ABSTRACT

Data play an indispensable role in transport modelling. The availability of data from non-conventional sources, such as mobile phones, social media, and public transport smart cards, changes the way we conduct mobility analyses and travel forecasting. Existing studies have demonstrated the multitude and varied applications of these emerging data in transport modelling. The transferability of current research and further endeavours depend mostly on the availability of these data. Therefore, the openness or public availability of the prominent data for transport modelling needs to be adequately investigated. Such a discussion should also encompass these data’s application aspects to provide a holistic overview. This paper defines a typology for the data classification based on a set of availability or openness attributes from the existing literature. Subsequently, we use the developed typology to classify the prominent transport data into four categories: (i) Commercial data, (ii) Inaccessible data, (iii) Gratis and accessible data with restricted use, and (iv) Open data. Using this typology, we conclude that the public data, which refer to the data that are accessible and free of cost, are a superset of open data. Further, we discuss the applications and limitations of the selected data in transport modelling and highlight in which task(s) certain data excel. Lastly, we synthesise our review using a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis to bring out the aspects relevant to data owners and data consumers. Public availability of data can help in various modelling steps such as trip generation, accessibility, destination choice, route choice, network modelling. Complementary datasets such as General Transit Feed Specification (GTFS) and Volunteered Geographic Information (VGI) increase the usability of other data. Thus, modellers can gain from the positive cascade effect by prioritising these data. There is also a potential for data owners to release proprietary data, such as mobile phone data, with restricted-use licenses after addressing privacy risks. Our study contributes by dealing with two problems at the same time. On the one hand, the paper analyses existing data based on their potential for mobility studies. On the other hand, we classify them based on how open they are. Hence, we identify the most promising public data for developing the next generation of transport models.

摘要

数据在交通建模中起着不可或缺的作用。来自移动电话、社交媒体和公共交通智能卡等非传统来源的数据的可用性改变了我们进行移动分析和旅行预测的方式。现有研究已经证明了这些新兴数据在交通建模中的多种应用。当前研究和进一步努力的可转移性主要取决于这些数据的可用性。因此，需要对交通建模的重要数据的开放性或公共可用性进行充分调查。这样的讨论还应该包括这些数据的应用方面，以提供一个整体的概述。本文根据现有文献中的一组可用性或开放性属性定义了数据分类的类型。随后，我们使用开发的类型学将突出的交通数据分为四类：（i）商业数据，（ii）不可访问的数据，（iii）免费和受限使用的可访问数据，以及（iv）开放数据。使用这种类型，我们得出结论，公共数据是指可访问且免费的数据，是开放数据的超集。此外，我们讨论了所选数据在交通建模中的应用和局限性，并强调了某些数据在哪些任务中表现出色。最后，我们使用优势、劣势、机会和威胁 (SWOT) 分析来综合我们的审查，以找出与数据所有者和数据消费者相关的方面。数据的公共可用性有助于各种建模步骤，例如行程生成、可达性、目的地选择、路线选择、网络建模。通用运输饲料规范 (GTFS) 和自愿地理信息 (VGI) 等补充数据集提高了其他数据的可用性。因此，建模者可以通过优先处理这些数据来从积极的级联效应中获益。数据所有者也有可能在解决隐私风险后发布具有限制使用许可的专有数据，例如手机数据。我们的研究有助于同时处理两个问题。一方面，本文根据现有数据在流动性研究方面的潜力进行了分析。另一方面，我们根据它们的开放程度对其进行分类。因此，我们确定了用于开发下一代交通模型的最有希望的公共数据。