Avoiding highway infrastructure construction cost overruns and reducing associated claims and disputes continues to be a challenge in many countries. Research is needed in identifying notable project planning and management deficiencies that are likely to cause cost overruns. The literature suggests numerous potential causes of cost overrun but the clustering of cause variables and relative importance of clusters has not been researched. The research reported here addresses this knowledge gap using predictive models developed with data contributed by several agencies in participating countries and suggests mitigation measures. Following a review of methods and data sources, a methodological framework is advanced that encompasses statistical methods well suited for providing a scientific basis for identifying important clusters of cost overrun variables. Fifty-three completed questionnaires contributed by knowledge experts and experienced managers from Canada, the United States, the Middle East, and Australia met the sample requirements of statistical methods. Starting from 53 variables, the principal component-supported factor analysis method identified clusters of cost overrun variables and their relative importance was inferred with developed logistic regression models. Deeper insights into the causes of cost overruns obtained from this research suggest mitigation measures (e.g., improved qualification and experience of personnel, enhanced planning and design practices, risk analysis of inputs to cost estimation process) that are within reach of managers. The results can enhance infrastructure planning and management practice including a reduction in claims and disputes.

This study presents a machine learning framework for predicting traffic accident occurrence and congestion intensity using artificial neural networks (ANNs) trained on real-world traffic data collected from a central urban corridor in Egypt. The research aims to enhance proactive traffic management by providing reliable, data-driven forecasts derived from temporal and environmental road features. Sixty-seven traffic observations were recorded over three months, capturing variations across vehicle flow, speed, weather, holidays, and road conditions. Two predictive models were developed: a binary accident detection classifier and a multi-class congestion level estimation classifier. Both models employed Bayesian optimization for hyperparameter tuning and were evaluated under three validation strategies—5-fold cross-validation, 10-fold cross-validation, and resubstitution—combined with different train/test splits. The results demonstrated that the model using 10-fold cross-validation and a 75/25 split achieved the highest accuracy in accident prediction (93.8% on test data), with minimal variance between validation and testing phases. In contrast, resubstitution validation yielded artificially high training accuracy (up to 100%) but lower generalization performance, confirming overfitting risks. Congestion prediction showed similarly strong classification trends, with the optimized model effectively distinguishing between congestion levels under dynamic traffic conditions. These findings validate the use of ANN-based prediction in real-world traffic scenarios and highlight the critical role of validation design in developing robust forecasting models. The proposed approach holds promise for integrating intelligent transportation systems, enabling anticipatory interventions, and enhancing road safety.

Assessing risk in life cycle cost and benefit estimates of highway investments is recommended by major organizations such as the World Bank and the U.S. Federal Highway Administration. This challenging task needs methodological support. Mutually exclusive investment alternatives can differ in terms of costs of construction, maintenance, rehabilitation, and end of life value. Due to many causal factors and the long life of highway infrastructure, these items cannot be estimated with certainty. To go beyond sources of uncertainty study, a method is needed to check economic feasibility of acquiring additional information for deeper insight. This paper reports research on the value of Bayesian pre-posterior information for refining life cycle analysis of uncertain costs and benefits for evaluating highway investment alternatives. Example applications demonstrate how the Bayesian pre-posterior analysis can be applied to check the feasibility of obtaining new information for enhancing life cycle analysis of highway investments. The value of Bayesian pre-posterior information is illustrated for reducing risk. Also, depending upon specifics of uncertain states, a change in the choice of the investment alternative for implementation can be investigated. The product of this research can potentially upgrade highway infrastructure planning and management practice.

Assessing risk in life-cycle cost and benefit estimates of highway investments is recommended by major organizations such as the World Bank and the U.S. Federal Highway Administration. This challenging task needs methodological support. Mutually exclusive investment alternatives can differ in terms of the costs of construction, maintenance, rehabilitation, and end-of-life value. Due to many causal factors and the long life of highway infrastructure, these items cannot be estimated with certainty. To go beyond the study of the sources of uncertainty, a method is needed to check the economic feasibility of acquiring additional information for deeper insight. This paper reports on research on the value of Bayesian pre-posterior information for refining the life-cycle cost analysis of uncertain costs and benefits for evaluating highway investment alternatives. Example applications demonstrate how the Bayesian pre-posterior analysis can be applied to check the feasibility of obtaining new information for enhancing the life-cycle cost analysis of highway investments. The value of Bayesian pre-posterior information is illustrated for reducing risk. Also, depending upon the specifics of uncertain states, a change in the choice of the investment alternative for implementation can be investigated. The product of this research can potentially upgrade highway infrastructure planning and management practices.

Avoiding highway infrastructure construction cost overruns and reducing associated claims and disputes continues to be a challenge in many countries. Research is needed in identifying notable project planning and management deficiencies that are likely to cause cost overruns. Literature sources suggest numerous potential causes of cost overrun but clustering of cause variables and relative importance of clusters has not been researched. Research reported here addresses this knowledge gap using predictive models developed with data contributed by several agencies in participating countries and suggests mitigation measures. Following a review of methods and data sources, a methodological framework is advanced that encompasses statistical methods well suited for providing a scientific basis for identifying important clusters of cost overrun variables. A questionnaire study enabled data acquisition from Canadian, United States, Middle East, and Australian sources. Starting from many variables, the principal component-supported factor analysis method identified clusters of cost overrun variables and their relative importance was inferred with developed logistic regression models. Deeper insights in causes of cost overruns obtained from this research suggest mitigation measures that are within reach of managers. The results can potentially enhance infrastructure planning and management practice including reduction of claims and disputes.