Pedestrian Flow Identification and Occupancy Prediction for Indoor Areas
Indoor localization is used to locate objects and people within buildings where outdoor tracking tools and technologies cannot provide precise results. This paper aims to improve analytics research, focusing on data collected through indoor localization methods. Smart devices recurrently broadcast a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/9/4301 |
| _version_ | 1797601738456301568 |
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| author | Nikolaos Tsiamitros Tanmaya Mahapatra Ioannis Passalidis Kailashnath K Georgios Pipelidis |
| author_facet | Nikolaos Tsiamitros Tanmaya Mahapatra Ioannis Passalidis Kailashnath K Georgios Pipelidis |
| author_sort | Nikolaos Tsiamitros |
| collection | DOAJ |
| description | Indoor localization is used to locate objects and people within buildings where outdoor tracking tools and technologies cannot provide precise results. This paper aims to improve analytics research, focusing on data collected through indoor localization methods. Smart devices recurrently broadcast automatic connectivity requests. These packets are known as Wi-Fi probe requests and can encapsulate various types of spatiotemporal information from the device carrier. In addition, in this paper, we perform a comparison between the Prophet model and our implementation of the autoregressive moving average (ARMA) model. The Prophet model is an additive model that requires no manual effort and can easily detect and handle outliers or missing data. In contrast, the ARMA model may require more effort and deep statistical analysis but allows the user to tune it and reach a more personalized result. Second, we attempted to understand human behaviour. We used historical data from a live store in Dubai to forecast the use of two different models, which we conclude by comparing. Subsequently, we mapped each probe request to the section of our place of interest where it was captured. Finally, we performed pedestrian flow analysis by identifying the most common paths followed inside our place of interest. |
| first_indexed | 2024-03-11T04:06:49Z |
| format | Article |
| id | doaj.art-5ead526c71014f4d8f47677e370ada43 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:06:49Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5ead526c71014f4d8f47677e370ada432023-11-17T23:42:27ZengMDPI AGSensors1424-82202023-04-01239430110.3390/s23094301Pedestrian Flow Identification and Occupancy Prediction for Indoor AreasNikolaos Tsiamitros0Tanmaya Mahapatra1Ioannis Passalidis2Kailashnath K3Georgios Pipelidis4Ariadne Maps GmbH, Munich, Brecherspitzstraße 8, 81541 Munich, GermanyDepartment of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani 333031, IndiaInstitute for Informatics, Technical University of Munich, Boltzmannstraße 3, 85748 Garching, GermanyDepartment of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani 333031, IndiaAriadne Maps GmbH, Munich, Brecherspitzstraße 8, 81541 Munich, GermanyIndoor localization is used to locate objects and people within buildings where outdoor tracking tools and technologies cannot provide precise results. This paper aims to improve analytics research, focusing on data collected through indoor localization methods. Smart devices recurrently broadcast automatic connectivity requests. These packets are known as Wi-Fi probe requests and can encapsulate various types of spatiotemporal information from the device carrier. In addition, in this paper, we perform a comparison between the Prophet model and our implementation of the autoregressive moving average (ARMA) model. The Prophet model is an additive model that requires no manual effort and can easily detect and handle outliers or missing data. In contrast, the ARMA model may require more effort and deep statistical analysis but allows the user to tune it and reach a more personalized result. Second, we attempted to understand human behaviour. We used historical data from a live store in Dubai to forecast the use of two different models, which we conclude by comparing. Subsequently, we mapped each probe request to the section of our place of interest where it was captured. Finally, we performed pedestrian flow analysis by identifying the most common paths followed inside our place of interest.https://www.mdpi.com/1424-8220/23/9/4301indoor positioningindoor localizationpedestrian flow analysisARMA modelProphet model |
| spellingShingle | Nikolaos Tsiamitros Tanmaya Mahapatra Ioannis Passalidis Kailashnath K Georgios Pipelidis Pedestrian Flow Identification and Occupancy Prediction for Indoor Areas Sensors indoor positioning indoor localization pedestrian flow analysis ARMA model Prophet model |
| title | Pedestrian Flow Identification and Occupancy Prediction for Indoor Areas |
| title_full | Pedestrian Flow Identification and Occupancy Prediction for Indoor Areas |
| title_fullStr | Pedestrian Flow Identification and Occupancy Prediction for Indoor Areas |
| title_full_unstemmed | Pedestrian Flow Identification and Occupancy Prediction for Indoor Areas |
| title_short | Pedestrian Flow Identification and Occupancy Prediction for Indoor Areas |
| title_sort | pedestrian flow identification and occupancy prediction for indoor areas |
| topic | indoor positioning indoor localization pedestrian flow analysis ARMA model Prophet model |
| url | https://www.mdpi.com/1424-8220/23/9/4301 |
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