| Machine learning in construction: From shallow to deep learning Y Xu, Y Zhou, P Sekula, L Ding Developments in the built environment 6, 100045, 2021 | 254 | 2021 |
| Combining association rules mining with complex networks to monitor coupled risks Y Zhou, C Li, L Ding, P Sekula, PED Love, C Zhou Reliability Engineering & System Safety 186, 194-208, 2019 | 79 | 2019 |
| Estimating historical hourly traffic volumes via machine learning and vehicle probe data: A Maryland case study P Sekuła, N Marković, Z Vander Laan, KF Sadabadi Transportation Research Part C: Emerging Technologies 97, 147-158, 2018 | 76 | 2018 |
| Applications of trajectory data from the perspective of a road transportation agency: Literature review and maryland case study N Marković, P Sekuła, Z Vander Laan, G Andrienko, N Andrienko IEEE Transactions on Intelligent Transportation Systems 20 (5), 1858-1869, 2018 | 54* | 2018 |
| Design and automated assembly of Planetary LEGO Brick for lunar in-situ construction C Zhou, B Tang, L Ding, P Sekula, Y Zhou, Z Zhang Automation in Construction 118, 103282, 2020 | 33 | 2020 |
| Estimating hourly traffic volumes using artificial neural network with additional inputs from automatic traffic recorders S Zahedian, P Sekuła, A Nohekhan, Z Vander Laan Transportation Research Record 2674 (3), 272-282, 2020 | 23 | 2020 |
| Estimating highway volumes using vehicle probe data-proof of concept Y Hou, SE Young, K Sadabadi, PB SekuBa, D Markow National Renewable Energy Lab.(NREL), Golden, CO (United States), 2018 | 11 | 2018 |
| Machine learning in construction: From shallow to deep learning. Developments in the Built Environment, 6, 100045 Y Xu, Y Zhou, P Sekula, L Ding Go to original source, 2021 | 9 | 2021 |
| Machine learning in construction: from shallow to deep learning. Dev Built Environ 6: 100045 Y Xu, Y Zhou, P Sekula, L Ding | 5 | 2021 |
| Analyzing impact of I-85 bridge collapse on regional travel in Atlanta M Hamedi, S Eshragh, M Franz, PM Sekula Transportation Research Board 97th Annual Meeting. Washington, DC, 2018 | 4 | 2018 |
| Predicting work zone collision probabilities via clustering: application in optimal deployment of highway response teams P Sekuła, Z Vander Laan, K Farokhi Sadabadi, MJ Skibniewski Journal of Advanced Transportation 2018 (1), 3179207, 2018 | 4 | 2018 |
| Interesariusze projektów publicznych–sukces projektu publicznego w ujęciu specjalistów od zarz±dzania projektami H Brandenburg, K Ficek-Wojciuch, M Magdoń, P Sekuła Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 2016 | 4 | 2016 |
| Developments in the built environment Y Xu, Y Zhou, P Sekula, L Ding Machine Learning in Construction: From Shallow to Deep Learning 6, 100045, 2021 | 2 | 2021 |
| Projekt badawczy pt" Sukces projektu publicznego i jego uwarunkowania"-wyniki studiów literaturowych i program realizacji projektu H Brandenburg, K Ficek-Wojciuch, M Magdoń, P Sekuła Prace Naukowe/Uniwersytet Ekonomiczny w Katowicach, 52-60, 2016 | 2 | 2016 |
| Budżet zadaniowy jako skuteczne narzędzie zarz±dzania w samorz±dzie P Sekuła Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 229-238, 2015 | 2 | 2015 |
| Szacowanie natężenia ruchu drogowego z wykorzystaniem sieci neuronowych P Sekuła Transport Miejski i Regionalny, 2022 | 1 | 2022 |
| Transferability of a Machine Learning‐Based Model of Hourly Traffic Volume Estimation—Florida and New Hampshire Case Study P Sekuła, ZV Laan, KF Sadabadi, K Kania, S Zahedian Journal of Advanced Transportation 2021 (1), 9944918, 2021 | 1 | 2021 |
| Application of vehicle probe data in estimating traffic volumes: a Maryland case study P Sekula, N Marković, Z Vander Laan, K Farokhi Sadabadi Transportation Research Board 97th Annual MeetingTransportation Research Board, 2018 | 1 | 2018 |
| Zarz±dzanie programami według Program Management Institute P Sekuła Regional Barometer. Analyses & Prognoses 12 (2), 147-155, 2014 | 1 | 2014 |
| Kolejowe przewozy regionalne–wyzwania i problemy P Sekuła Biblioteka Regionalisty, 201-210, 2013 | 1 | 2013 |
