Li P.,Kentucky Transportation Center |
Li P.,Nanjing Southeast University |
Furth P.,Northeastern University |
Zhu N.,AECOM Technology Corporation |
Guo X.,Nanjing Southeast University
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC | Year: 2011
More and more towns in the states have installed central traffic managing software (ATMS). ATMS can not only enable traffic engineers to remotely access controllers but also enable them to collect more data than ever. How to better utilize these data to improve the performance of traffic signals has been a topic receiving wide interest in the signal community. In actuated coordination, the main-line greens may start earlier than what are programmed because uncoordinated phases could gap out and return unused green back to the main-line. In this paper, the authors considered the main-line greens random variants ranging from the programmed maximum greens to the whole cycle length. The authors first defined this new concept as Most-likely Optimal Offsets, then used cycle-by-cycle green usage reports and a Monte Carlo simulation model to determine the most-likely optimal offsets. The cycle-by-cycle green usage report is a typical function of major ATMS systems to provide the distributions of random main-line greens. It serves as the basis to infer the optimal offset distributions and thus allow for identifying the most likely optimal offsets. The case study revealed that the new offsets could significantly reduce the travel times on arterials with 95% confidence level compared to SYNCHRO 7 when the early-return-to-green frequently occurs. The implementation in the field also supported the speculations from simulation. © 2011 IEEE.
Stamatiadis N.,University of Kentucky |
Kirk A.,Kentucky Transportation Center |
Hartman D.,Kentucky Transportation Center |
Pigman J.,Kentucky Transportation Center
Journal of Transportation Engineering | Year: 2010
Developing a procedure that yields up to the maximum margin of return for the investment requires an approach that takes into account specific safety issues and the commensurate design elements for each roadway. Kentucky's highway agency has embarked upon an initiative tagged "practical solutions" which sets its goal toward reducing costs throughout the project development process extended into operations and maintenance of all highway facilities. This operationally defines a design procedure within the context of practical solutions and sets up the guiding principles of the approach. The most critical component of practical solutions in planning and design is the definition and clarification of the initial project concept (its specific goals and objectives) since it is the corner stone of the project and used to significantly contain the cost and impact of a project. Traditional design tends to seek as high a design speed as reasonable with the aim to reduce travel time. Practical design requires that levels of service should not be taken as absolutes but rather be viewed as starting points. Each project should be viewed as an investment and as such requires an understanding of the marginal returns to be realized. As in any financial situation, there is always a point of diminishing returns, i.e., greater investment will have no or little effect on increasing the return. The system-based evaluation of practical design in this study examined the safety and operational performance of various cross-section alternatives, based on highway capacity and highway safety manual procedures. The various alternative cross sections ranged from an improved two-lane section representing a practical solution approach to a four-lane-divided highway. A case study of a Kentucky intersection improvement project is presented that exemplifies a practical solution in practice. © 2010 ASCE.
Ripy J.,Kentucky Transportation Center |
Grossardt T.,Kentucky Transportation Center |
Shouse M.,Kentucky Transportation Center |
Mink P.,University of Kentucky |
And 2 more authors.
Transportation Research Record | Year: 2014
This paper reports on the deployment of a predictive model that combines spatial analysis and fuzzy logic modeling to translate expert archeological knowledge into predictive surfaces. Analytic predictive archeological models have great utility for state departments of transportation, and some states have invested millions of dollars in such models. However, classic statistical modeling approaches often require too much data and create questions about whether areas are categorized as low probability because (a) there are no sites or (b) no surveys have been conducted there. However, this process can build robust models around typically sparse archeological data and is not subject to spatial bias. These models are intended to lower overall project costs by identifying corridors with a lower probability of having archeological sites, not to supplant field surveys once a corridor has been chosen. Five influencing factors were defined by archeologists and were calculated with the ArcGIS platform. The archeologists then informed a fuzzy logic induction process that was mapped to output probability functions. These data were geocoded into ArcGIS output surfaces that showed the probability of encountering artifacts. The predictive results were tested through a blind control protocol against cleansed archeological data. These models were shown to perform as well as or better than traditional statistical models and required much less data. The Kentucky implementation includes the superior predictive coverage and, more important, a suite of tools to allow the ArcGIS-competent archeologist to design and execute new modeling routines or to build new models. The availability of higher-quality geographic information systems data will also allow archeologists to update the model.