To solve this congestion travel time concern, travel assignment procedures often use an iterative approach until the route choice proportions, and trip matrix are similarly consistent. Under congested conditions, trips are allowed to take paths other than the uncongested shortest travel path due to congestion time increases on the main roadways. The difference between this work and the work by Wilson is the inclusion of congestion in the network.
Literature Review A brief review of relevant literature is provided to highlight the contribution of this paper. 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. The goal of this paper is to determine the acceptable level traffic counts necessary to develop a statistically significant O/D matrix through estimation. Sensitivity analysis was performed by testing simplified scenarios using different seed matrices. Additionally, as several approaches for O/D estimation rely on a seed matrix to work properly, this paper tested the influence of the seed matrix on the O/D matrix estimation. To do so, 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 an actual matrix accurately. The application of the entropy maximization technique for estimating an origin/destination (O/D) matrix from traffic counts can be explored and extended to study a large congested network. It also determined the relationship between the number of traffic counts required in a network and the number of origin/destination pairs to build an accurate origin/destination matrix, since existing traffic counts are relatively easy and inexpensive to obtain. The study by Wilson evaluated the potential to estimate an accurate origin/destination matrix from existing traffic counts. 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.
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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.
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