Review and Analysis of the Kansas Department of Transportation Maintenance Quality Assurance Program
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ISBN 13 :
Total Pages : 29 pages
Book Rating : 4.:/5 (465 download)
Book Synopsis Review and Analysis of the Kansas Department of Transportation Maintenance Quality Assurance Program by :
Download or read book Review and Analysis of the Kansas Department of Transportation Maintenance Quality Assurance Program written by and published by . This book was released on 2009 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Kansas Department of Transportation (KDOT) has had a MQA program in place since 1999 to evaluate the effectiveness of maintenance activities. Like many states with MQA programs, KDOT samples a portion of their facility in the fall of each year. Specifically, KDOT samples thirty 0.1 mile inspection sites in each of the state's 112 maintenance subareas in October of each year. KDOT subareas were originally established so that each would have approximately equal roadway miles, allowing similar workloads for such maintenance activities as snow removal and sign maintenance. The purpose of this project was to conduct a review of the Kansas Department of Transportation's Maintenance Quality Assurance (MQA) Program and make recommendations to ensure there was adequate representation for each class of roadway in the state network. A representative sampling of each class would allow independent analysis of the various classes and could be used to answer questions such as: "How well are the Kansas Interstates maintained?" Maintenance managers can also use the results as a planning tool to identify areas where maintenance efforts were ineffective compared to expectations, providing information on how to prioritize maintenance needs for the next maintenance season. The case of the MQA data for the state of Kansas is a good example of why modeling is used to solve complex problems: it is impractical - if not impossible - to know all of the various maintenance ratings on each element in each of over 100,000 possible inspection sites. Developing a model using average values was not practical, as each rated element is supposed to be given either a 0 or a 1; an average value (say 0.85 or 0.7) would make no sense in this context. In the final result, the model developed included 108,247 possible inspection sites, which is slightly more than KDOT's number used in the past few years, as shown in Table 2.1. The total number of inspection sites that could have been selected varies slightly from year to year because sections of roadway that are under construction and also those sections that are maintained by cities (i.e., outside of KDOT maintenance activities) are removed from possible selection. There are several unanswered questions regarding ways that the KDOT MQA could be modified. One question raised by KDOT pertained to the examination of rare elements such as certain drainage structures only found in certain areas of the state. As the process used to populate the model assigned elements across the virtual network randomly, elements that were concentrated in certain areas of the state were not correctly represented. If the model population process was modified so that individual elements were allocated randomly based on smaller geographic areas (such as districts, areas, or subareas) it might be possible to further increase the validity of the results of individual elements. Finally, there have been questions raised regarding the ability of the MQA data collection to evaluate locations smaller than the state level. For example, could the MQA data be used to evaluate the maintenance efficacy of on district, area, or even subarea compared to others. Because the algorithm used to populate the virtual network did not account for these geographic boundaries this was a question that could not be adequately researched. However, if the algorithm were updated to fill in inspection sites with data representative to the geographic unit in question (district, area, subarea) then this distinction could be made. However, as with any statistical procedure, the results should be used with caution due to the reduction in sample size.
