Special Issue "Designing and Managing the Next Generation of Transportation Infrastructure"

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 2113

Special Issue Editors

Faculty of Applied Science, University of British Columbia, Vancouver, BC, Canada
Interests: transportation engineering; highway design; traffic safety; infrastructure management; LiDAR
Special Issues, Collections and Topics in MDPI journals
University of Alberta, Edmonton, AB, Canada
Interests: lidar; GIS; highways; traffic signs; cross sections
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transportation agencies around the world are presented with a set of challenges including climate change and natural disaster, ageing transportation infrastructure, and anticipated changes in forms of mobility. Managing and overcoming those challenges lies in the ability to:

  • develop tools that facilitate efficient large-scale assessment and the proactive management of existing infrastructure
  • use information about existing infrastructure to propose innovations to roadway design provisions that would ensure that future infrastructure is capable of handling the anticipated changes.

The use of remote sensing data and GIS tools including mobile LiDAR data, photogrammetry is critical to achieving the aforementioned objectives. These tools have made large volumes of data readily available to transportation agencies and municipalities, nonetheless, efficient processing and semantic segmentation of critical information from those datasets and the ability to relate the extracted information to other components of the transportation system is extremely challenging.

To promote more resilient transportation infrastructure, this special issue invites papers that propose novel methods and approaches that help utilize remote sensing and GIS data to promote efficient management and improved design of transportation infrastructure. Potential areas of interest for this call for papers include

  • Semantic segmentation tools for the extraction roadside assets and the assessment of roadway design elements and from remote sensing data.
  • Decision support tools that use remote sensing data to improve the efficiency of managing and accessing conditions of existing transportation infrastructure.
  • Investigations focused on identification and mapping of sections of the transportation infrastructure that are most vulnerable to natural disaster such as flooding and wildfires.
  • Models developed to help understand the relationships between critical design elements of roadway infrastructure, road user behaviour, road user demographics, traffic, and roadway safety.

You may choose our Joint Special Issue in Remote Sensing.

Dr. Suliman Gargoum
Mr. Lloyd Karsten
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. Infrastructures is an international peer-reviewed open access monthly 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 1600 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.


  • Infrastructure Resiliency
  • Remote Sensing
  • Traffic Safety
  • Semantic Segmentation
  • LiDAR Technology
  • Roadway Design

Published Papers (1 paper)

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Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis
Infrastructures 2022, 7(1), 7; https://doi.org/10.3390/infrastructures7010007 - 04 Jan 2022
Cited by 3 | Viewed by 1729
Fatalities and serious injuries still represent a significant portion of run-off-the-road (ROR) collisions on highways in North America. In order to address this issue and design safer and more forgiving roadside areas, more empirical evidence is required to understand the association between roadside [...] Read more.
Fatalities and serious injuries still represent a significant portion of run-off-the-road (ROR) collisions on highways in North America. In order to address this issue and design safer and more forgiving roadside areas, more empirical evidence is required to understand the association between roadside elements and safety. The inability to gather that evidence has been attributed in many cases to limitations in data collection and data fusion capabilities. To help overcome such issues, this paper proposes using LiDAR datasets to extract the information required to analyze factors contributing to the severity of ROR collisions on a localized collision level. Specifically, the paper proposes a new method for extracting pole-like objects and tree canopies. Information about other roadside assets, including signposts, alignment attributes, and side slopes is also extracted from the LiDAR scans in a fully automated manner. The extracted information is then attached to individual collisions to perform a localized assessment. Logistic regression is then used to explore links between the extracted features and the severity of fixed-object collisions. The analysis is conducted on 80 km of roads from 10 different highways in Alberta, Canada. The results show that roadside attributes vary significantly for the different collisions along the 80 km analyzed, indicating the importance of utilizing LiDAR to extract such features on a disaggregate collision level. The regression results show that the steepness of side slopes and the offset of roadside objects had the most significant impacts on the severity of fixed-object collisions. Full article
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