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

doi:10.1109/TEM.2018.2876321

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

School of Energy, Construction and Environment

Research output: Contribution to journal › Article › peer-review

27 Citations (Scopus)

490 Downloads (Pure)

Abstract

Accurate prediction of potential delays in public private partnerships (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 behavior 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 predictive modeling 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.

Original language	English
Article number	8542956
Pages (from-to)	430-453
Number of pages	24
Journal	IEEE Transactions on Engineering Management
Volume	67
Issue number	2
Early online date	21 Nov 2018
DOIs	https://doi.org/10.1109/TEM.2018.2876321
Publication status	Published - May 2020

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;

Access to Document

10.1109/TEM.2018.2876321

Post-printAccepted author manuscript, 2.73 MB

Cite this

@article{3b3b25cf442c4b029e254de520d56221,

title = "Predicting Completion Risk in PPP Projects using Big Data Analytics",

abstract = "Accurate prediction of potential delays in public private partnerships (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 behavior 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 predictive modeling 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 = "{\textcopyright} 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 {\textcopyright} 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 = "2020",

month = may,

doi = "10.1109/TEM.2018.2876321",

language = "English",

volume = "67",

pages = "430--453",

journal = "IEEE Transactions on Engineering Management",

issn = "0018-9391",

publisher = "Institute of Electrical and Electronics Engineers",

number = "2",

}

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 - 2020/5

Y1 - 2020/5

N2 - Accurate prediction of potential delays in public private partnerships (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 behavior 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 predictive modeling 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 public private partnerships (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 behavior 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 predictive modeling 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;

UR - http://www.scopus.com/inward/record.url?scp=85057401775&partnerID=8YFLogxK

U2 - 10.1109/TEM.2018.2876321

DO - 10.1109/TEM.2018.2876321

M3 - Article

SN - 0018-9391

VL - 67

SP - 430

EP - 453

JO - IEEE Transactions on Engineering Management

JF - IEEE Transactions on Engineering Management

IS - 2

M1 - 8542956

ER -

Predicting Completion Risk in PPP Projects using Big Data Analytics

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this