Mapping Bicycling Patterns with an Agent-Based Model, Census and Crowdsourced Data
Mapping Bicycling Patterns with an Agent-Based Model, Census and Crowdsourced Data
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使用基于代理的模型、人口普查和众包数据绘制骑行模式
DOI：
10.1007/978-3-319-51957-9_7
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
C. Pettit
中科院分区：
文献类型：
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作者：
S. Leao;C. Pettit
随着我们的城市继续发展，拥堵、空气污染和人口健康等问题也在增加。积极的交通可以在激活市民和城市的多重利益方面发挥重要作用。在这项研究中，我们的注意力集中在理解骑自行车的人作为一种积极的交通方式的模式和行为上。有许多数据源可用于分析跨城市骑自行车的模式。随着带有GPS和骑行规范应用程序的智能手机的出现，众包方法可以用来获取精细的个人自行车出行模式。在这项研究中，我们分析了通过乘客日志应用程序获得的这种众包数据，并特别关注悉尼市。我们使用这个丰富的数据源以及其他更传统的工作旅程和家庭旅行调查数据，使用开源的GAMA平台创建一个基于代理的模型。这篇论文的工作是建立一个更复杂的基于代理的模型(ABM)来理解悉尼市的骑行模式的早期工作，因此我们首先测试简单的假设是最短距离骑自行车通勤的主要标准？
As our cities continue to grow issues such as congestion, air pollution and population health are also on the increase. Active transport can play an important part in activating multi-benefits for citizens and the city. In this research we focus our attention on understanding the patterns and behaviours of bicyclists as a form of active transport. There are a number of data sources which can be used to analyse patterns of cycling across cities. With the advent of smart phones with GPS and cycling specification apps, crowdsourced approaches can be used to acquire fine scale individual cycle travel patterns. In this research we analyse such crowdsourced data acquired through the riderlog application with specific focus on the City of Sydney. We use this rich data source along with other a more traditional journey to work and household travel survey data to create an agent based model using the open source GAMA platform. The work in this paper is early work in building a more sophisticated Agent-Based Model (ABM) to understanding cycling patterns across the City of Sydney to hence we commence by first testing the simple hypothesis is the shortest distance the main criteria for commuting by bicycle?