Identyfikatory
Warianty tytułu
Big data in planning of road infrastructure
Języki publikacji
Abstrakty
W artykule wskazano, że przedsięwzięcia budownictwa drogowego mogą być rozpatrywane jako projekty, zorganizowane według ogólnej metodyki zarządzania projektami z uwzględnieniem specyfiki branży. Wskazano potrzebę rozpatrywania tego typu przedsięwzięć w perspektywie całego cyklu życia obiektu budowlanego. Sprecyzowano też, że zarządzanie ryzykiem odgrywa kluczową rolę w zarządzaniu projektem. Sformułowano mocne i słabe strony, a także szanse i zagrożenia wynikające ze stosowania dużych zbiorów danych w zarządzaniu poszczególnymi obszarami projektów budowy infrastruktury drogowej. Pokazano możliwości zarządzania projektami z wykorzystaniem danych gromadzonych w sposób cyfrowy, w formie Big Data (BD). Zadanie to wykonano omawiając użyteczność BD zarówno w odniesieniu do poszczególnych wymiarów zarządzania projektami, jak i poszczególnych faz cyklu życia obiektów budowlanych.
The article described that road construction projects can be considered as projects that are covered by the general management methodology with taking into account the specificity of the industry. It was pointed out that there is a need for considering such projects in the perspective of the whole life cycle of the structure. It was also specified that risk management plays a key role in project management. Strengths and weaknesses have been formulated, as well as the opportunities and threats resulting from the use of large data sets in the management of individual areas of road infrastructure projects. There were identified project management capabilities with use of data collected digitally, namely Big Data (BD). The utility of BD was discussed both in terms of particular constraints of project management as well as individual phases of the structure.
Rocznik
Tom
Strony
1725--1732, CD
Opis fizyczny
Bibliogr. 26 poz., tab., wykr.
Twórcy
Bibliografia
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- 8. Cunbin L., Yunqi L., Shuke L. (2015). Human resources risk element transmission model of construction project based on system dynamic. "Open Cybernetics and Systemics Journal", 9, ss. 295-305.
- 9. Formoso C., Bølviken T., Rooke J., Koskela L. (2015). A conceptual Framework for the Prescriptive Causal Analysis of Construction Waste. "23rd Annual Conference of the International Group for Lean Construction", ss. 454-461. Retrieved from http://www.iglc.net/Papers/Details/1162.
- 10. Goh B. H. (2005). The dynamic effects of the Asian financial crisis on construction demand and tender price levels in Singapore. "Building and Environment", 40(2), ss. 267-276. http://doi.org/10.1016/j.buildenv.2004.07.012.
- 11. Górecki J. (2015). Maturity of project management in polish and foreign construction companies. "Foundations of Management", 7(1), ss. 71–82. http://doi.org/10.1515/fman-2015-0026.
- 12. Guo S., Luo H., Yong L. (2015). A Big Data-based Workers Behavior Observation in China Metro Construction. "Procedia Engineering" (Vol. 123, ss. 190-197). http://doi.org/10.1016/j.proeng.2015.10.077.
- 13. Hilbert M. (2015). Big Data for Development: A Review of Promises and Challenges. "Development Policy Review", 34(1), ss. 135-174. http://doi.org/http://doi.org/10.1111/dpr.12142.
- 14. Hua X., Wang J., Lei L., Zhou B., Zhang X., Liu P. (2016). HTDMS: A system for traffic big data management. "Communications in Computer and Information Science" (Vol. 626, ss. 85-96). http://doi.org/10.1007/978-981-10-2209-8_8.
- 15. Imawan A., Kwon J. (2015). A timeline visualization system for road traffic big data. "Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015" (ss. 2928–2929). http://doi.org/10.1109/BigData.2015.7364125.
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- 19. NSF (National Science Foundation) (2012), Core Techniques and Technologies for Advancing Big Data Science & Engineering (BIGDATA), Washington.
- 20. Pan Y., Tian Y., Liu X., Gu D., Hua G. (2016). Urban Big Data and the Development of City Intelligence. "Engineering". http://doi.org/10.1016/J.ENG.2016.02.003.
- 21. Pengcheng X., Kun L. (2012). The evaluation for the behavioral risk of principal participants in construction project based on BP neural network. "Advances in Information Sciences and Service Sciences", 4(14), ss. 97-107. http://doi.org/10.4156/AISS.vol4.issue14.12.
- 22. Wang X., Li Z. (2016). Integrated Platform for Smart Traffic Big Data. "Proceedings of the 6th IEEE International Conference on Logistics, Informatics and Services Sciences (LISS’2016)" (ss. 345-350). http://doi.org/10.1109/LISS.2016.7854592.
- 23. Yang T., Chen G., Sun X. (2015). A big-data-based Urban flood defense decision support system. "International Journal of Smart Home", 9(12), ss. 81–90. http://doi.org/10.14257/ijsh.2015.9.12.09.
- 24. Yuan N. (2015). Mining Social and Urban Big Data. "WWW'15 Companion Proceedings of the 24th International Conference on World Wide Web". http://doi.org/10.1145/2740908.2745843.
- 25. Yuan Y., He L., Li W., Yan L., Deris M. M. (2016). Real-time calculation of road traffic saturation based on big data storage and computing. "Proceedings - 14th International Symposium on Distributed Computing and Applications for Business, Engineering and Science, DCABES 2015" (ss. 204-207). http://doi.org/10.1109/DCABES.2015.58.
- 26. Zhang Y., Luo H., He Y. (2015). A System for Tender Price Evaluation of Construction Project Based on Big Data. "Procedia Engineering" (Vol. 123, ss. 606-614). http://doi.org/10.1016/j.proeng.2015.10.114.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-580a21dc-a65c-4b3c-b637-da5037e755c4