Coordinated vehicle dispatching and charging scheduling for electric ride hailing fleet under charging congestion

Tai-Yu Ma; Richard Connors; Francesco Viti

Coordinated vehicle dispatching and charging scheduling for electric ride hailing fleet under charging congestion

Tai-Yu Ma
, Richard Connors
, Francesco Viti

Urban Development & Mobility

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Conference contribution › Revue par des pairs

15 Téléchargements (Pure)

Résumé

This study develops a sequential optimization approach to coordinate vehicle dispatching and charging operations for e-taxi fleet management under stochastic demand, variable energy prices, and congested charging facilities. We conduct a simulation case study using NYC taxi data. The computational results show the developed methodology outperforms several benchmark approaches with higher profit and customer service rates under different scenarios (demand intensities, battery capacity, fleet size/charging facilities).

langue originale	Anglais
titre	Proceedings of the 12th Triennial Symposium on Transportation Analysis conference (TRISTAN XII)
Sous-titre	Okinawa, Japan, 22-27 June 2025.
Nombre de pages	4
état	Publié - avr. 2025

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https://tristan2025.org/proceedings/TRISTAN2025_ExtendedAbstract_182.pdf

M-EVRST: Multimodal Electric VEhicle demand RESponsive Transport
Ma, T.-Y. (PI), Klein, S. (CoI), Viti, F. (???upmproject.roles.upmproject.copi???), Chow, J. Y. J. (Non Contracting Partner), Connord, R. (CoI) & Venditti, S. (CoI)
Fonds National de la Recherche, Luxembourg Institute of Socio-Economic Research LISER
1/04/21 → 31/03/24
Projet: Recherche

Contient cette citation

@inproceedings{c27662940980418d9652bfc9f0afc3e1,

title = "Coordinated vehicle dispatching and charging scheduling for electric ride hailing fleet under charging congestion",

abstract = "This study develops a sequential optimization approach to coordinate vehicle dispatching and charging operations for e-taxi fleet management under stochastic demand, variable energy prices, and congested charging facilities. We conduct a simulation case study using NYC taxi data. The computational results show the developed methodology outperforms several benchmark approaches with higher profit and customer service rates under different scenarios (demand intensities, battery capacity, fleet size/charging facilities).",

keywords = "ride-hailing, electric vehicle, charging management, capacitated charging network, time-of-use energy prices",

author = "Tai-Yu Ma and Richard Connors and Francesco Viti",

year = "2025",

month = apr,

language = "English",

booktitle = "Proceedings of the 12th Triennial Symposium on Transportation Analysis conference (TRISTAN XII)",

}

Coordinated vehicle dispatching and charging scheduling for electric ride hailing fleet under charging congestion. / Ma, Tai-Yu; Connors, Richard; Viti, Francesco.
Proceedings of the 12th Triennial Symposium on Transportation Analysis conference (TRISTAN XII): Okinawa, Japan, 22-27 June 2025.. 2025.

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Conference contribution › Revue par des pairs

TY - GEN

T1 - Coordinated vehicle dispatching and charging scheduling for electric ride hailing fleet under charging congestion

AU - Ma, Tai-Yu

AU - Connors, Richard

AU - Viti, Francesco

PY - 2025/4

Y1 - 2025/4

N2 - This study develops a sequential optimization approach to coordinate vehicle dispatching and charging operations for e-taxi fleet management under stochastic demand, variable energy prices, and congested charging facilities. We conduct a simulation case study using NYC taxi data. The computational results show the developed methodology outperforms several benchmark approaches with higher profit and customer service rates under different scenarios (demand intensities, battery capacity, fleet size/charging facilities).

AB - This study develops a sequential optimization approach to coordinate vehicle dispatching and charging operations for e-taxi fleet management under stochastic demand, variable energy prices, and congested charging facilities. We conduct a simulation case study using NYC taxi data. The computational results show the developed methodology outperforms several benchmark approaches with higher profit and customer service rates under different scenarios (demand intensities, battery capacity, fleet size/charging facilities).

KW - ride-hailing

KW - electric vehicle

KW - charging management

KW - capacitated charging network

KW - time-of-use energy prices

M3 - Conference contribution

BT - Proceedings of the 12th Triennial Symposium on Transportation Analysis conference (TRISTAN XII)

ER -

Coordinated vehicle dispatching and charging scheduling for electric ride hailing fleet under charging congestion

Résumé

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Projets

M-EVRST: Multimodal Electric VEhicle demand RESponsive Transport

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