JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2019, Vol. 54 ›› Issue (5): 8-20.doi: 10.6040/j.issn.1671-9352.2.2018.200

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Trajectory model of area crowd based on WiFi positioning

Yang XU1,2(),Jian-zhong SUN1,Lei HUANG2,Xiao-yao XIE1   

  1. 1. Key Laboratory of Information and Computing Science of Guizhou Province, Guizhou Normal University, Guiyang 550001, Guizhou, China
    2. Guizhou Normal University-Guiyang Public Security Bureau Joint Research Centre for Information Security, Guiyang 550001, Guizhou, China
  • Received:2018-07-19 Online:2019-05-20 Published:2019-05-09
  • Supported by:
    国家自然科学基金重点项目(61332019);中央引导地方科技发展专项资金项目(黔科中引地20184008);贵州省科技合作计划重点项目(黔科合LH字20157763);住房和城乡建设部科学技术计划项目(2016-K3-009);全国统计科学研究项目(2016LY81)

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Abstract:

The existing indoor WiFi positioning methods are difficult to resolve the need of crowd trajectory analysis in the large-scale activities and inter-regional. Aiming at this problem, a new crowd location method based on trilateration measurement and signal strength indication (RSSI) for large-scale venues and complex environments is proposed. Crowd positioning in the region, crowd division inside and outside the region, and crowd flow analysis in the region can be achieved. A prediction algorithm based on probability and statistics is used to predict crowd trajectories. A crowd trajectory model in WiFi area is established. A cross regional crowd mobility trajectory model is further established to analyse the flow of crowd in large area inter regional. A proving system of WiFi regional crowd trajectory model is established. Using the data of 2016 Guiyang International Big Data Expo, data visualization analysis is carried out. In this way, the validity of the model is proved.

Key words: WiFi positioning, crowd trajectory, trilateration, received signal strength indication, location fingerprint

CLC Number: 

  • TP391

Fig.1

Trilateration diagram"

Fig.2

Principle of location fingerprint location"

Fig.3

Location diagram"

Fig.4

Venue diagram"

Fig.5

AP point error of venue edge"

Fig.6

Statistical model of crowd flow"

Fig.7

Regional division diagram"

Fig.8

Adjacency matrix of graph G"

Fig.9

Trajectory prediction model of hotspot area"

Fig.10

Trajectory model of crowd movement in different regions"

Table 1

MAC storage"

idstationMacchannelDetect ApNameRSSInoisefirstDetectTimelastDetectTime
15C:F7:C3:A2:**: **11ap203-80-1025/25/2016 00:00:005/26/2016 00:00:00
200:18:4C:04: **:**6ap207-85-915/25/2016 00:00:005/26/2016 00:00:00
348:62:76:18: **:**8ap203-85-925/25/2016 00:00:005/26/2016 00:00:00
448:62:76:18: **:**8ap203-84-925/25/2016 00:00:005/26/2016 00:00:00
5B8:B4:2E:33: **:**11ap202-66-905/25/2016 00:00:005/26/2016 00:00:00

Table 2

AP point information storage"

cs_idcs_apAliascs_macAddresscs_longcs_latcs_att
162ap3738:91:D5:B6:**:**109.*26.******
163ap33838:91:D5:B6:**:**109.*26.******
164ap31938:91:D5:B6:**:**109.*26.******
166ap34038:91:D5:B6:**:**109.*26.******
168ap2638:91:D5:B6:**:**109.*26.******

Fig.11

Number of crowd in venues during the big data Expo"

Fig.12

Real-time crowd statistics in venues"

Fig.13

Real-time statistics of the number of crowd in each venue"

Fig.14

Distribution of crowd heat in different booths"

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