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.
| Original language | English |
|---|---|
| Journal | Transport Reviews |
| Volume | 42 |
| Issue number | 4 |
| Pages (from-to) | 415-440 |
| Number of pages | 26 |
| ISSN | 0144-1647 |
| DOIs | |
| Publication status | Published - 2022 |
Bibliographical note
Funding Information:This work was supported by the Deutsche Forschungsgemeinschaft DFG under Grant 415208373; Guido Cantelmo acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie 754462.
Funding Information:
This work was supported by the Deutsche Forschungsgemeinschaft DFG under Grant 415208373; Guido Cantelmo acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie 754462. We want to thank the three anonymous reviewers for taking the time to provide their valuable comments. We are deeply grateful to them for helping us enrich the manuscript and improve it significantly. We also want to thank Santhanakrishnan Narayanan (TU Munich) for sharing the codes to query the scientific database.
Publisher Copyright:
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Keywords
- Big data
- Open data
- Public data
- Transport model
- Transport modelling