocity model, and traffic signals are switched to reduce these virtual impulses. We performed simulations under various traffic conditions, and the results showed that the proposed control scheme works adaptively and resiliently in response to each set of circumstances. Thus, the virtual impulse can be a key physical quantity for designing adaptive traffic systems.

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“…An example of adaptation through self-organization has been proposed for traffic light coordination (Gershenson, 2005;Cools et al, 2007;Gershenson & Rosenblueth, In Press). Instead of trying to blindly predict when an average flow of vehicles should arrive at intersections, each traffic light gives preference to the streets with higher demand.…”

Section: Self-organizationmentioning

confidence: 99%

Facing Complexity: Prediction vs. Adaptation

Gershenson

2013

Understanding Complex Systems

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One of the presuppositions of science since the times of Galileo, Newton, Laplace, and Descartes has been the predictability of the world. This idea has strongly influenced scientific and technological models. However, in recent decades, chaos and complexity have shown that not every phenomenon is predictable, even if it is deterministic. If a problem space is predictable, in theory we can find a solution via optimization. Nevertheless, if a problem space is not predictable, or it changes too fast, very probably optimization will offer obsolete solutions. This occurs often when the immediate solution affects the problem itself. An alternative is found in adaptation. An adaptive system will be able to find by itself new solutions for unforeseen situations.

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Self-Organizing Traffic Lights: A Realistic Simulation

Cited by 156 publications

References 23 publications

Deliberative Self-Organizing Traffic Lights with Elementary Cellular Automata

Deliberative Self-Organizing Traffic Lights with Elementary Cellular Automata

Autonomous Decentralized Control of Traffic Signals that can Adapt to Changes in Traffic

Facing Complexity: Prediction vs. Adaptation

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