Prepared in cooperation with the Indiana Department of Transportation and Federal Highway Administration. AbstractA commonality among state Departments of Transportation is the inability to complete projects on time and within budget. This project assessed the extent of the problem of cost overruns, time delays, and change orders associated with Indiana Department of Transportation (INDOT) construction projects, identified the reasons for such problems, and finally developed a set of recommendations aimed at their future reduction. For comparison purposes, data from other states were collected and studied using a questionnaire instrument. The analysis of the cost overrun, time delay and change order data was done using an array of statistical methods. The literature review and agency survey showed that time delays, cost overruns and change orders are generally due to factors such as design, unexpected site conditions, increases in project scope, weather conditions, and other project changes. The results of the agency survey showed that with regard to the problem of cost overruns, INDOT has an average rank compared to other states. Between 1996 and 2001, the overall rate for cost overrun amounts for INDOT projects was determined as 4.5%, and it was found that 55% of all INDOT contracts experienced cost overruns. It was determined that the average cost overrun amount and rate, as well as the contributory cost overrun factors differ by project type. The average cost overrun rates were as follows: bridge projects --8.1%, road construction --5.6%, road resurfacing --2.6%, traffic projects --5.6%, maintenance projects --7.5%. With regard to time delays, it was found that 12% of all INDOT contracts experience time delays, and the average delay per contract was 115 days. With regard to change orders, the study found that the dominant category of reasons for change orders is "errors and omissions", a finding which is suggestive of possible shortcomings in current design practices The statistical analyses in the present study showed that the major factors of cost overruns, time delays, and change orders in Indiana are contract bid amount, difference between the winning bid and second bid, difference between the winning bid and the engineer's estimate, project type and location by district. Besides helping to identify or confirm influential factors of cost overruns, time delay and change orders, the developed regression models may be used to estimate the extent of future cost overruns, time delay and change orders of any future project given its project characteristics and any available contract details. Such models can therefore be useful in long-term budgeting and needs assessment studies. Finally, the present study made recommendations for improving the management of projects and the administration of contracts in order to reduce cost overruns, time delays and change orders.

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Prepared in cooperation with the Indiana Department of Transportation and Federal Highway Administration. AbstractWith ever increasing traffic loadings, highway pavement maintenance needs continue to outpace the availability of resources, and transportation agencies seek cost-effective maintenance practices. This study investigated the effectiveness of maintenance treatments in the short-term and the cost effectiveness of maintenance strategies over entire pavement life. The study also analyzed the relationships and trade-offs between maintenance and capital investments such as pavement rehabilitation, and the trade-offs between preventive and corrective maintenance. These analyses were carried out through a work sequence that included analyses of historical trends, literature review, and a questionnaire survey. The study found that there are significant benefits associated with maintenance treatments, and that such short-term impacts generally involve an increase in pavement condition or a decrease in the rate of deterioration. For most treatments, a greater benefit is generally obtained for a larger effort expended on the maintenance treatment, at a given level of pavement condition, up to a point. The study also found that if chosen appropriately, maintenance strategies could be cost-effective in the long run. The most costeffective strategy was determined for each pavement family. Finally, the study determined that trade-off relationships exist between intervals of capital investments on one hand, and maintenance, traffic loading, and weather on the other hand: up to a point, increasing maintenance leads to increased rehabilitation interval, while increasing traffic loads and weather severity leads to reduction in rehabilitation interval, albeit at different rates for each pavement family. Marginal effects models were used to determine the effect of unit changes in maintenance levels, traffic loading, and weather on changes in rehabilitation interval. This information is useful not only for pavement management, but also for policy analyses involving truck weights, and pavement repair needs assessment to reflect changing traffic and weather conditions in the long-term. The data for the study was supplied by the Indiana Department of Transportation. Key WordsPavement maintenance, preventive maintenance, corrective maintenance, maintenance strategies, rehabilitation, marginal effects, short-term effectiveness, long-term effectiveness, cost-effectiveness, life-cycle cost analysis, capital expenditure. Distribution StatementNo restrictions. This document is available to the public through the National Technical Information Service, Springfield, VA 22161 TECHNICAL SummaryTechnology Transfer and Project Implementation Information TRB Subject Code:14-3 Financial Programming June 2003 Publication No.: FHWA/IN/JTRP-2002/27, SPR-2397 Final Report THE EFFECTIVENESS OF MAINTENANCE AND ITS IMPACT ON CAPITAL EXPENDITURES IntroductionWith ever increasing traffic loadings coupled with aging of highway infrastructure, high...

A traffic prediction model that incorporates relevant demographic variables for county roads was developed. Field traffic data were collected from 40 out of 92 counties in Indiana. The selection of a county was based on population, state highway mileage, per capita income, and the presence of interstate highways. Three to four automatic traffic counters were installed in each selected county. Most counters installed on the selected road sections were based on the standard 48-hour traffic counts. Then, the obtained average daily traffic was converted to annual average daily traffic by means of adjustment factors. Multiple regression analysis was conducted to develop the model. There were quantitative and qualitative predictor variables used in the model development. To validate the developed model, additional field traffic data were collected from eight randomly selected counties. The accuracy measures of the validation showed the high accuracy of the model. The statistical analyses also found that the independent variables employed in the model were statistically significant. The number of independent variables included in the model was kept to a minimum.