remotesensing-logo

Journal Browser

Journal Browser

Harnessing the Geospatial Data Revolution for Promoting Sustainable Transport Systems

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 1839

Special Issue Editors

Transport Studies Unit, University of Oxford, Oxford OX1 3QY, UK
Interests: GIS; spatial data science; transport geography; mobility analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A sustainable transport system plays a vital role in a successful society, the construction and operation of which require the support of considerable amounts of transport data from a variety of sources to inspect road asset conditions; track traffic flow dynamics; monitor transport emergencies; analyse traffic safety, equality, and accessibility, etc. With the advancement of new and scalable data sources, robust acquisition methodologies, and transmission techniques, unprecedented amounts of traffic information are being generated and collected from various data sources, such as roadside sensors, very-high-resolution (VHR)/HR satellite imagery, streetscape observations, sensor-rich mobile devices, and connected and autonomous vehicles (CAVs). Compared with conventional data sources, these emerging geospatial big datasets are massive in size, spatiotemporally fine-scaled, and high-dimensional (e.g., multivariate and multivalued), providing researchers with rich and timely information to effectively manage transport systems and gain new insights into different transport challenges (e.g., crashes, congestion, emissions, mobility inequality). However, managing and analysing these big complex datasets expose new problems and challenges in terms of strategies to promote multiple aspects, including (1) data integration, enrichment, storage, archiving, and sharing; (2) data quality control (e.g., reducing data uncertainty and redundancy); (3) data security, integrity, and privacy; and (4) data processing, analysis, and visualization.

This Special Issue invites the submission of original research papers and review articles that showcase the latest developments, innovations, and applications of emerging transport data in sustainable transport management and operations. Topics of interest include but are not limited to:

  • Application of emerging geospatial data in road asset recognition, digitization, and inventorization;
  • Application of emerging geospatial data in transport equity and accessibility, including environmental policy assessments;
  • Application of new geo-visualization methods and platforms for exploring big transport data;
  • Strategies for encouraging transport data sharing and protecting data privacy and security;
  • Multi-source data fusion challenges and considerations in transport applications;
  • Examining the role of geospatial big data in enhancing resilience and emergency responses for transport systems in the face of natural disasters, pandemics, and other crises.

You may choose our Joint Special Issue in ISPRS International Journal of Geo-Information.

Dr. Xiao Li
Dr. Xiao Huang
Dr. Zhenlong Li
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 submissions that pass pre-check are 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. Remote Sensing 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 2700 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

  • sustainable transport system
  • emerging transport data
  • big data analytics
  • novel sensing technologies
  • road asset recognition and digitization
  • transport monitoring and assessment

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

17 pages, 2403 KiB  
Article
Estimating Pavement Condition by Leveraging Crowdsourced Data
by Yangsong Gu, Mohammad Khojastehpour, Xiaoyang Jia and Lee D. Han
Remote Sens. 2024, 16(12), 2237; https://doi.org/10.3390/rs16122237 - 20 Jun 2024
Viewed by 694
Abstract
Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay [...] Read more.
Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay road maintenance. By contrast, crowdsourced data, in a manner of crowdsensing, can provide real-time and valuable roadway information for extensive coverage. This study exploited crowdsourced Waze pothole and weather reports for pavement condition evaluation. Two surrogate measures are proposed, namely, the Pothole Report Density (PRD) and the Weather Report Density (WRD). They are compared with the Pavement Quality Index (PQI), which is calculated using laser truck data from the Tennessee Department of Transportation (TDOT). A geographically weighted random forest (GWRF) model was developed to capture the complicated relationships between the proposed measures and PQI. The results show that the PRD is highly correlated with the PQI, and the correlation also varies across the routes. It is also found to be the second most important factor (i.e., followed by pavement age) affecting the PQI values. Although Waze weather reports contribute to PQI values, their impact is significantly smaller compared to that of pothole reports. This paper demonstrates that surrogate pavement condition measures aggregated by crowdsourced data could be integrated into the state decision-making process by establishing nuanced relationships between the surrogated performance measures and the state pavement condition indices. The endeavor of this study also has the potential to enhance the granularity of pavement condition evaluation. Full article
Show Figures

Figure 1

Back to TopTop