Adaptive self‐organization vs static optimization

Gershenson, Carlos; Rosenblueth, David A.

doi:10.1108/03684921211229479

Cited by 18 publications

(6 citation statements)

References 31 publications

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“…In Fouladvand et al (2004) is proposed a traffic responsive signalization algorithm, following the concept of cutoff queue length and cut-off density, where is wanted to reduce the total delay of the intersecting streets, leading to the optimum signalization. The work in Gershenson & Rosenblueth (2012a) shows that using a self-organi-zing method the traffic lights control on cities can be improved for the drivers benefit. In Helbing et al (2005) it is presented a fluid dynamic model to simulate traffic on roads with different lengths and capacities, the model is designed to easily simulate congested and free traffic, also throughputs and travel times are improved considering self-organization principles to set the interaction between vehicles and traffic lights.…”

Section: Introductionmentioning

confidence: 99%

Soluciones de conducción para vehículos autónomos, y propuestas adaptativas para establecer los tiempos de semáforos y elegir rutas

Carrillo-González

2019

iit

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Two objectives are pursued in this article: 1) with adaptive solutions, improve the traffic flow by setting the time cycle of traffic lights at intersections and reduce the travel time by selecting the vehicles route (treated as separated problems). 2) Avoid driving conflicts among autonomous vehicles (which have defined trajectories) and these with a non-autonomous vehicle (which follows a free path). The traffic lights times are set with formulas that continuously recalculate the times values according the number of vehicles on the intersecting streets. For selecting the vehicles route an algorithm was developed, this calculates different routes (connected streets that conform a solution from the origin to the destination) and selects a route with low density. The results of the article indicate that the adaptive solutions to set the traffic lights times and to select the vehicles path, present a greater traffic flow and a shorter travel time, respectively, than conventional solutions. To avoid collisions among autonomous vehicles which follow a linear path, an algorithm was developed, this was successfully tested in different scenarios through simulations, besides the algorithm allows the interaction of a vehicle manually controlled (circulating without restrictions) with the autonomous vehicles. The algorithm regulates the autonomous vehicles acceleration (deceleration) and assigns the right of way among these and with the human controlled vehicle.

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Section: Introductionmentioning

confidence: 99%

Soluciones de conducción para vehículos autónomos, y propuestas adaptativas para establecer los tiempos de semáforos y elegir rutas

Carrillo-González

2019

iit

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“…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%

“…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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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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“…From the aspects of timing [6], induction and adaptive control [7], three kinds of control methods are studied separately, and point control is proposed for the reality of urban traffic vehicle, Wire control and surface control [8] [9].…”

Section: Introductionmentioning

confidence: 99%

Design of Intelligent Traffic Rules Planning System Based on Cellular Automation

Lin¹,

Liu²,

He³

et al. 2017

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017

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Abstract. In order to analyze the validity and scientificity of the traffic rules, we design an intelligent traffic rules analysis system. According to the complexity of traffic flow, we adopt a revised cellular automation to establish corresponding simulation model, in which each vehicle is regarded as a cellular. As the position and speed of each vehicle in the traffic flow are all continual, we alter the conventional cellar space and adjacent zone. We propose and quantify the indexes, including traffic volume, safety and level of service (LOS) of the road to evaluate the performance of traffic rules, and the rationality of road speed limits is analyzed. The results of simulation show that safety and traffic volume are contradicted. Hereafter, we propose two methods to balance the two indexes and provide recommendations for the traffic administrative branches. Meanwhile, the simulation results show the weakness of low road occupancy in light traffic. In terms of the weakness, we propose a new rule, under which vehicles with different speeds travel on different lanes and the speed limits for different lanes are different. Simulation results show that only in light traffic this rule is efficient.

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Adaptive self‐organization vs static optimization

Cited by 18 publications

References 31 publications

Soluciones de conducción para vehículos autónomos, y propuestas adaptativas para establecer los tiempos de semáforos y elegir rutas

Soluciones de conducción para vehículos autónomos, y propuestas adaptativas para establecer los tiempos de semáforos y elegir rutas

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

Design of Intelligent Traffic Rules Planning System Based on Cellular Automation

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