Effect of density control in partially observable asymmetric-exit evacuation under guidance: Strategic suggestion under time delay

Gao, Fengqiang; Yan, Yuyue; Chen, Zhihao; Zheng, Linxiao; Ren, Huan

doi:10.1016/j.amc.2021.126838

Cited by 9 publications

(5 citation statements)

References 36 publications

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“…Saleem M and Saifullah Y proposed a new technique for designing a kind of special arrays utilizing strategy encoding elements on the surface of the ground, for the reduction of broadband bistatic and monostatic backscatter energy levels. The results indicate that the antenna array significantly reduces the backscattering field of both x-polarized and y-polarized incident waves, and the measurement outcomes of the prototype produced are in agreement with the experimental results [8]. Zhang Z et al proposed an AMC feature fusion scheme based on convolutional neural networks (CNN).…”

Section: Related Worksupporting

confidence: 70%

Signal Automatic Modulation Classification and Recognition in View of Deep Learning

Xu,

2023

IEEE Access

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With the advancement of 5G technology, wireless communication resources such as channels and spectrum become scarce. This necessitates ensuring the efficiency and security of signal modulation and demodulation, which imposes higher requirements for wireless communication systems. However, signal modulation has the problems of large amount of data, low recognition accuracy and various types. In this study, a classification network of automatic modulation classification recognition algorithm for signal-to-noise ratio is proposed to solve the problem that traditional noise reduction algorithms will damage signals with high signal-to-noise ratio, consequently reducing the accuracy of signal recognition. In order to solve the problem of high complexity of network model algorithm, in particular, a signal automatic modulation classification and recognition algorithm based on neural network autoencoder is proposed. Experimental results show that the accuracy of signal automatic modulation classification recognition in the algorithm increases as the increase of modulation signals and tends to be stable. When the modulation signal is 0dB, the recognition accuracy gradually converges to the highest, and reaches 81.6% when the modulation signal is 18dB. In contrast, the DenseNet algorithm has the lowest recognition accuracy, with only 77.5% recognition accuracy when the signal modulation classification is 18dB, a difference of 4.1%. This indicates that the algorithm performs exceptionally well in automatic signal modulation classification, and its complexity is lower than other comparative network models, providing certain advantages.

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Section: Related Worksupporting

confidence: 70%

Signal Automatic Modulation Classification and Recognition in View of Deep Learning

Xu,

2023

IEEE Access

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“…According to previous works, 41,42 the Moore type cellular automata model can describe the movement rule of passengers more realistically. Therefore, this paper selects the Moore neighborhood type with eight neighbors [43][44][45] as shown in Figure 1. Each cell has two states, either occupied by passengers or empty.…”

Section: Passenger Motion Modelmentioning

confidence: 99%

Stair/escalator/elevator selection behavior of passengers in subway stations based on the fuzzy logic theory

et al. 2023

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The behavior of stair/escalator/elevator selection of passengers at the subway platform could directly affect the travel efficiency and even the service level of the station. This paper proposes a stair/escalator/elevator selection model of passengers at the subway platform, where the cellular automata model is used to simulate passenger movement, and the fuzzy logic theory is used to describe selection behavior. The distance to the stair/escalator/elevator and the density of passengers in front of the facility are selected as the main influence factors. The rationality and effectiveness of the selection model are verified by comparing the field data with simulation results of passengers choosing different stairs/escalators/elevators. The relationship between passenger quantity, passenger speed, escalator quantity, escalator location, and the traffic rate is analyzed based on simulation results, which can provide a theoretical reference for the facility layout optimization at the platform floor of the subway station.

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“…Guiding strategies are defined by their objectives and by the measures taken to achieve the objectives. We aim at reducing travel times and densities as in PLOS ONE [39]. As measure we provide pedestrians in a certain zone with information on which route they should take, following [8].…”

Section: Model Of the Dynamic Pedestrian Management Systemmentioning

confidence: 99%

“…The goal is to offer an environment to test guiding strategies. Gao et al [ 39 ] switch signals on and off in the hope to reduce densities and travel times in a simulated evacuation scenario. They also investigate what effect partially available or delayed measurement data have within their model.…”

Section: Introductionmentioning

confidence: 99%

Guiding crowds when facing limited compliance: Simulating strategies

Mayr

Köster²

2022

PLoS ONE

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At traffic hubs, it is important to avoid congestion of pedestrian streams to ensure safety and a good level of service. This presents a challenge, since distributing crowds on different routes is much more difficult than opening valves to, for example, regulate fluid flow. Humans may or may not comply with re-directions suggested to them typically with the help of signage, loudspeakers, apps, or by staff. This remains true, even if they perceive and understand the suggestions. Yet, simulation studies so far have neglected the influence of compliance. In view of this, we complement a state-of-the-art model of crowd motion and crowd behavior, so that we can vary the compliance rate. We consider an abstracted scenario that is inspired by a metro station in the city of Munich, where traffic regulators wish to make some passengers abandon the obviously shortest route so that the flow evens out. We investigate the effect of compliance for two very simple guiding strategies. In the first strategy, we alternate routes. In the second strategy, we recommend the path with the lowest crowd density. We observe that, in both cases, it suffices to reroute a small fraction of the crowd to reduce travel times. But we also find that taking densities into account is much more efficient when facing low compliance rates.

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Effect of density control in partially observable asymmetric-exit evacuation under guidance: Strategic suggestion under time delay

Cited by 9 publications

References 36 publications

Signal Automatic Modulation Classification and Recognition in View of Deep Learning

Signal Automatic Modulation Classification and Recognition in View of Deep Learning

Stair/escalator/elevator selection behavior of passengers in subway stations based on the fuzzy logic theory

Guiding crowds when facing limited compliance: Simulating strategies

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