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Applications of Artificial Intelligence Based Methods in Transportation Engineering

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 12093

Special Issue Editors


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Guest Editor
Faculty of Engineering, The University of Auckland, Auckland 1010, New Zealand
Interests: intelligent transportation systems; modelling and simulation of transport facilities; traffic safety; resilience of transport system; traffic operations and management; applications of emerging technologies in transportation engineering

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Guest Editor
School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom
Interests: Artificial Intelligence; cognitive systems; intelligent transportation systems; machine learning applications
Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom
School of Highway, Chang’an University, Nanerhuan Rd, Xi`an, Shaanxi, China
Interests: motorway traffic control and management; modelling and controlling mixed automated/manual vehicles; short-term traffic prediction

Special Issue Information

Dear Colleagues,

As we all know, transportation is an inevitable part of our daily life. A properly planned, designed, operated, and managed transportation system widens opportunities to satisfy the demand for the movement of people and goods in a safe, economical, convenient, and sustainable manner. With urbanization and economic growth, transportation systems are facing the challenge to cope with the imbalanced increase in travel demand. Consequently, many cities around the world are facing congestion, accidents, and environmental problems, which negatively influences their economic and social development.

Emerging technologies such as connected and autonomous vehicles, electric vehicles, and information and communication technologies offer promising solutions to the existing transportation problems. Artificial Intelligence (AI) and Machine Learning are beginning to play an important role in the development of these technologies.

This Special Issue seeks to provide researchers and professionals with high-quality research and review articles describing the latest advances in the design and application of AI or machine learning techniques in transportation engineering. These articles will not only describe current state-of-the-art research but also highlight the emerging trends and the open problems in this field.

Prospective authors are invited to submit original research and review articles related to the design and use of AI or machine learning techniques to improve sustainability of transportation systems, including the planning, design, operation and management of transportation facilities. The potential topics of interest include but are not limited to the following:

  • Big-data analytics in transportation
  • Connected and autonomous vehicles
  • Electric vehicles
  • Environmental assessment
  • Human and machine interface
  • Intelligent transportation systems
  • Modelling and simulation of transport facilities
  • Smart cities
  • Traffic studies and data processing
  • Traffic operations and management

Dr. Prakash Ranjitkar
Dr. Mohan Sridharan
Dr. Duo 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. 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 2400 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 analytics in transportation
  • Connected and autonomous vehicles
  • Electric vehicles
  • Environmental assessment
  • Human and machine interface
  • Intelligent transportation systems
  • Modelling and simulation of transport facilities
  • Smart cities
  • Traffic studies and data processing
  • Traffic operations and management

Published Papers (1 paper)

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Research

21 pages, 6218 KiB  
Article
Artificial Intelligence-Enabled Traffic Monitoring System
by Vishal Mandal, Abdul Rashid Mussah, Peng Jin and Yaw Adu-Gyamfi
Sustainability 2020, 12(21), 9177; https://doi.org/10.3390/su12219177 - 4 Nov 2020
Cited by 34 | Viewed by 11178
Abstract
Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which [...] Read more.
Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow. Full article
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