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.

中文翻译：

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使用大数据分析预测 PPP 项目的完工风险

准确预测公私合作 (PPP) 项目的潜在延误可以为未来 PPP 项目的规划和降低完工风险提供有价值的信息。然而，现有的评估完工风险的技术仍然无法识别大样本项目中风险行为的隐藏模式，而这些模式与准确预测越来越相关。为了有效解决 PPP 项目中的这个问题，本研究提出了一种用于完成风险预测的大数据分析预测建模技术。利用 1992 年至 2015 年间在欧洲交付的 4294 个 PPP 项目样本的数据，使用线性回归、回归树、随机森林、支持向量机和深度神经网络设计和评估了一系列预测模型，用于完成风险预测。本研究的结果和发现表明，随机森林是一种预测 PPP 项目延迟的有效技术，平均测试预测误差低于其他传统回归技术。研究中还提出了与模型选择、训练和验证相关的研究问题。