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Inverse Analysis of Origin-Destination matrix for Microscopic Traffic Simulator

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1 The University of Tokyo, Bunkyo, Tokyo, Japan。

Computer Modeling in Engineering & Sciences 2017, 113(1), 71-87. https://doi.org/10.3970/cmes.2017.113.068

Abstract

Microscopic traffic simulations are useful for solving various traffic- related problems, e.g. traffic jams and accidents, local and global environmental and energy problems, maintaining mobility in aging societies, and evacuation plan- ning for natural as well as man-made disasters. The origin-destination (OD) matrix is often used as the input to represent traffic demands into traffic simulators. In this study, we propose an indirect method for estimating the OD matrix using a traffic simulator as an internal model. The proposed method is designed to output results that are consistent with the input of the simulator. The method consists of the following steps: (1) calculating link traffic volume from the OD matrix, and (2) updating the matrix. The estimated matrix is updated iteratively until it converges to a predefined tolerance level. Numerical experiments are then conducted using the proposed method on a grid network and on a representation of an actual road network. Finally, we discuss the characteristics of the proposed method and the non-negative constraint for the traffic volume.

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APA Style
Abe, K., Fujii, H., Yoshimura, S. (2017). Inverse analysis of origin-destination matrix for microscopic traffic simulator. Computer Modeling in Engineering & Sciences, 113(1), 71-87. https://doi.org/10.3970/cmes.2017.113.068
Vancouver Style
Abe K, Fujii H, Yoshimura S. Inverse analysis of origin-destination matrix for microscopic traffic simulator. Comput Model Eng Sci. 2017;113(1):71-87 https://doi.org/10.3970/cmes.2017.113.068
IEEE Style
K. Abe, H. Fujii, and S. Yoshimura, “Inverse Analysis of Origin-Destination matrix for Microscopic Traffic Simulator,” Comput. Model. Eng. Sci., vol. 113, no. 1, pp. 71-87, 2017. https://doi.org/10.3970/cmes.2017.113.068



cc Copyright © 2017 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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