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Right-of-way (R/W) acquisition, one of the most basic of preconstruction activities on a highway project, is an important subject by many state DOTs because the failure to make timely acquisitions of R/W causes significant delays and conflicts. Various management practices and strategies have been designed and implemented to deliver R/W acquisition on schedule but completing this preconstruction process is still not an easy activity for many state DOTs. The Texas DOT (TXDOT) is no exception. The research reported in this paper has identified all drivers that can be considered influential in the determination of durations for R/W acquisition; it was undertaken to improve the R/W duration-prediction capabilities of practitioners involved in regional districts of TXDOT. In the research process, each driver's importance level was evaluated by such practitioners. The practitioners, all of whom had different levels of accuracy at estimating durations, showed significantly different perceptions of the identified duration-drivers. Based on this observation, the research reported in this paper suggests that all drivers which show such significantly different levels of importance should be determined when predicting durations for R/W acquisition in a project's early-planning phase. The research reported in this paper demonstrates that determining the influential drivers is critical to accurately estimate durations for R/W acquisition on highway projects. [ABSTRACT FROM AUTHOR]

While digital map has been applied to many advanced driver assistance system applications, one critical attribute - road/lane curvature—is not available in the existing digital maps. Curvature derivation using splines is a well-known method in the computer graphics modelling community; however, it was also found that the direct application of the spline method is often not appropriate for real-time safety applications due to the resultant discontinuities in the curvature estimates and the lack of robustness against map data errors [CAMP, Enhanced digital mapping project final report, United States Department of Transportation, Washington, DC, 2004]. This paper presents a method to reconstruct road curvature attributes in real time using digital map data based on the proposed circle centre search and circle selection algorithms. Simulation and experimental results demonstrated that the proposed method can deliver curvature estimates that meet the desired accuracy. The method was also applied to the curve over-speed warning system in an on-going research project: Vehicle Infrastructure Integration—California. Some preliminary experimental results are also presented in this paper. [ABSTRACT FROM AUTHOR]