Civil Engineering Commons^™

Traffic Signal Control - A Neural Network Approach, Gerard Nivard Cadet

FIU Electronic Theses and Dissertations

Artificial Neural Networks (ANNs) have been proven to be an important development in a variety of problem solving areas. Increasing research activity in ANN applications has been accompanied by equally rapid growth in the commercial mainstream use of ANNs. However, there is relatively little research of practical application of ANNs taking place in the field of transportation engineering. The central idea of this thesis is to use Artificial Neural Network Software Autonet in connection with Highway Capacity Software to estimate delay. Currently existing signal control system are briefly discussed and their short coming presented. As a relative new mathematical model, …

Temporal Gis Applications In Public Transit Planning And Management, Hesham R. Elbadrawi

FIU Electronic Theses and Dissertations

Geographic Information Systems (GISs) provide a powerful framework for various tasks of transit management such as planning, performance evaluation, and marketing. GIS may be used to solve complex planning problems, assist in operations planning, and meet other management and operational needs. However, due to the changing nature of transit planning and operational data, transit planners and operators need to analyze the data over time, which requires a temporal GIS that is capable of storing, manipulating, and analyzing changes with respect to both time and space.

Temporal GIS will allow planners and transit operators to analyze data within a certain time …

Flexible Pavement Layer Moduli Determination : An Adaptive Artificial Neural Network Approach, Michael A. Adeife

FIU Electronic Theses and Dissertations

The estimation of pavement layer moduli through the use of an artificial neural network is a new concept which provides a less strenuous strategy for backcalculation procedures. Artificial Neural Networks are biologically inspired models of the human nervous system. They are specifically designed to carry out a mapping characteristic. This study demonstrates how an artificial neural network uses non-destructive pavement test data in determining flexible pavement layer moduli. The input parameters include plate loadings, corresponding sensor deflections, temperature of pavement surface, pavement layer thicknesses and independently deduced pavement layer moduli.