Model of State Spaces for Estimating the Dynamic Origin–Destination Matrix for a Public Transport Network by Applying the Kalman Filter

Lina Patricia Zapata; Victor Manuel Larios; Francisco Castro Carrasco; José Luis Aguayo

doi:10.1007/978-981-16-4126-8_12

Model of State Spaces for Estimating the Dynamic Origin–Destination Matrix for a Public Transport Network by Applying the Kalman Filter

Lina Patricia Zapata, Victor Manuel Larios, Francisco Castro Carrasco, José Luis Aguayo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A model to estimate the passenger flow based on the dynamic origin–destination matrix applied at the South-Eastern Corridor from the terrestrial transport network in the city of Quito was the goal of this project. In the model, first: The state space was defined for the transport network, and later, was applied the Kalman Filter algorithm to get the dynamic origin–destination matrix. The state variables defined were the origin–destination pairs, the temporal variation, and the speed changes in the origin–destination pairs. Finally, the measure vector, the evolution matrix model, the process noise matrix, and the measurement error matrix were developed.

Original language	English
Title of host publication	Communication, Smart Technologies and Innovation for Society - Proceedings of CITIS 2021
Editors	Álvaro Rocha, Paulo Carlos López-López, Juan Pablo Salgado-Guerrero
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	117-128
Number of pages	12
ISBN (Print)	9789811641251
DOIs	https://doi.org/10.1007/978-981-16-4126-8_12
State	Published - 2022
Event	7th International Conference on Science, Technology and Innovation for Society, CITIS 2021 - Virtual, Online Duration: 26 May 2021 → 28 May 2021

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	252
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	7th International Conference on Science, Technology and Innovation for Society, CITIS 2021
City	Virtual, Online
Period	26/05/21 → 28/05/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Dynamic origin–destination matrix
Kalman filter
Static origin–destination matrix

Access to Document

10.1007/978-981-16-4126-8_12

Cite this

Zapata, L. P., Larios, V. M., Carrasco, F. C., & Aguayo, J. L. (2022). Model of State Spaces for Estimating the Dynamic Origin–Destination Matrix for a Public Transport Network by Applying the Kalman Filter. In Á. Rocha, P. C. López-López, & J. P. Salgado-Guerrero (Eds.), Communication, Smart Technologies and Innovation for Society - Proceedings of CITIS 2021 (pp. 117-128). (Smart Innovation, Systems and Technologies; Vol. 252). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-4126-8_12

Zapata, Lina Patricia ; Larios, Victor Manuel ; Carrasco, Francisco Castro et al. / Model of State Spaces for Estimating the Dynamic Origin–Destination Matrix for a Public Transport Network by Applying the Kalman Filter. Communication, Smart Technologies and Innovation for Society - Proceedings of CITIS 2021. editor / Álvaro Rocha ; Paulo Carlos López-López ; Juan Pablo Salgado-Guerrero. Springer Science and Business Media Deutschland GmbH, 2022. pp. 117-128 (Smart Innovation, Systems and Technologies).

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abstract = "A model to estimate the passenger flow based on the dynamic origin–destination matrix applied at the South-Eastern Corridor from the terrestrial transport network in the city of Quito was the goal of this project. In the model, first: The state space was defined for the transport network, and later, was applied the Kalman Filter algorithm to get the dynamic origin–destination matrix. The state variables defined were the origin–destination pairs, the temporal variation, and the speed changes in the origin–destination pairs. Finally, the measure vector, the evolution matrix model, the process noise matrix, and the measurement error matrix were developed.",

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Zapata, LP, Larios, VM, Carrasco, FC & Aguayo, JL 2022, Model of State Spaces for Estimating the Dynamic Origin–Destination Matrix for a Public Transport Network by Applying the Kalman Filter. in Á Rocha, PC López-López & JP Salgado-Guerrero (eds), Communication, Smart Technologies and Innovation for Society - Proceedings of CITIS 2021. Smart Innovation, Systems and Technologies, vol. 252, Springer Science and Business Media Deutschland GmbH, pp. 117-128, 7th International Conference on Science, Technology and Innovation for Society, CITIS 2021, Virtual, Online, 26/05/21. https://doi.org/10.1007/978-981-16-4126-8_12

Model of State Spaces for Estimating the Dynamic Origin–Destination Matrix for a Public Transport Network by Applying the Kalman Filter. / Zapata, Lina Patricia; Larios, Victor Manuel; Carrasco, Francisco Castro et al.
Communication, Smart Technologies and Innovation for Society - Proceedings of CITIS 2021. ed. / Álvaro Rocha; Paulo Carlos López-López; Juan Pablo Salgado-Guerrero. Springer Science and Business Media Deutschland GmbH, 2022. p. 117-128 (Smart Innovation, Systems and Technologies; Vol. 252).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Zapata LP, Larios VM, Carrasco FC, Aguayo JL. Model of State Spaces for Estimating the Dynamic Origin–Destination Matrix for a Public Transport Network by Applying the Kalman Filter. In Rocha Á, López-López PC, Salgado-Guerrero JP, editors, Communication, Smart Technologies and Innovation for Society - Proceedings of CITIS 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 117-128. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-16-4126-8_12

Model of State Spaces for Estimating the Dynamic Origin–Destination Matrix for a Public Transport Network by Applying the Kalman Filter

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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