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Sustainable and Intelligent Transportation Systems in the Era of the COVID-19 Pandemic

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 2809

Special Issue Editors

Prof. Dr. Camille Kamga

E-Mail Website
Guest Editor
Department of Civil Engineering, The City College of New York, New York, NY 10031, USA
Interests: intelligent transportation systems; traffic incident management; transportation operations, management and organizations; transportation policy and planning; transportation safety; urban sustainability
Dr. Lei Zhu

E-Mail Website
Guest Editor
Department of Systems Engineering and Engineering Management, University of North Carolina at Charlotte, Charlotte, NC, USA
Interests: smart and sustainable mobility systems; spatial sensing technologies; human mobility modeling and Wi-Fi data processing; emerging mobility modeling and simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Sandeep Mudigonda

E-Mail Website
Guest Editor
University Transportation Research Center, The City College of New York, New York, NY 10031, USA
Interests: sustainable transportation; connected and automated vehicles; transportation data analytics

Special Issue Information

Dear Colleagues,

ITS aims to enhance the efficiency, safety and environmental impact of transportation systems. ITS spans a wide spectrum of technologies, including those that sense, process, communicate and/or control aspects of transportation. Recent innovations in computing power, sensor and communication technologies, and development in machine learning and artificial intelligence hves enabled rapid deployment in transportation applications.

The COVID-19 pandemic has provided an immense disruption to the usage and operation of the transportation system throughout the world. With the measures deployed by the government to reduce the spread of the virus, we have noticed a drastic reduction in travel demand, disruption in the ssupply chain, and an increase in the use of telecommunications. The pandemic has revealed numerous fault lines in urban mobility, and it has also offered an opportunity to reshape transportation systems, eliminate points of friction and strongly focus urban and rural mobility services on the human aspect. Compounded with new disruptions to transportation operations such as mobility-as-a-service, multimodal service integrations and electrification of modes, ITS has provided ways to enhance not only operational efficiency but also aspects of sustainable transportation such as accessibility, equitable services and environmental impact. It is clear that there is a need to be prepared for a wider variety of potential disruptions in the future and to understand its different potential effects on the transportation system.

This Special Issue invites scholarly work that has studied ITS to explore impacts or deployments to enhance aspects of sustainability in transportation. Topics include (but are not limited to) analyses using automated, cooperative and connected mobility; smart city technologies; new forms of mobility; intelligent infrastructure; supply chain; new services from new technologies and solutions for cities and rural areas, to measure, locate, analyze or optimize transportation services from a sustainability standpoint (greenhouse gas emissions, energy consumption, etc.); Internet of Things (IoT); electric vehicles; and technological architectures for sustainable transportation technologies. Applications can also include more bleeding-edge technologies such as automated mobility and blockchain.

Prof. Dr. Camille Kamga
Prof. Dr. Lei Zhu
Dr. Sandeep Mudigonda
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

  • intelligent transportation systems
  • connected mobility
  • automated mobility
  • electric vehicles
  • greenhouse gas emissions
  • energy consumption
  • internet of things
  • big data
  • sharing mobility
  • supply chain technologies

Published Papers (1 paper)

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Research

16 pages, 3640 KiB  
Article
Data-Driven Graph Filter-Based Graph Convolutional Neural Network Approach for Network-Level Multi-Step Traffic Prediction
by Lei Lin, Weizi Li and Lei Zhu
Sustainability 2022, 14(24), 16701; https://0-doi-org.brum.beds.ac.uk/10.3390/su142416701 - 13 Dec 2022
Cited by 2 | Viewed by 1795
Abstract
Accurately predicting network-level traffic conditions has been identified as a critical need for smart and advanced transportation services. In recent decades, machine learning and artificial intelligence have been widely applied for traffic state, including traffic volume prediction. This paper proposes a novel deep [...] Read more.
Accurately predicting network-level traffic conditions has been identified as a critical need for smart and advanced transportation services. In recent decades, machine learning and artificial intelligence have been widely applied for traffic state, including traffic volume prediction. This paper proposes a novel deep learning model, Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF), for network-wide multi-step traffic volume prediction. More specifically, the proposed GCNN-DDGF model can automatically capture hidden spatiotemporal correlations between traffic detectors, and its sequence-to-sequence recurrent neural network architecture is able to further utilize temporal dependency from historical traffic flow data for multi-step prediction. The proposed model was tested in a network-wide hourly traffic volume dataset between 1 January 2018 and 30 June 2019 from 150 sensors in the Los Angeles area. Detailed experimental results illustrate that the proposed model outperforms the other five widely used deep learning and machine learning models in terms of computational efficiency and prediction accuracy. For instance, the GCNN-DDGF model improves MAE, MAPE, and RMSE by 25.33%, 20.45%, and 29.20% compared to the state-of-the-art models, such as Diffusion Convolution Recurrent Neural Network (DCRNN), which is widely accepted as a popular and effective deep learning model. Full article
(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems in the Era of the COVID-19 Pandemic)
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