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Understanding Traffic Congestion via Network Analysis, Agent Modeling, and the Trajectory of Urban Expansion: A Coastal City Case

Auteur(s):

ORCID
Médium: article de revue
Langue(s): anglais
Publié dans: Infrastructures, , n. 6, v. 6
Page(s): 85
DOI: 10.3390/infrastructures6060085
Abstrait:

The study of patterns of urban mobility is of utter importance for city growth projection and development planning. In this paper, we analyze the topological aspects of the street network of the coastal city of Cartagena de Indias employing graph theory and spatial syntax tools. We find that the resulting network can be understood on the basis of 400 years of the city’s history and its peripheral location that strongly influenced and shaped the growth of the city, and that the statistical properties of the network resemble those of self-organized cities. Moreover, we study the mobility through the network using a simple agent-based model that allows us to study the level of street congestion depending on the agents’ knowledge of the traffic while they travel through the network. We found that a purely shortest-path travel scheme is not an optimal strategy and that assigning small weights to traffic avoidance schemes increases the overall performance of the agents in terms of arrival success, occupancy of the streets, and traffic accumulation. Finally, we argue that localized congestion can be only partially ascribed to topological properties of the network and that it is important to consider the decision-making capability of the agents while moving through the network to explain the emergence of traffic congestion in the system.

Copyright: © 2021 the Authors. Licensee MDPI, Basel, Switzerland.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10723042
  • Publié(e) le:
    22.04.2023
  • Modifié(e) le:
    10.05.2023
 
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