Modeling of shared mobility services - An approach in between aggregate four-step and disaggregate agent-based approaches for strategic transport planning

Narayanan, Santhanakrishnan; Grau, Josep Maria Salanova; Frederix, Rodric; Tympakianaki, Athina; Masegosa, Antonio D.; Antoniou, Constantinos

doi:10.1080/15472450.2023.2246374

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…Numerous European cities, particularly those that are small to medium-sized, have persisted in employing the traditional strategic four-step modeling approach for various reasons. These reasons encompass factors such as inadequate data, a shortage of technical expertise, and the preference for simpler models [20]. The widely adopted four-step transportation demand forecasting models are currently utilized in the majority of cities and regions to inform decision-making processes associated with transportation planning [21].…”

Section: State Of the Artmentioning

confidence: 99%

Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case

Ranceva,

Ušpalytė-Vitkūnienė

2024

Sustainability

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A transport model usually consists of a demand model and an available transport network model. The purpose of this article is to identify the key specifics for the development of a regional public transport (PT) demand model and to point out the differences from the urban PT demand model. The traditional four-step transport planning demand model consists of trip generation, trip distribution, modal split, and assignment. This article consists of PT model development, calibration, and validation. A PTV VISUM macroscopic modeling program is used for this research. As a result, this article presents basic suggestions for how a PT demand model should be developed in regions. The presented suggestions for developing a PT demand model can be applied to any low-density region. The rest of the article is structured as follows: (1) Background: presents a literature analysis of the four-step model, modal splits, and the features of the PTV VISUM program; (2) Methods: describes the considered region of Lithuania and the data of the developed model; describes the four-step model, which is adapted to the Lithuanian region; (3) Results: presents the results and main suggestions for creating a PT demand model; and (4) Conclusions: presents the main conclusions of the study.

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Section: State Of the Artmentioning

confidence: 99%

Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case

Ranceva,

Ušpalytė-Vitkūnienė

2024

Sustainability

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Can emerging mobility solutions complement public transport and lead to a sustainable future?: A case study on Regensburg, Germany

Narayanan,

Arango,

Tympakianaki

et al. 2025

Case Studies on Transport Policy

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Graph neural networks as strategic transport modelling alternative ‐ A proof of concept for a surrogate

Narayanan,

Makarov,

Antoniou

2024

IET Intelligent Trans Sys

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Practical applications of graph neural networks (GNNs) in transportation are still a niche field. There exists a significant overlap between the potential of GNNs and the issues in strategic transport modelling. However, it is not clear whether GNN surrogates can overcome (some of) the prevalent issues. Investigation of such a surrogate will show their advantages and the disadvantages, especially throwing light on their potential to replace complex transport modelling approaches in the future, such as the agent‐based models. In this direction, as a pioneer work, this paper studies the plausibility of developing a GNN surrogate for the classical four‐step approach, one of the established strategic transport modelling approaches. A formal definition of the surrogate is presented, and an augmented data generation procedure is introduced. The network of the Greater Munich metropolitan region is used for the necessary data generation. The experimental results show that GNNs have the potential to act as transport planning surrogates and the deeper GNNs perform better than their shallow counterparts. Nevertheless, as expected, they suffer performance degradation with an increase in network size. Future research should dive deeper into formulating new GNN approaches, which are able to generalize to arbitrary large networks.

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Modeling of shared mobility services - An approach in between aggregate four-step and disaggregate agent-based approaches for strategic transport planning

Cited by 5 publications

References 30 publications

Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case

Specifics of Creating a Public Transport Demand Model for Low-Density Regions: Lithuanian Case

Can emerging mobility solutions complement public transport and lead to a sustainable future?: A case study on Regensburg, Germany

Graph neural networks as strategic transport modelling alternative ‐ A proof of concept for a surrogate

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