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Designing and Managing the Next Generation of Transportation Infrastructure

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 3102

Special Issue Editors


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Guest Editor
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

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Guest Editor
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 Infrastructures.

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. 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

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

Published Papers (1 paper)

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Research

19 pages, 3731 KiB  
Article
Optimizing Moving Object Trajectories from Roadside Lidar Data by Joint Detection and Tracking
by Jiaxing Zhang, Wen Xiao and Jon P. Mills
Remote Sens. 2022, 14(9), 2124; https://doi.org/10.3390/rs14092124 - 28 Apr 2022
Cited by 8 | Viewed by 2515
Abstract
High-resolution traffic data, comprising trajectories of individual road users, are of great importance to the development of Intelligent Transportation Systems (ITS), in which they can be used for traffic microsimulations and applications such as connected vehicles. Roadside laser scanning systems are increasingly being [...] Read more.
High-resolution traffic data, comprising trajectories of individual road users, are of great importance to the development of Intelligent Transportation Systems (ITS), in which they can be used for traffic microsimulations and applications such as connected vehicles. Roadside laser scanning systems are increasingly being used for tracking on-road objects, for which tracking-by-detection is the widely acknowledged method; however, this method is sensitive to misdetections, resulting in shortened and discontinuous object trajectories. To address this, a Joint Detection And Tracking (JDAT) scheme, which runs detection and tracking in parallel, is proposed to mitigate miss-detections at the vehicle detection stage. Road users are first separated by moving point semantic segmentation and then instance clustering. Afterwards, two procedures, object detection and object tracking, are conducted in parallel. In object detection, PointVoxel-RCNN (PV-RCNN) is employed to detect vehicles and pedestrians from the extracted moving points. In object tracking, a tracker utilizing the Unscented Kalman Filter (UKF) and Joint Probabilistic Data Association Filter (JPDAF) is used to obtain the trajectories of all moving objects. The identities of the trajectories are determined from the results of object detection by using only a certain number of representatives for each trajectory. The developed scheme has been validated at three urban study sites using two different lidar sensors. Compared with a tracking-by-detection method, the average range of object trajectories has been increased by >20%. The approach can also successfully maintain continuity of the trajectories by bridging gaps caused by miss-detections. Full article
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