Selecting the Most Feasible Construction Phasing Plans for Urban Highway Rehabilitation Projects

Despite the abundance of research that has aimed to understand the effects of highway work zones, very little definitive information is available concerning the determination of work zone length (WZL). Quantitative studies that holistically model WZL are very rare. To fill this gap, this study identifies critical factors affecting WZL and develops decision support models that determine the optimal WZL in a balanced tradeoff between motorists’ inconvenience due to traffic disruption and their opportunity cost. A high-confidence dataset was created by conducting a series of scheduling and traffic simulations and analyses. The results revealed that traffic loading and work zone duration are critical factors, with traffic loading at approximately 41,000 vehicles-per-day being an important benchmarking point. Based on these findings, a decision support model was developed to determine the most feasible WZL. As the first of its kind, this study will help state transportation agencies devise sounder construction phasing plans by providing a point of reference when establishing WZL in a viable way to minimize traffic disruption during construction

Quantifying Causes, Schedule, and Cost Impacts of Change Orders: "Is Alternative Contracting Really Effective?"

The most professions who are engaged in the highway construction industry would commonly concur with an idea that the project cannot be delivered with no change. Regardless of considering different contract methods or what so ever, contract change orders (CCO) are yet inevitable due to unforeseen utility conflicts, unpredicted geology, and other unanticipated conditions. No matter of the project location and/or condition, the CCO negatively affects the project in aspects of project cost and schedule. The main purpose of this study is to carefully examine the influences of change orders in infrastructure development projects in the schedule and cost aspects. The aim of this study starts with collecting Florida Department of Transportation’s (FDOT) 9 years of solid data that contains abundant information of CCO in highway projects completed in the state of Florida. In addition to the data, it contains 2,990 infrastructure projects completed between 2002 and 2011, 43,000 change order types, 8 contract methods including conventional (D/B/B), Design-Build (D/B), Incentive/Disincentive (I/D), No Excuse Bonus, Lump Sum, etc., and 7 major types of projects. These detailed and vast data was utilized to evaluate each method's performances affecting projects on cost and schedule aspects by carrying quantitative analysis, such as graphs, box plots, etc. Lastly, the research hypothesis test, which utilized regression analyses, Q-Q plots, scatterplot matrixes, etc., was conducted to verify the data variation, normal distribution, equal variances, correlation, etc. The research results reveal that the innovative methods perform better than conventional in aspects of saving project cost and time. In addition to the innovative methods, D/B is the most effective method that saves both cost and time of projects. I/D compresses project duration but often causes project cost overrun. And Lump Sum significantly saves the project cost but causes project schedule overrun. This study will help interstate transportation agencies with a proper guideline to choose an ideal delivery or contracting method for a project. By providing the information of each method’s advantages and disadvantages, it is expected to significantly reduce the agencies’ time and expenses required to deliver projects

A Multi-Contextual Approach to Modeling the Impact of Critical Highway Work Zones in Large Urban Corridors

Accurate Construction Work Zone (CWZ) impact assessments of unprecedented travel inconvenience to the general public are required for all federally-funded highway infrastructure improvement projects. These assessments are critical, but they are also very difficult to perform. Most existing prediction approaches are project-specific, shortterm, and univariate, thus incapable of benchmarking the potential traffic impact of CWZs for highway construction projects. This study fills these gaps by creating a big-data-based decision-support framework and testing if it can reliably predict the potential impact of a CWZ under arbitrary lane closure scenarios. This study proposes a big-data-based decision-support analytical framework, “Multi-contextual learning for the Impact of Critical Urban highway work Zones” (MICUZ). MICUZ is unique as it models the impact of CWZ operations through a multi-contextual quantitative method utilizing sensored big transportation data. MICUZ was developed through a three-phase modeling process. First, robustness of the collected sensored data was examined through a Wheeler’s repeatability and reproducibility analysis, for the purpose of verifying the homogeneity of the variability of traffic flow data. The analysis results led to a notable conclusion that the proposed framework is feasible due to the relative simplicity and periodicity of highway traffic profiles. Second, a machine-learning algorithm using a Feedforward Neural Networks (FNN) technique was applied to model the multi-contextual aspects of iii long-term traffic flow predictions. The validation study showed that the proposed multi-contextual FNN yields an accurate prediction rate of traffic flow rates and truck percentages. Third, employing these predicted traffic parameters, a curve-fitting modeling technique was implemented to quantify the impact of what-if lane closures on the overall traffic flow. The robustness of the proposed curve-fitting models was then scientifically verified and validated by measuring forecast accuracy. The results of this study convey the fact that MICUZ would recognize how stereotypical regional traffic patterns react to existing CWZs and lane closure tactics, and quantify the probable but reliable travel time delays at CWZs in heavily trafficked urban cores. The proposed framework provides a rigorous theoretical basis for comparatively analyzing what-if construction scenarios, enabling engineers and planners to choose the most efficient transportation management plans much more quickly and accurately