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SCIENTIA SINICA Informationis, Volume 49, Issue 4: 405-421(2019) https://doi.org/10.1360/N112018-00275

Key technology of lightweight Web3D online planning of metro fire escape

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  • ReceivedOct 17, 2018
  • AcceptedNov 12, 2018
  • PublishedApr 11, 2019

Abstract

Virtual training of metro fire evacuation definitely becomes considerably more efficient and convenient immigration of trainees from the PC desktop to mobile web browsers. However, web browsers face extreme difficulty in sustaining online visualization of large-scale building information modeling (BIM) data on metro, FDS smoking data, crowding passengers, and planning of fire evacuation path simultaneously due to the weak rendering capability and limited networking bandwidth. Herein, a novel lightweight Web3D technology roadmap is proposed to address these problems. First, the lightweight preprocessing of metro BIM data is performed by semantic and geometric retrieval of repetitive entities. Second, crowding passengers are visualized online by reusing several avatars and behavior randomly in terms of textures and scales in a lightweight manner. Third, substantial smoking data are reduced by redundancy elimination and voxel normalization and rendered online with spirit texture particle. Finally, the backbone escape paths are extracted by clustering the virtual evacuation traces that are collected by virtual reality devices, such as HTC VIVE, as initial heuristics, and a lightweight metro fire evacuation planning evacuation based on adaptive ant colony optimization is realized on mobile web browsers. Numerous tests have been conducted online, and experimental results show that the proposed method can be loaded remotely in a few seconds and visualized on mobile web browsers smoothly.


Funded by

中央基本业务经费基金——科技创新计划(0200219153)

中央基本业务经费基金——重点领域交叉合作项(2100219066)

吉林省科技攻关计划项目课题(20170203004GX)

吉林省科技攻关计划项目课题(20170203003GX)


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  • Figure 1

    (Color online) Overall technology roadmap

  • Figure 2

    (Color online) Screenshots of two typical metro stations. (a) Che Kung Temple metro station; (b) Tongji University metro station

  • Figure 3

    (Color online) Online interactive Web3D visualization of large scale escaping virtual avatars. Large scale virtual avatars online rendering (a) technology roadmap and (b) effect

  • Figure 4

    (Color online) Lightweight preprocessing of huge FDS data. (a) Technical roadmap of lightweighting FDS data; (b) visual effects of lightweight smoke rendering

  • Figure 5

    (Color online) Normalization of smoke density at the ${2}^{8}$ levels of transparency

  • Figure 6

    (Color online) Virtual traces clustering and firing evacuation path planning

  • Figure 9

    (Color online) (a) Valid path; (b) invalid path due to failure of escaping; (c) invalid path due to timeout; protectłinebreak (d) invalid path due to redundancy

  • Table 1   Comparison of fire evacuation simulation platforms
    Stand-alone On-line Web-side The Web-side system
    system system system in this paper
    Dependence Application Client Plug-in No plug-in
    User support Single user Multi-user Multiplayer online Multiplayer online
    Smoke simulation accuracy Very high High Normal High
    Scene size Huge Medium Small Huge
    Usability Low Medium High High
    Portability Low Medium High High
  • Table 2   Comparison of time consumption between two algorithms (s)
    100 people 200 people 300 people 400 people 500 people
    Traditional ACO algorithm 273.722 467.396 707.625 985.688 1261.27
    The eAACO algorithm in this paper 68.875 65.655 69.5 70.21 70.5
  • Table 3   Hardware configuration of test environment
    PC testing environment iOS testing environment Android testing environment
    CPU i7-7700HQ Apple A10 Snapdragon 820 (MSM8996)
    Memory 16 GB 2 GB 4 GB
    GPU Nvidia GTX M1070 PowerVR GT7600 Adreno530
    OS Windows 10 64 Bit iOS 11.4 Android 6.0
    Network 4G wireless network 4G wireless network 4G wireless network
  • Table 4   Occupied memory (MB)
    100 people 200 people 300 people 400 people 500 people
    PC 293 354 401 411 412
    iOS 221 299 282 284 314
    Android 322 339 346 358 372
  • Table 5   Rendering frame rate or frequency (Hz)
    100 people 200 people 300 people 400 people 500 people
    PC 86 65 52 45 35
    iOS 37 25 22 18 15
    Android 26 26 23 21 18
  • Table 6   Comparison of FDS data before and after lightweight processing
    FDS data 1 FDS data 2
    Raw FDS data volume (MB) 85.3 229.2
    Lightweight FDS data volume (MB) 3.26 7.76

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