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首页> 外文期刊>Journal of Transport Geography >How urban density, network topology and socio-economy influence public transport ridership: Empirical evidence from 48 European metropolitan areas
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How urban density, network topology and socio-economy influence public transport ridership: Empirical evidence from 48 European metropolitan areas

机译：城市密度，网络拓扑结构和社会经济如何影响公共交通出行率：来自48个欧洲大都市区的经验证据

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Understanding the determinants of public transport ridership is important in order to plan attractive public transport systems efficiently. This study analyses at a meta-level per capita public transport ridership across 48 European cities based on a rich database collected as part of this study. The dataset includes detailed mode specific information about the public transport networks, hence extending previous research by analysing each public transport mode separately while simultaneously taking into account the main determinants of ridership identified by a thorough literature review of 36 previous studies, e.g. urban demographics and land uses. Factor analysis was deployed revealing four main composite determinants, namely i) metro coverage, network connectivity, and urban density, ii) suburban rail coverage, iii) economic inequality, and iv) light rail coverage. Subsequent multiple regression analysis confirmed the a priori hypothesis of per capita ridership being positively associated with the extent of network coverage in terms of metro, suburban rail and light rail transit. The importance of network connectivity was included with results suggesting that the number of transfer stations was more important than the cyclomatic number of the public transport network. Cities with higher economic inequality in terms of higher unemployment, lower per capita GDP and higher GINI-coefficient showed lower public transport ridership. Finally, the analyses highlighted the importance of proper definitions of urban areas in order to perform consistent analyses of data across cities. This revealed the impact on transit ridership of urban density defined as population and especially job intensity per km(2). As the study is based on aggregate, cross-sectional data from a relatively small sample of European cities, it is not without limitations in terms of mainly revealing correlational structures rather than causations as well as not including all variables related to public transport ridership. Future studies should further investigate these interrelationships before drawing conclusions on the causational relationships.
机译：为了有效地规划吸引人的公共交通系统，了解公共交通出行的决定因素很重要。这项研究基于本研究的一部分，收集了丰富的数据库，对欧洲48个城市的人均公共交通出行量进行了分析。数据集包含有关公共交通网络的详细模式特定信息，因此通过分别分析每种公共交通模式来扩展先前的研究，同时考虑到对36项先前研究的全面文献综述（例如，第2部分）确定的乘车率的主要决定因素。城市人口统计和土地利用。进行了因素分析，揭示了四个主要的综合决定因素，即：i）地铁覆盖范围，网络连通性和城市密度； ii）郊区铁路覆盖率； iii）经济不平等； iv）轻轨覆盖率。随后的多元回归分析证实了人均乘车率的先验假设与地铁，郊区铁路和轻轨交通网络覆盖范围的正相关。结果包括网络连接的重要性，表明中转站的数量比公共交通网络的圈数更重要。在较高的失业率，较低的人均GDP和较高的GINI系数等方面，经济不平等程度较高的城市，其公共交通出行率较低。最后，分析强调了正确定义城市区域的重要性，以便对整个城市的数据进行一致的分析。这揭示了人口密度特别是每公里工作强度对城市交通过境的影响（2）。由于该研究基于相对较小的欧洲城市样本的总体横截面数据，因此在主要揭示相关结构而非因果关系以及不包括与公共交通出行相关的所有变量方面，它并非没有局限。在得出因果关系的结论之前，未来的研究应进一步研究这些相互关系。

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