Predicting Completion Risk in PPP Projects using Big Data Analytics

Hakeem O Owolabi, Muhammad Bilal, Lukumon O. Oyedele, Hafiz Alaka, Saheed O. Ajayi, Olugbenga O. Akinade

Research output: Contribution to journalArticle

Abstract

Accurate prediction of potential delays in PPP projects could provide valuable information relevant for planning, and mitigating completion risk in future PPP projects. However, existing techniques for evaluating completion risk remain incapable of identifying hidden patterns in risk behaviour within large samples of projects, which are increasingly relevant for accurate prediction. To effectively tackle this problem in PPP projects, this study proposes a Big Data Analytics (BDA) predictive modelling technique for completion risk prediction. With data from 4294 PPP project samples delivered across Europe between 1992 and 2015, a series of predictive models have been devised and evaluated using linear regression, regression trees, random forest, support vector machine and deep neural network for completion risk prediction. Results and findings from this study reveal that random forest is an effective technique for predicting delays in PPP projects, with lower average test predicting error than other legacy regression techniques. Research issues relating to model selection, training and validation are also presented in the study.
LanguageEnglish
Pages(In-press)
JournalIEEE Transactions on Engineering Management
Volume(In-press)
Early online date21 Nov 2018
DOIs
Publication statusE-pub ahead of print - 21 Nov 2018

Fingerprint

Linear regression
Support vector machines
Big data
Prediction
Planning
Predictive modeling
Model selection
Research issues
Regression tree
Support vector machine
Neural networks
Deep neural networks
Predictive analytics

Bibliographical note

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Keywords

  • Benchmark
  • Completion risk (CR)
  • Forecasting
  • Predictive modeling
  • Public private partnerships (PPP)
  • Big Data;

Cite this

Owolabi, H. O., Bilal, M., Oyedele, L. O., Alaka, H., Ajayi, S. O., & Akinade, O. O. (2018). Predicting Completion Risk in PPP Projects using Big Data Analytics. IEEE Transactions on Engineering Management, (In-press), (In-press). https://doi.org/10.1109/TEM.2018.2876321

Predicting Completion Risk in PPP Projects using Big Data Analytics. / Owolabi, Hakeem O; Bilal, Muhammad; Oyedele, Lukumon O.; Alaka, Hafiz; Ajayi, Saheed O.; Akinade, Olugbenga O.

In: IEEE Transactions on Engineering Management, Vol. (In-press), 21.11.2018, p. (In-press).

Research output: Contribution to journalArticle

Owolabi, Hakeem O ; Bilal, Muhammad ; Oyedele, Lukumon O. ; Alaka, Hafiz ; Ajayi, Saheed O. ; Akinade, Olugbenga O. / Predicting Completion Risk in PPP Projects using Big Data Analytics. In: IEEE Transactions on Engineering Management. 2018 ; Vol. (In-press). pp. (In-press).
@article{3b3b25cf442c4b029e254de520d56221,
title = "Predicting Completion Risk in PPP Projects using Big Data Analytics",
abstract = "Accurate prediction of potential delays in PPP projects could provide valuable information relevant for planning, and mitigating completion risk in future PPP projects. However, existing techniques for evaluating completion risk remain incapable of identifying hidden patterns in risk behaviour within large samples of projects, which are increasingly relevant for accurate prediction. To effectively tackle this problem in PPP projects, this study proposes a Big Data Analytics (BDA) predictive modelling technique for completion risk prediction. With data from 4294 PPP project samples delivered across Europe between 1992 and 2015, a series of predictive models have been devised and evaluated using linear regression, regression trees, random forest, support vector machine and deep neural network for completion risk prediction. Results and findings from this study reveal that random forest is an effective technique for predicting delays in PPP projects, with lower average test predicting error than other legacy regression techniques. Research issues relating to model selection, training and validation are also presented in the study.",
keywords = "Benchmark, Completion risk (CR), Forecasting, Predictive modeling, Public private partnerships (PPP), Big Data;",
author = "Owolabi, {Hakeem O} and Muhammad Bilal and Oyedele, {Lukumon O.} and Hafiz Alaka and Ajayi, {Saheed O.} and Akinade, {Olugbenga O.}",
note = "{\circledC} 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Copyright {\circledC} and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.",
year = "2018",
month = "11",
day = "21",
doi = "10.1109/TEM.2018.2876321",
language = "English",
volume = "(In-press)",
pages = "(In--press)",
journal = "IEEE Transactions on Engineering Management",
issn = "0018-9391",
publisher = "Institute of Electrical and Electronics Engineers",

}

TY - JOUR

T1 - Predicting Completion Risk in PPP Projects using Big Data Analytics

AU - Owolabi, Hakeem O

AU - Bilal, Muhammad

AU - Oyedele, Lukumon O.

AU - Alaka, Hafiz

AU - Ajayi, Saheed O.

AU - Akinade, Olugbenga O.

N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

PY - 2018/11/21

Y1 - 2018/11/21

N2 - Accurate prediction of potential delays in PPP projects could provide valuable information relevant for planning, and mitigating completion risk in future PPP projects. However, existing techniques for evaluating completion risk remain incapable of identifying hidden patterns in risk behaviour within large samples of projects, which are increasingly relevant for accurate prediction. To effectively tackle this problem in PPP projects, this study proposes a Big Data Analytics (BDA) predictive modelling technique for completion risk prediction. With data from 4294 PPP project samples delivered across Europe between 1992 and 2015, a series of predictive models have been devised and evaluated using linear regression, regression trees, random forest, support vector machine and deep neural network for completion risk prediction. Results and findings from this study reveal that random forest is an effective technique for predicting delays in PPP projects, with lower average test predicting error than other legacy regression techniques. Research issues relating to model selection, training and validation are also presented in the study.

AB - Accurate prediction of potential delays in PPP projects could provide valuable information relevant for planning, and mitigating completion risk in future PPP projects. However, existing techniques for evaluating completion risk remain incapable of identifying hidden patterns in risk behaviour within large samples of projects, which are increasingly relevant for accurate prediction. To effectively tackle this problem in PPP projects, this study proposes a Big Data Analytics (BDA) predictive modelling technique for completion risk prediction. With data from 4294 PPP project samples delivered across Europe between 1992 and 2015, a series of predictive models have been devised and evaluated using linear regression, regression trees, random forest, support vector machine and deep neural network for completion risk prediction. Results and findings from this study reveal that random forest is an effective technique for predicting delays in PPP projects, with lower average test predicting error than other legacy regression techniques. Research issues relating to model selection, training and validation are also presented in the study.

KW - Benchmark

KW - Completion risk (CR)

KW - Forecasting

KW - Predictive modeling

KW - Public private partnerships (PPP)

KW - Big Data;

U2 - 10.1109/TEM.2018.2876321

DO - 10.1109/TEM.2018.2876321

M3 - Article

VL - (In-press)

SP - (In-press)

JO - IEEE Transactions on Engineering Management

T2 - IEEE Transactions on Engineering Management

JF - IEEE Transactions on Engineering Management

SN - 0018-9391

ER -