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Special Issue "Big Data, Travel Behaviour and Sustainable Transportation"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 May 2021).

Special Issue Editors

Dr. Jinhyun Hong
E-Mail Website
Guest Editor
Urban Studies, University of Glasgow, Glasgow G12 8RZ, UK
Interests: travel behaviour; ICT use; big data analytics; interaction between land use and travel behaviour
Dr. David Philip McArthur
E-Mail Website
Guest Editor
Urban Studies, University of Glasgow, Glasgow G12 8RZ, UK
Interests: active travel; new and emerging data forms; interactions between transport networks and labour markets

Special Issue Information

Dear Colleagues,

New forms of data generated naturally by various technologies, sometimes referred to as Big Data, have provided unprecedented opportunities for transport researchers and planners. They include detailed mobility information at fine temporal and geographic scales. The richness of these data allows researchers to examine various aspects of sustainable transport systems and travel behaviours, which would not have been possible with traditional transport data such as travel surveys. However, there are a range of barriers to using them in research. These include issues such as privacy, data quality (representativeness, relevance), computational demands, and accessibility/cost.

This Special Issue seeks to deepen our understanding of new opportunities and challenges of using big data for research in travel behaviour and sustainable transport. We welcome papers on the following topics (but not limited to):

  • Analysing travel behaviour using big data;
  • Evaluating the effects of big infrastructure investments or transport policies on travel behaviour using big data;
  • Understanding challenges related to privacy, data quality or accessibility, and the implications for research;
  • Empirical studies that establish or enhance the representativeness of big data;
  • Exploring the impacts of the coronavirus pandemic on sustainable transport modes using big data;
  • Future perspectives on the use of big data in transport research.

Dr. Jinhyun Hong
Dr. David Philip McArthur
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big data
  • Travel behaviour
  • Sustainable transport
  • Big data analytics
  • Bias correction

Published Papers (2 papers)

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Research

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Article
Assignment of Freight Truck Shipment on the U.S. Highway Network
Sustainability 2021, 13(11), 6369; https://doi.org/10.3390/su13116369 - 03 Jun 2021
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Abstract
With the ever-increasing demand for freight movements, nationwide freight shipments between geographical regions by freight trucks need to be investigated since they comprise the largest share of total freight movements in the United States. To this end, the procedures for freight truck shipment [...] Read more.
With the ever-increasing demand for freight movements, nationwide freight shipments between geographical regions by freight trucks need to be investigated since they comprise the largest share of total freight movements in the United States. To this end, the procedures for freight truck shipment demand network assignment on the entire U.S. highway network considering congestion effect are discussed, and the results are explained in detail, with visual illustrations. A fundamental traffic assignment model with a convex combinations algorithm is proposed to solve the nationwide freight truck shipment assignment problem under the user equilibrium principle. A link cost function is modified, considering the traffic volume that already exists on U.S. highways. A case study is conducted using big data including the entire U.S. highway network and freight shipment information in 2007. Total and average freight shipment costs for both truck and rail transportation for a specific origin–destination pair in the database are computed to compare the characteristics of these two major freight transportation modes in the United States. Application of the proposed model could be possible to address many other related problems, such as improvement of highway infrastructure, and reductions in traffic congestion and vehicle emissions. Full article
(This article belongs to the Special Issue Big Data, Travel Behaviour and Sustainable Transportation)
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Review

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Review
Sources and Applications of Emerging Active Travel Data: A Review of the Literature
Sustainability 2021, 13(13), 7006; https://doi.org/10.3390/su13137006 - 22 Jun 2021
Viewed by 730
Abstract
Active travel (AT) has the potential to integrate with, or in some cases substitute for, trips taken by motorized transportation. In this paper we review relevant research on AT outcomes to address the potential of AT and emerging data sources in supporting the [...] Read more.
Active travel (AT) has the potential to integrate with, or in some cases substitute for, trips taken by motorized transportation. In this paper we review relevant research on AT outcomes to address the potential of AT and emerging data sources in supporting the transport paradigm shift toward AT. Our analysis identifies physical, mental, built and physical environmental, monetary, and societal outcomes. Traditional methods used to acquire AT data can be divided into manual methods that require substantial user input and automated methods that can be employed for a lengthier period and are more resilient to inclement weather. Due to the proliferation of information and communication technology, emerging data sources are prevailing and can be grouped into social fitness networks, in-house developed apps, participatory mapping, imagery, bike sharing systems, social media, and other types. We assess the emerging data sources in terms of their applications and potential limitations. Furthermore, we identify developing policies and interventions, the potential of imagery, focusing on non-cycling modes and addressing data biases. Finally, we discuss the challenges of data ownership within emerging AT data and the corresponding directions for future work. Full article
(This article belongs to the Special Issue Big Data, Travel Behaviour and Sustainable Transportation)
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