Lock-in and its influence on the project performance of large-scale transportation infrastructure projects: investigating the way in which lock-in can emerge and affect cost overruns

Ground conditions constitute a key risk factor that can ultimately determine the successful performance of construction contracts, with the literature reporting statistics of projects which have significantly exceeded their initial budget due to geotechnical uncertainties. The study explores the nature of geotechnical risk factors in transportation infrastructure projects, which potentially lead to cost overruns. The study provides a kaleidoscopic view of the various routes to managing risks due to the ground, at the preconstruction phases of highway projects, and how a lack thereof, can culminate to determine the trend of high-cost overruns in highway projects. The study findings reveal arguments and widely contested issues in geotechnical practice, which to various degrees, can have a significant financial impact on project completion cost in highway projects. The findings uncover various error traps and gaps in practice such as the lack of deterministic costing methods that better reflect heterogeneous ground conditions; insufficiency of preliminary geotechnical exploration; poor geotechnical risk containment in contracts as well as non-deployment of multi-dimensional geotechnically bespoke contractor selection algorithm. The study submits that these gaps in practice constitute the various trajectories through which geotechnical risk can trigger inefficiency and wastage of financial resources, leading to cost overruns in transportation infrastructure projects.

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Cost Overruns of Transportation Infrastructure Projects

International Encyclopedia of Transportation ◽

10.1016/b978-0-08-102671-7.10089-2 ◽

2021 ◽

pp. 483-489

Author(s):

James Odeck ◽

Morten Welde

Keyword(s):

Transportation Infrastructure ◽

Infrastructure Projects ◽

Cost Overruns

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A sustainable approach for selecting and timing the urban transportation infrastructure projects in large-scale networks: A case study of Isfahan, Iran

Sustainable Cities and Society ◽

10.1016/j.scs.2019.101981 ◽

2020 ◽

Vol 53 ◽

pp. 101981 ◽

Cited By ~ 2

Author(s):

Razieh Khayamim ◽

Seyyed-Nader Shetab-Boushehrib ◽

Seyyed-Mohammadreza Hosseininasab ◽

Hadi Karimi

Keyword(s):

Large Scale ◽

Urban Transportation ◽

Transportation Infrastructure ◽

Infrastructure Projects ◽

Large Scale Networks

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Accuracy of Traffic Forecasts and Cost Estimates on Large Transportation Projects

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198196151800112 ◽

1996 ◽

Vol 1518 (1) ◽

pp. 65-69 ◽

Cited By ~ 11

Author(s):

Mette K. Skamris ◽

Bent Flyvbjerg

Keyword(s):

Transportation Infrastructure ◽

Infrastructure Projects ◽

Construction Costs ◽

Cost Estimates ◽

Cost Overruns ◽

Transportation Projects ◽

Before And After

Little research has been carried out on before-and-after studies of traffic and construction costs in large transportation infrastructure projects. The few studies that do exist show that forecasts of construction costs tend to be underestimated and those of traffic, overestimated. An examination of construction costs and traffic in seven large Danish bridge and tunnel projects indicates that, on average, construction costs were underestimated and traffic was overestimated in the initial phases of planning. This pattern is also found for similar projects in other countries. When the results of all the projects are pooled, it is seen that cost overruns of 50 to 100 percent are common and that overruns above 100 percent are not uncommon. Traffic forecasts that are off by 20 to 60 percent when compared with actual development are frequent in large transportation infrastructure projects. The result of this overoptimism in the initial planning phases is that decisions are based on misleading forecasts that may lead to a misallocation of funds and underperforming projects.

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Flexible Spectrum Management: Approaches for India

Telecom Business Review ◽

10.21863/tbr/2015.8.1.003 ◽

2015 ◽

Vol 8 (1) ◽

Author(s):

Arturo Basaure ◽

Heikki Kokkinen ◽

Heikki Hämmäinen ◽

V. Sridhar

Keyword(s):

Large Scale ◽

Mobile Services ◽

Radio Spectrum ◽

Spectrum Management ◽

Management Approach ◽

Unlicensed Spectrum ◽

Spectrum Band ◽

Management Policies ◽

Management Approaches ◽

Lock In

Radio spectrum for commercial mobile services continues to be scarce. Countries around the world have recognized the importance of efficient utilization of this scarce resource and have initiated regulatory and policy steps towards flexible approaches to spectrum management, including sharing of licensed spectrum, and releasing unlicensed spectrum for mobile services. Technologies for shared access and the associated standardization activities have also progressed towards possible large scale deployments. In this paper, we analyze the evolution of spectrum management policies using a causal model and indicate how the markets can lock in to either centralized or flexible approach. We also cite a use case of a flexible spectrum management approach using spectrum band fill option and indicate its suitability to the Indian context.

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Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects

Sustainability ◽

10.3390/su13116376 ◽

2021 ◽

Vol 13 (11) ◽

pp. 6376

Author(s):

Junseo Bae ◽

Sang-Guk Yum ◽

Ji-Myong Kim

Keyword(s):

Machine Learning ◽

Financial Management ◽

Construction Projects ◽

Transportation Infrastructure ◽

Classification Model ◽

Insurance Companies ◽

Economic Damage ◽

Infrastructure Projects ◽

Machine Learning Classification ◽

Financial Losses

Given the highly visible nature, transportation infrastructure construction projects are often exposed to numerous unexpected events, compared to other types of construction projects. Despite the importance of predicting financial losses caused by risk, it is still difficult to determine which risk factors are generally critical and when these risks tend to occur, without benchmarkable references. Most of existing methods are prediction-focused, project type-specific, while ignoring the timing aspect of risk. This study filled these knowledge gaps by developing a neural network-driven machine-learning classification model that can categorize causes of financial losses depending on insurance claim payout proportions and risk occurrence timing, drawing on 625 transportation infrastructure construction projects including bridges, roads, and tunnels. The developed network model showed acceptable classification accuracy of 74.1%, 69.4%, and 71.8% in training, cross-validation, and test sets, respectively. This study is the first of its kind by providing benchmarkable classification references of economic damage trends in transportation infrastructure projects. The proposed holistic approach will help construction practitioners consider the uncertainty of project management and the potential impact of natural hazards proactively, with the risk occurrence timing trends. This study will also assist insurance companies with developing sustainable financial management plans for transportation infrastructure projects.

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