位置:首页 >工程技术 >交通运输科学与技术 >EUROPEAN TRANSPORT RESEARCH REVIEW >Transport indicator analysis and comparison of 151 urban areas, based on open source data

基于开源数据,对151个城市地区的交通指标进行分析和比较

Transport indicator analysis and comparison of 151 urban areas, based on open source data

作者:Ali Enes Dingil;Joerg Schweizer;Federico Rupi;Zaneta Stasiskiene;

关键词:Infrastructure accessibility,Congestion,Open source data,OSM,TomTom,Transport policy,Population density

DOI:https://doi.org/10.1186/s12544-018-0334-4

发表时间:2018年

  • 文献详情
  • 相似文献

摘要

简介对大量城市地区的交通指标进行准确分析和比较,有助于评估所采取的不同交通政策的绩效。本文试图根据 OpenstreetMap (OSM) 和 TomTom 数据库等可比较的、可直接观察的开源数据,确定 151 个城市地区和 51 个国家的重要交通和社会经济指标。分析This是第一个使用来自世界各地不同城市地区的最新开源数据的系统指标分析。人均道路公里数指标(有时被称为基础设施可达性)是通过处理 OSM 数据计算得出的。有关拥堵程度的信息取自 TomTom 数据库,社会经济数据取自各种可公开访问的数据库。通过相关性确定指标之间的关系,并校准回归模型,量化交通基础设施和绩效指标之间的关系。分别定义和研究了不同人口规模城市的三个子类别(小城市、大城市和大都市)。此外,还进行了定性分析,将五个不同的指标联系起来。结果与结论主要结果再次证实了之前的发现,但样本量更大,数据更具可比性。基础设施可达性、社会经济指标和拥堵程度之间具有良好的相关性。研究表明,GDP 较高的城市通常建设了更多的基础设施,从而减少了拥堵程度。特别是对于人口密度较低(每平方公里约 1500 英寸以上)的城市,人均道路数量越多,拥堵程度就越低;如果人均铁路基础设施比率较高,人口密度高的城市的拥堵程度通常较低。此外,这些城市增加人均铁路比增加人均公路长度更能有效减少拥堵。


Abstract

IntroductionThe accurate analysis and comparison of transport indicators from a large variety of urban areas can help to evaluate the performance of different adopted transport policies. This paper attempts to determine important transport and socio-economic indicators from 151 urban areas and 51 countries, based on comparable, directly observable open-source data such as OpenstreetMap (OSM) and the TomTom database.AnalysisThis is the first, systematic indicator-analysis using recent, open source data from different urban areas around the world. The indicator road kilometers per person, sometimes cited as infrastructure accessibility is calculated by processing OSM data. Information on congestion levels have been taken from the TomTom database and socio-economic data from various, publicly accessible databases. Relations between indicators are identified through correlations and regression models are calibrated, quantifying the relation between transport infrastructure and performance indicators. Three sub-categories of cities with different population sizes (small cities, large cities and metropolises) are defined and studied individually. In addition, a qualitative analysis is performed, putting five different indicators into relation.Results & ConclusionsThe main results reconfirm previous findings but with a larger sample size and more comparable data. Good correlation values between infrastructure accessibility, socio-economic indicators, and congestion levels are demonstrated. It is shown that cities with higher GDP have generally built more infrastructure which in turn reduces their congestion levels. In particular, for cities with low population density (above approximately 1500 inh. Per sq.km), more roads per inhabitant lead to lower congestion levels; cities with high population density have in general lower congestion levels if the rail infrastructure per person ratio is high. Furthermore, these cities increasing railways per person is more effective in reducing congestions than increasing road length per person.