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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Communication, Smart Technologies and Innovation for Society - Proceedings of CITIS 2021
Editores	Álvaro Rocha, Paulo Carlos López-López, Juan Pablo Salgado-Guerrero
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	117-128
Número de páginas	12
ISBN (versión impresa)	9789811641251
DOI	https://doi.org/10.1007/978-981-16-4126-8_12
Estado	Publicada - 2022
Evento	7th International Conference on Science, Technology and Innovation for Society, CITIS 2021 - Virtual, Online Duración: 26 may. 2021 → 28 may. 2021

Serie de la publicación

Nombre	Smart Innovation, Systems and Technologies
Volumen	252
ISSN (versión impresa)	2190-3018
ISSN (versión digital)	2190-3026

Conferencia

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

Nota bibliográfica

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

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

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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. En 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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author = "Zapata, {Lina Patricia} and Larios, {Victor Manuel} and Carrasco, {Francisco Castro} and Aguayo, {Jos{\'e} Luis}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 7th International Conference on Science, Technology and Innovation for Society, CITIS 2021 ; Conference date: 26-05-2021 Through 28-05-2021",

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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. En 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

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AB - 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. Model of State Spaces for Estimating the Dynamic Origin–Destination Matrix for a Public Transport Network by Applying the Kalman Filter. En Rocha Á, López-López PC, Salgado-Guerrero JP, editores, 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

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