Institute for Transportation

About the research

As of July 2016, Montana was the only state to maintain a differential speed limit on two-lane two-way rural highways, utilizing a daytime statutory speed limit of 70 mph for cars and light trucks and 60 mph for trucks exceeding a one-ton payload capacity. Although differential speed limits are common on freeways, the use of differential limits on two-lane roadways presents unique safety and operational issues due to passing limitations and subsequent queuing, and prior research on such issues is scarce. Consequently, research was performed to evaluate the safety and operational impacts associated with the aforementioned differential speed limit on rural two-lane highways in Montana, particularly when compared to a uniform 65 mph speed limit. A series of field studies were performed on two-lane rural highways in Montana, which predominately possessed the 70 mph/60 mph differential speed limit, and in neighboring states where uniform 65 mph speed limits prevailed. The locations with 65 mph speed limits generally displayed less variability in travel speeds, shorter platoon lengths, less high-risk passing behavior, and fewer crashes. Surveys were performed to determine the speed limit policy preferences among motorists and members of the trucking industry in Montana. Although motorist support for the uniform 65 mph speed limit was mixed, the trucking industry strongly supported the uniform 65 mph limit over the current differential limit. Overall, the collective findings support transitioning to a uniform 65 mph speed limit on two-lane rural highways in Montana. Selective implementation of this new speed limit is advised initially, and candidate highways should possess relatively high traffic volumes, relatively high truck percentages, and limited passing opportunities.

About the research

A highway construction cost index (HCCI) is an indicator of the purchasing power of a highway agency. Thus, it must reflect the actual construction market conditions. However, the current method used by MDT is not robust enough to meet this primary goal due to (1) a significantly insufficient sample size of bid items used in HCCI calculation and (2) inability to address the need to track cost trend of construction submarket segments such as, but not limited to, various project types, sizes, and locations. This study develops an advanced methodology to overcome these apparent limitations using two new concepts: (1) dynamic item basket; and (2) multidimensional HCCIs. The dynamic item basket process identifies and utilizes an optimum amount of bid-item data to calculate HCCIs in order to minimize the potential error due to a small sample size, which leads to a better reflection of the current market conditions. Multidimensional HCCIs dissect the state highway construction market into distinctively smaller sectors of interest and thus allow MDT to understand the market conditions with much higher granularity. A methodology is developed to integrate these two concepts and a standalone ‘MDT HCCI Calculation and Bid Analysis System’ is developed to automate the HCCI calculation process. The results show an eightfold increase in terms of the number of bid items used in calculating HCCIs and at least a 20% increase in terms of the total cost of bid items used. In addition, the multidimensional HCCIs reveal different cost-change patterns across different highway sectors. For example, the bridge construction market historically shows a very different trend compared with the overall highway construction market. The new methodology is expected to aid MDT in making more-reliable decisions in preparing business plans and budgets with more accurate and detailed information about the construction market conditions. Further, the system is expected to provide insights on the cost trends of a specific item; aid in identifying project types, locations, and sizes with higher construction cost growth; and aid in identifying hidden relationships such as cost-quality relationship.

More information is available at the Montana DOT project page.

About the research

Accurate and practical production rate estimates are crucial for an accurate forecast of contract completion time. As costs of highway projects increase with time, the importance of estimating highway construction contract time has increased significantly, thereby emphasizing the need for effective production rates due to the interrelatedness between the two. By reviewing the literature, various aspects of production rate estimation were identified including factors that influence production rates, production rate adjustment factors, and statistical methods, and current practices of the Montana Department of Transportation (MDT). The purpose of this research was to develop historical data-driven estimates of production rates using daily work report (DWR) data in order to enhance current contract time determination practices.

The research team analyzed the MDT’s DWR data along with bid data and GIS data to estimate realistic production rates. Descriptive analysis, regression analysis, and Monte Carlo simulation were deployed to offer insights into historical projects’ characteristics and production rates of 31 controlling activities of MDT. The major findings of the descriptive analysis were statistical measures (i.e., mean, first quartile, median, and third quartile) of 31 controlling activities, which provide more practical, detailed, and updated estimates in comparison with the current published values. In addition, variations of production rates in terms of different seasons of work, districts, area types (urban/rural), and budget types were explored. The study also developed regression equations to estimate production rates of 27 out of 31 controlling activities. For each activity, factors that had a significant effect on production rate were included in the regression model as predictor variables. Besides, a production rate-based method was proposed to evaluate contractor’s performances, and a Microsoft Excel based Production Rate Estimation Tool (PRET) was developed to assist MDT practitioners.

About the research

This project contains the information and background on top-down cost estimating using artificial neural networks (ANN) to enhance the accuracy of MDT early estimates of construction costs. Upon conducting an extensive review of MDT’s budgeting and cost estimating efforts, and following a survey of agency experts on the identification of the most salient project attributes with the dual-objectives of low effort and high accuracy, a rational method for top-down variable selection is proposed.

Selected variables were further tested in their explanatory power of construction costs through the application of two cost estimating methodologies—multiple regression and artificial neural network methodologies. Both methods are shown to provide sizeable improvements over the agency’s current levels of prediction accuracy for its construction costs. Potential accuracy gains are also demonstrated to depend on project work types. The comparison of mean absolute percentage errors across different estimating methods confirms that the potential benefits from the proposed methodologies are expected to rise as the project level complexity and uncertainty increase. New construction and bridge replacement projects, for instance, are expected to gain the most in estimating accuracy since these two groups seem to exhibit considerably higher levels of deviation from the MDT’s preliminary cost estimates.

To facilitate MDT’s implementation of the suggested methodology described in this report, a cost estimation methodology was also presented in an Excel spreadsheet format. This achieves two goals. First, it provides an accessible tool to make top-down cost predictions for agency planners during the budgeting stage based on MDT’s historical project data. Second, it furnishes a process through which the proposed model can be improved as new project information becomes available. Ultimately, the insights gained from this study are expected to contribute to a better formulation of the agency’s early cost estimation and budgeting efforts.