An Equilibrium-Seeking Search Algorithm for Integrating Large-Scale Activity-Based and Traffic Assignment Models

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Authors

Date

2025

Department

Department of Built Environment

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Mcode

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Language

en

Pages

15

Series

IEEE Open Journal of Intelligent Transportation Systems, Volume 6, pp. 1156-1170

Abstract

This paper proposes an iterative methodology to integrate large-scale behavioral activity-based models with mesoscopic traffic assignment models. The proposed approach fully decouples the two parts, allowing the ex-post integration of multiple models as long as certain assumptions are satisfied. A measure of error is defined to characterize a search space easily explorable within its boundaries. Within it, a joint distribution of the number of trips and travel times is identified as the equilibrium distribution, i.e., the distribution for which trip numbers and travel times are bound in the neighborhood of the equilibrium between supply and demand. The approach is tested on a medium-sized city of 400,000 inhabitants and the results suggest that the proposed iterative approach does perform well, reaching equilibrium between demand and supply in a limited number of iterations thanks to its perturbation techniques. Overall, 15 iterations are needed to reach values of the measure of error lower than 5%. The equilibrium identified this way is then validated against baseline distributions to demonstrate the goodness of the results.

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| openaire: EC/H2020/856602/EU//FINEST TWINS Publisher Copyright: © IEEE. 2020 IEEE.

Keywords

Activity-based models, Measure of error, Model integration

Other note

DOI

10.1109/OJITS.2025.3600918

Citation

Agriesti, S, Roncoli, C & Nahmias-Biran, B H 2025, 'An Equilibrium-Seeking Search Algorithm for Integrating Large-Scale Activity-Based and Traffic Assignment Models', IEEE Open Journal of Intelligent Transportation Systems, vol. 6, pp. 1156-1170. https://doi.org/10.1109/OJITS.2025.3600918

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https://urn.fi/URN:NBN:fi:aalto-202509037084

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