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Semantic enrichment of spatio-temporal trajectories for worker safety on construction sites

  • Muhammad ArslanEmail author
  • Christophe Cruz
  • Dominique Ginhac
Original Article
  • 16 Downloads

Abstract

Thousands of fatalities are reported from the construction industry every year and a high percentage of them are due to the unsafe worker movements which resulted in falling from heights, transportation accidents, exposure to harmful environments, and striking against or being struck by the moving equipment. To reduce such fatalities, a system is proposed to monitor worker movements on a construction site by collecting their raw spatio-temporal trajectory data and enriching it with the relevant semantic information. To acquire the trajectories, the use of an indoor positioning system (IPS) is considered. Bluetooth beacons are used for collecting spatio-temporal information of the building users. By means of an Android-based mobile application, neighboring beacons’ signals are selected, and a geo-localization technique is performed to get the unique pairs of users’ location coordinates. After pre-processing this collected data, three semantic enrichment techniques are used to construct semantically enriched trajectories which are as follows: (1) enrichment with the semantic points which maps site location identification to the trajectory points; (2) enrichment with the semantic lines which relies on the speed-based segmentation approach to infer user modes of transportation; (3) enrichment with the semantic region for mapping a complete trajectory on an actual building or a construction site zone. The proposed system will help in extracting multifaceted trajectory characteristics and generates semantic trajectories to enable the desired semantic insights for better understanding of the underlying meaningful worker movements using the contextual data related to the building environment. Generated semantic trajectories will help health and safety (H&S) managers in making improved decisions for monitoring and controlling site activities by visualizing site-zones’ density to avoid congestion, proximity analysis to prevent workers collisions, identifying unauthorized access to hazardous areas, and monitoring movements of workers and machinery to reduce transportation accidents.

Keywords

Safety Workers Construction Spatio-temporal data Fatalities BIM 

Notes

Acknowledgements

The authors thank the Conseil Régional de Bourgogne-Franche-Comté, the French government for their funding, SATT Grand-Est, and IUT-Dijon (http://iutdijon.u-bourgogne.fr). The authors also want to thank Orval Touitou for his technical assistance to this research work.

References

  1. 1.
    Shao B, Hu Z, Liu Q, Chen S, He W (2018) Fatal accident patterns of building construction activities in China. Saf Sci 111:253–263 (In Press).  https://doi.org/10.1016/j.ssci.2018.07.019 CrossRefGoogle Scholar
  2. 2.
    Poh CQ, Ubeynarayana CU, Goh YM (2018) Safety leading indicators for construction sites: a machine learning approach. Autom Constr 93:375–386.  https://doi.org/10.1016/j.autcon.2018.03.022 CrossRefGoogle Scholar
  3. 3.
    Stats.bls.gov. (2017) Census of fatal occupational injuries (CFOI) - current and revised data. http://stats.bls.gov/iif/oshcfoi1.htm. Accessed 28 September 2017
  4. 4.
    Fang W, Ding L, Luo H, Love PE (2018) Falls from heights: a computer vision-based approach for safety harness detection. Autom Constr 91:53–61.  https://doi.org/10.1016/j.autcon.2018.02.018 CrossRefGoogle Scholar
  5. 5.
    Zhou C, Ding LY (2017) Safety barrier warning system for underground construction sites using internet-of-things technologies. Autom Constr 83:372–389.  https://doi.org/10.1016/j.autcon.2017.07.005 CrossRefGoogle Scholar
  6. 6.
    Li H, Yang X, Skitmore M, Wang F, Forsythe P (2017) Automated classification of construction site hazard zones by crowd-sourced integrated density maps. Autom Constr 81:328–339.  https://doi.org/10.1016/j.autcon.2017.04.007 CrossRefGoogle Scholar
  7. 7.
    Pradhananga N, Teizer J (2013) Automatic spatio-temporal analysis of construction site equipment operations using GPS data. Autom Constr 29:107–122.  https://doi.org/10.1016/j.autcon.2012.09.004 CrossRefGoogle Scholar
  8. 8.
    Yu Y, Guo H, Ding Q, Li H, Skitmore M (2017) An experimental study of real-time identification of construction workers’ unsafe behaviors. Autom Constr 82:193–206.  https://doi.org/10.1016/j.autcon.2017.05.002 CrossRefGoogle Scholar
  9. 9.
    Park MW, Brilakis I (2016) Continuous localization of construction workers via integration of detection and tracking. Autom Constr 72:129–142.  https://doi.org/10.1016/j.autcon.2016.08.039 CrossRefGoogle Scholar
  10. 10.
    Teizer J, Cheng T (2015) Proximity hazard indicator for workers-on-foot near miss interactions with construction equipment and geo-referenced hazard areas. Autom Constr 60:58–73.  https://doi.org/10.1016/j.autcon.2015.09.003 CrossRefGoogle Scholar
  11. 11.
    Awolusi I, Marks E, Hallowell M (2018) Wearable technology for personalized construction safety monitoring and trending: review of applicable devices. Autom Constr 85:96–106.  https://doi.org/10.1016/j.autcon.2017.10.010 CrossRefGoogle Scholar
  12. 12.
    Cai H, Andoh AR, Su X, Li S (2014) A boundary condition based algorithm for locating construction site objects using RFID and GPS. Adv Eng Inform 28(4):455–468.  https://doi.org/10.1016/j.aei.2014.07.002 CrossRefGoogle Scholar
  13. 13.
    Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Technol 6(3):1–41.  https://doi.org/10.1145/2743025 CrossRefGoogle Scholar
  14. 14.
    Albanna BH, Moawad IF, Moussa SM, Sakr MA (2015) Semantic Trajectories: A Survey from Modeling to Application. In: Semantic trajectories: a survey from modeling to application. Information fusion and geographic information systems. Springer International Publishing, pp 59–76.  https://doi.org/10.1007/978-3-319-16667-4_4
  15. 15.
    Li H, Lu M, Hsu SC, Gray M, Huang T (2015) Proactive behavior-based safety management for construction safety improvement. Saf Sci 75:107–117.  https://doi.org/10.1016/j.ssci.2015.01.013 CrossRefGoogle Scholar
  16. 16.
    Baslyman M, Rezaee R, Amyot D, Mouttham A, Chreyh R, Geiger G, Stewart A, Sader S (2015) Real-time and location-based hand hygiene monitoring and notification: proof-of-concept system and experimentation. Pers Ubiquit Comput 19:667–688.  https://doi.org/10.1007/s00779-015-0855-y CrossRefGoogle Scholar
  17. 17.
    Lopez-Novoa U, Aguilera U, Emaldi M, López-de-Ipina D, Pérez-de-Albeniz I, Valerdi D, Iturricha I, Arza E (2017) Overcrowding detection in indoor events using scalable technologies. Pers Ubiquit Comput 21(3):507–519.  https://doi.org/10.1007/s00779-017-1012-6 CrossRefGoogle Scholar
  18. 18.
    Spaccapietra S, Parent C, Damiani ML, de Macedo JA, Porto F, Vangenot C (2008) A conceptual view on trajectories. Data Knowl Eng 65(1):126–146.  https://doi.org/10.1016/j.datak.2007.10.008 CrossRefGoogle Scholar
  19. 19.
    Arslan M, Cruz C, Roxin AM, Ginhac D (2018) Spatio-temporal analysis of trajectories for safer construction sites. Smart and Sustainable Built Environ 7(1):80–100.  https://doi.org/10.1108/SASBE-10-2017-0047
  20. 20.
    Guc B, May M, Saygin Y, Körner C (2008) Semantic annotation of GPS trajectories. Proceedings of the eleventh AGILE international conference on geographic information science. Girona, Spain, pp 1–9Google Scholar
  21. 21.
    Yan Z, Giatrakos N, Katsikaros V, Pelekis N, Theodoridis Y (2011) SeTraStream: semantic-aware trajectory construction over streaming movement data. Advances in spatial and temporal databases. Lect Notes Comput Sci 6849:367–385.  https://doi.org/10.1007/978-3-642-22922-0_22 CrossRefGoogle Scholar
  22. 22.
    Buchin M, Driemel A, Kreveld MV, Sacristan V (2010) An algorithmic framework for segmenting trajectories based on Spatio-Temporal Criteria. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems New York, NY, USA 202–211.  https://doi.org/10.1145/1869790.1869821
  23. 23.
    Dabiri S, Heaslip K (2018) Inferring transportation modes from GPS trajectories using a convolutional neural network. Transportation Res. Part C: Emerg Technol 86:360–371.  https://doi.org/10.1016/j.trc.2017.11.021
  24. 24.
    Balzano W, Sorbo MRD (2014) SeTra: a smart framework for GPS trajectories’ segmentation. International Conference on Intelligent Networking and Collaborative Systems Salerno, Italy 362–368.  https://doi.org/10.1109/INCoS.2014.106
  25. 25.
    Sankararaman S, Agarwal PK, Mølhave T, Pan J, Boedihardjo AP (2013) Model-driven matching and segmentation of trajectories. Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems New York, NY, USA, pp 234–243.  https://doi.org/10.1145/2525314.2525360
  26. 26.
    Panagiotakis C, Pelekis N, Kopanakis I, Ramasso E, Theodoridis Y (2012) Segmentation and sampling of moving object trajectories based on representativeness. IEEE Trans Knowl Data Eng 24(7):1328–1343.  https://doi.org/10.1109/TKDE.2011.39 CrossRefGoogle Scholar
  27. 27.
    Yan Z, Chakraborty D, Parent C, Spaccapietra S, Aberer K (2011) SeMiTri: a framework for semantic annotation of heterogeneous trajectories. Proceedings of the 14th International Conference on Extending Database Technology (EDBT/ICDT '11), ACM, New York, NY, USA :259–270.  https://doi.org/10.1145/1951365.1951398
  28. 28.
    Yan Z (2011) Semantic trajectories: computing and understanding mobility data. Doctoral dissertation. Lausanne, EPFL.  https://doi.org/10.5075/epfl-thesis-5144
  29. 29.
    Wu F, Li Z, Lee WC, Wang H, Huang Z (2015) Semantic annotation of mobility data using social media. Proceedings of the 24th International Conference on World Wide Web. Florence, Italy, pp 1253–1263.  https://doi.org/10.1145/2736277.2741675 Google Scholar
  30. 30.
    Furletti B, Cintia P, Renso C, Spinsanti L (2013) Inferring human activities from GPS tracks. Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing (UrbComp '13), Chicago, Illinois, USA;1–8.  https://doi.org/10.1145/2505821.2505830
  31. 31.
    Nogueira TP, Braga RB, de Oliveira CT, Martin H (2018) FrameSTEP: a framework for annotating semantic trajectories based on episodes. J of Expert Systems with Appl 92:533–545.  https://doi.org/10.1016/j.eswa.2017.10.004
  32. 32.
    de Graaff V, de By RA, van Keulen M (2016) Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas. Proceedings of the 31st Annual ACM Symposium on Applied Computing (SAC '16), Pisa, Italy: 552–559.  https://doi.org/10.1145/2851613.2851709
  33. 33.
    Fileto R, May C, Renso C, Pelekis N, Klein D, Theodoridis Y (2015) The Baquara2 knowledge-based framework for semantic enrichment and analysis of movement data. Data Knowl Eng 98:104–122.  https://doi.org/10.1016/j.datak.2015.07.010 CrossRefGoogle Scholar
  34. 34.
    Wu F, Wang H, Li Z, Lee WC, Huang Z (2015) SemMobi: a semantic annotation system for mobility data. Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion), Florence, Italy: 255–258.  https://doi.org/10.1145/2740908.2742837
  35. 35.
    Wan C, Zhu Y, Yu J, Shen Y (2018) SMOPAT: mining semantic mobility patterns from trajectories of private vehicles. Inf Sci 429:12–25.  https://doi.org/10.1016/j.ins.2017.10.043 MathSciNetCrossRefGoogle Scholar
  36. 36.
    Cai G, Lee K, Lee I (2018) Mining mobility patterns from geotagged photos through semantic trajectory clustering. Cybern Syst 49(4):234–256.  https://doi.org/10.1080/01969722.2018.1448236 CrossRefGoogle Scholar
  37. 37.
    Heijden K (2005) Scenarios: the art of strategic conversation, 2nd edn. John Wiley & Sons, HobokenGoogle Scholar
  38. 38.
    Carroll JM (2009) Scenario based design. Handbook of human-computer interaction, 2nd edn. North-Holland, pp 383–406.  https://doi.org/10.1016/B978-044481862-1.50083-2
  39. 39.
    Kontakt (2018) “Beacons,” available at:https://kontakt.io/ble-beacons-tags/. Last Accessed 05 Dec 2018
  40. 40.
    Lu CT, Lei PR, Peng WC, Su J (2011) A framework of mining semantic regions from trajectories. In: International Conference on Database Systems for Advanced Applications. Springer, Berlin, Heidelberg, pp 193–207.  https://doi.org/10.1007/978-3-642-20149-3_16 CrossRefGoogle Scholar
  41. 41.
    Ashbrook D, Starner T (2003) Using GPS to learn significant locations and predict movement across multiple users. Pers Ubiquit Comput 7(5):275–286.  https://doi.org/10.1007/s00779-003-0240-0 CrossRefGoogle Scholar
  42. 42.
    Palma AT, Bogorny V, Kuijpers B, Alvares LO (2008) A clustering-based approach for discovering interesting places in trajectories. Proceedings of the ACM symposium on Applied computing, New York, NY, USA: 863–868.  https://doi.org/10.1145/1363686.1363886
  43. 43.
    Cruz C (2017) Semantic trajectory modeling for dynamic built environments, IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp 468–476Google Scholar
  44. 44.
    Fileto R, Krüger M, Pelekis N, Theodoridis Y, Renso C (2013) Baquara: a holistic ontological framework for movement analysis using linked data. International Conference on Conceptual Modeling, Springer, Berlin, Heidelberg, pp 342–355Google Scholar
  45. 45.
    Bogorny V, Renso C, de Aquino AR, de Lucca Siqueira F, Alvares LO (2014) Constant–a conceptual data model for semantic trajectories of moving objects. Trans GIS 18(1):66–88CrossRefGoogle Scholar
  46. 46.
    Mohammadi MS, Isabelle M, Thérèse L, Christophe F (2017) A semantic modeling of moving objects data to detect the remarkable behavior. AGILE, WageningenGoogle Scholar
  47. 47.
    Yan Z, Parent C, Spaccapietra S, Chakraborty D (2010) A hybrid model and computing platform for spatio-semantic trajectories. Extended Semantic Web Conference: 60–75, Springer, Berlin, Heidelberg.  https://doi.org/10.1007/978-3-642-13486-9_5
  48. 48.
    Zhang S, Teizer J, Pradhanang N (2015) Global positioning system data to model and visualize workspace density in construction safety planning. Proceedings of the 32nd International Symposium on Automation and Robotics in Construction, Oulu, Finland, pp 1–7.  https://doi.org/10.22260/ISARC2015/0041
  49. 49.
    Costin AM, Teizer J, Schoner B (2015) RFID and BIM-enabled worker location tracking to support real-time building protocol and data visualization. Journal of Information Technology in Construction (ITcon) 20(29):495–517 http://www.itcon.org/2015/29 Google Scholar
  50. 50.
    Mahmoud H, Akkari N (2016) Shortest path calculation: a comparative study for location-based recommender system. World Symposium on Computer Applications & Research (WSCAR), Cairo, pp 1–5.  https://doi.org/10.1109/WSCAR.2016.16
  51. 51.
    Heng L, Shuang D, Skitmore M, Qinghua H, Qin Y (2016) Intrusion warning and assessment method for site safety enhancement. Saf Sci 84:97–107.  https://doi.org/10.1016/j.ssci.2015.12.004 CrossRefGoogle Scholar
  52. 52.
    Carbonari A, Giretti A, Naticchia B (2011) A proactive system for real-time safety management in construction sites. Autom Constr 20(6):686–698.  https://doi.org/10.1016/j.autcon.2011.04.019 CrossRefGoogle Scholar
  53. 53.
    Arslan M, Cruz C, Ginhac D (2018) Semantic enrichment of spatio-temporal trajectories for worker safety on construction sites. Procedia Computer Science 130:271–278.  https://doi.org/10.1016/j.procs.2018.04.039 CrossRefGoogle Scholar
  54. 54.
    Riaz Z, Arslan M, Kiani AK, Azhar S (2014) CoSMoS: a BIM and wireless sensor based integrated solution for worker safety in confined spaces. Autom Constr 45:96–106.  https://doi.org/10.1016/j.autcon.2014.05.010 CrossRefGoogle Scholar
  55. 55.
    Autodesk (2018) [online] Available at: http://paulaubin.com/_downloads/2017_AU/BIM128338-Aubin-AU2017.pdf. Accessed 13 Sep 2018

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratoire d’Informatique de Bourgogne - EA 7534University Bourgogne Franche-ComtéDijon CedexFrance
  2. 2.Laboratoire Imagerie et Vision Artificielle - EA 7535University Bourgogne Franche-ComtéDijon CedexFrance

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