The entropy maximization method was implemented in a study of a small community in Alabama, with minimal congestion, was tested and analyzed. Entropy maximization work was performed by Wilson and applied by Van Zuylen and Willumsen for origin/destination matrix estimation. Finding ways to reduce the time required and minimize the costs associated with developing these travel demand forecasting models would be beneficial to communities that maintain these models for forecasting and planning. Unfortunately, the money and time required for data collection and model development frequently result in only a little left over for the forecasting phase. These models can be used to represent current traffic volume, predict future traffic volumes, test alternative scenarios, and evaluate the transportation network. These models are a key analysis tool used in transportation planning and to support infrastructure investment decisions.
Introduction Travel demand forecasting models provide a representation of the traffic volumes on roadways within a transportation network. The estimated O/D can be used to support the development of travel demand model inputs and parameters, reducing the dependency on expensive household surveys.ġ. The paper presents the acceptable level traffic counts necessary to develop a statistically significant O/D matrix through estimation. This paper provides a case study highlighting the issues regarding the necessity of a base/seed matrix, the number of available traffic counts/link flows and their impact on the quality of the generated origin-destination matrix. To extend this research to study a large congested network, traffic counts were removed from a larger and semi-congested network with a known O/D matrix, and a complete set of traffic counts were used to determine the methodology’s ability to reflect the estimation of the actual matrix. The previous research evaluated the potential to estimate an accurate origin/destination matrix from existing traffic counts using entropy maximization method implemented in a study of a small community in Alabama, with minimal congestion.
Concerns for safety and need to avoid inconveniencing drivers have limited the use of traditional household surveys. Travel demand forecasting models are a key analysis tool used in transportation planning and to support infrastructure investment decisions.
0 kommentar(er)
