Peer state assessment of TxDOT maintenance program and practices : workshop and road rally findings

In an effort to evaluate and improve their practices to ensure the future excellence of the Texas highway system, the Texas Department of Transportation (TxDOT) sought a forum in which experts from other state departments of transportation could share their expertise. Thus, the Peer State Review of TxDOT Maintenance Practices project was organized and conducted for TxDOT by the Center for Transportation Research (CTR) at The University of Texas at Austin. The goal of the project was to conduct a workshop at CTR and in the Austin District that would educate the visiting peers on TxDOT’s maintenance practices and invite their feedback. CTR and TxDOT arranged the participation of the following directors of maintenance: Steve Takigawa, CA; Roy Rissky, KS; Eric Pitts, GA; Jim Carney, MO; Jennifer Brandenburg, NC; and David Bierschbach, WA. One of the means used to capture the peer reviewers’ opinions was a carefully designed booklet of 15 questions. The peers provided TxDOT with written responses to these questions, and the oral comments made during the workshop were also captured. This information was then compiled and summarized in the following report. An examination of the peers’ comments suggests that TxDOT should use a more holistic, statewide approach to funding and planning rather than funding and planning for each district separately. Additionally, the peers stressed the importance of allocating funds based on the actual conditions of the roadways instead of on inventory. The visiting directors of maintenance also recommended continuing and proliferating programs that enhance communication, such as peer review workshops.

Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks

Rule extraction from neural network algorithms have been investigated for two decades and there have been significant applications. Despite this level of success, rule extraction from neural network methods are generally not part of data mining tools, and a significant commercial breakthrough may still be some time away. This paper briefly reviews the state-of-the-art and points to some of the obstacles, namely a lack of evaluation techniques in experiments and larger benchmark data sets. A significant new development is the view that rule extraction from neural networks is an interactive process which actively involves the user. This leads to the application of assessment and evaluation techniques from information retrieval which may lead to a range of new methods.