A model for optimizing the time distribution of trips in dynamic traffic assignment

Articles in Press

Document Type : Original Article

Authors

1 Professor-School of Civil Engineering-Iran University of Science and Technology

2 School of Civil Engineering-Iran University of Science and Technology

3 Consulting Manager, AECOM

10.22034/tri.2023.391962.3143

Abstract

Rapid urban growth has led to an increase in travel demand and private vehicle ownership, and the existing infrastructure has not been able to match the effects of demand and traffic congestion. As traffic density increases, passenger delays increase and network reliability decreases. In the analysis of transportation systems, estimation and analysis of travel demand have particular importance to achieve an efficient and effective transportation system. Estimating and correcting demand matrices is very important, and more reliable and practical results can be achieved by using more accuracy and details in their estimation. In the problem of matrix estimation and modification, especially in dense networks, the accuracy of models significantly increases by using simulation models. In this research, the goal is to optimize the time distribution coefficients of the demand matrix in 15-minute steps for the entire study period. A bi-level approach has been used to estimate the travel demand matrix from previous matrices, which are the static matrices of comprehensive studies. At the upper level, it produces coefficients by Gaussian mixture models and uses Teaching–Learning-Based Optimization algorithm to optimize. The objective function is to minimize the error through the RMSE value. Unused stations are used to validate the model, and the detection coefficient, R^2, in all intervals and stations is 0.91 and in the 3 peak periods, the value is 0.88, according to the optimization approach using previous matrices, to change the time distribution of trips (trips start), the results show the proper functioning of the presented bi-level process.

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Journal of Transportation Research

Articles in Press, Accepted Manuscript
Available Online from 16 July 2023

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