Data-Driven Modelling of Routing and Scheduling in Freight Transport

A. Nadi Najafabadi

doi:10.4233/uuid:6d71f79a-5cb5-4b64-a256-2906dbbedff6

Data-Driven Modelling of Routing and Scheduling in Freight Transport

A. Nadi Najafabadi

Transport and Planning

Research output: Thesis › Dissertation (TU Delft)

74 Downloads (Pure)

Abstract

This dissertation includes initial steps towards introducing a data-driven integrated logistics and traffic modelling framework. The main objective is to unravel the complex interaction between freight transport and traffic systems and to incorporate this knowledge into measures for improving the performance of traffic and logistics operations. Using large databases of observed truck trips and empirical research, a data-driven modelling pipeline is developed and applied, leading to new knowledge about the organization of road transport, in time and space.

Original language	English
Awarding Institution	Delft University of Technology
Supervisors/Advisors	Tavasszy, L.A., Supervisor van Lint, J.W.C., Supervisor Snelder, M., Advisor
Award date	17 Oct 2022
Print ISBNs	978-90-5584-317-6
DOIs	https://doi.org/10.4233/uuid:6d71f79a-5cb5-4b64-a256-2906dbbedff6
Publication status	Published - 2022

Bibliographical note

TRAIL Thesis Series no. T2022/14, the Netherlands Research School TRAIL

Access to Document

10.4233/uuid:6d71f79a-5cb5-4b64-a256-2906dbbedff6

DissertationFinal published version, 8.48 MBLicence: CC BY

Cite this

@phdthesis{6d71f79a5cb54b64a2562906dbbedff6,

title = "Data-Driven Modelling of Routing and Scheduling in Freight Transport",

abstract = "This dissertation includes initial steps towards introducing a data-driven integrated logistics and traffic modelling framework. The main objective is to unravel the complex interaction between freight transport and traffic systems and to incorporate this knowledge into measures for improving the performance of traffic and logistics operations. Using large databases of observed truck trips and empirical research, a data-driven modelling pipeline is developed and applied, leading to new knowledge about the organization of road transport, in time and space. ",

author = "{Nadi Najafabadi}, A.",

note = "TRAIL Thesis Series no. T2022/14, the Netherlands Research School TRAIL",

year = "2022",

doi = "10.4233/uuid:6d71f79a-5cb5-4b64-a256-2906dbbedff6",

language = "English",

isbn = "978-90-5584-317-6",

series = "TRAIL Thesis Series T2022/14",

number = "14",

school = "Delft University of Technology",

}

TY - THES

T1 - Data-Driven Modelling of Routing and Scheduling in Freight Transport

AU - Nadi Najafabadi, A.

N1 - TRAIL Thesis Series no. T2022/14, the Netherlands Research School TRAIL

PY - 2022

Y1 - 2022

N2 - This dissertation includes initial steps towards introducing a data-driven integrated logistics and traffic modelling framework. The main objective is to unravel the complex interaction between freight transport and traffic systems and to incorporate this knowledge into measures for improving the performance of traffic and logistics operations. Using large databases of observed truck trips and empirical research, a data-driven modelling pipeline is developed and applied, leading to new knowledge about the organization of road transport, in time and space.

AB - This dissertation includes initial steps towards introducing a data-driven integrated logistics and traffic modelling framework. The main objective is to unravel the complex interaction between freight transport and traffic systems and to incorporate this knowledge into measures for improving the performance of traffic and logistics operations. Using large databases of observed truck trips and empirical research, a data-driven modelling pipeline is developed and applied, leading to new knowledge about the organization of road transport, in time and space.

U2 - 10.4233/uuid:6d71f79a-5cb5-4b64-a256-2906dbbedff6

DO - 10.4233/uuid:6d71f79a-5cb5-4b64-a256-2906dbbedff6

M3 - Dissertation (TU Delft)

SN - 978-90-5584-317-6

T3 - TRAIL Thesis Series T2022/14

ER -