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Generative network models for simulating urban networks, the case of inter-city transport network in Southeast Asia

机译:用于模拟城市网络的生成网络模型,在东南亚的城际交通网络的情况下

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摘要

This paper examines the driving forces of urban network formation through the simulation of inter-city transportation networks in Southeast Asia. We present a generative network model (GNM) considering geographical and topological effects, thus combining factors commonly analysed through traditional spatial simulation models (e.g., gravity models) and topological simulation models (e.g., actor-oriented stochastic models)in a single framework. In our GNM, it is assumed that the probability of connections between cities emerges from competing forces. Stimulating factors are a measure of city size (i.e., population) and a topological rule favouring the formation of connections between cities sharing nearest neighbours (i.e., transitive effects). The hampering factors are physical distance between two cities as well as institutional distance (i.e., border effects). We discuss the model in the context of on-going engagements between urban-geographical research and the network science literature, and validate the credence of the model against empirical data on the transport networks connecting 51 major cities in Southeast Asia. Our results show that (1) the generated networks approximate the observed ones in terms of average path length, clustering, modularity, efficiency and quadratic assignment procedure (QAP) correlation between the observed composite network and the generated one, and that (2) GNM performs best when topographical and topological factors are considered simultaneously. Each factor contributes differently to network formation, with transitive effects playing the most important role.
机译:本文通过对东南亚城市间交通网络的模拟,研究了城市网络形成的驱动力。我们提出了一种考虑地理和拓扑影响的生成网络模型(GNM),从而在单个框架中结合了通常通过传统空间模拟模型(例如重力模型)和拓扑模拟模型(例如面向角色的随机模型)分析的因素。在我们的GNM中,假设城市之间建立联系的可能性来自竞争力量。刺激因素是对城市规模(即人口)的度量,也是一种拓扑规则,有利于在共享最近邻的城市之间建立联系(即传递效应)。阻碍因素是两个城市之间的物理距离以及机构距离(即边界效应)。我们将在城市地理研究与网络科学文献之间正在进行的合作中讨论该模型,并根据连接东南亚51个主要城市的运输网络的经验数据验证该模型的可信度。我们的结果表明(1)生成的网络在观察到的复合网络与生成的网络之间的平均路径长度,聚类,模块化,效率和二次分配过程(QAP)相关性方面近似于观测到的网络,以及(2)GNM同时考虑地形和拓扑因素时,效果最佳。每个因素对网络形成的贡献均不同,传递效应起着最重要的作用。

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