sustainability-logo

Journal Browser

Journal Browser

Current Movement in Sustainable Urban Mobility

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 3778

Special Issue Editors


E-Mail Website
Guest Editor
Qatar Transportation and Traffic Safety Center, Department of Civil and Architectural Engineering, College of Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
Interests: traffic simulation; human behavior modeling; traffic engineering; pedestrian safety; traffic safety; micromobility; crowd dynamics

E-Mail Website
Guest Editor
Urban and Transport Planning Laboratory, School of Systems Engineering, Kochi University of Technology, Kochi 782-8502, Japan
Interests: traffic engineering; public transport planning and management; traffic accident; traffic safety; personal mobility vehicles

Special Issue Information

Dear Colleagues,

Traffic congestion, safety of vulnerable road users, and greenhouse gas emissions have become serious concerns in the urban centers of populated cities. Individual use of cars, particularly for short-distance trips, may largely contribute to such issues. Sustainable modes of transport are being encouraged and promoted in many cities around the world as a strategy to mitigate the negative effects of personal automobile use.

In general, any form of eco-friendly or green transportation is referred to as sustainable transportation. Sustainable means of transportation can offer numerous social, economic, and environmental benefits, e.g., alleviating traffic congestion by reducing the use of single-occupancy vehicles, enhancing safety, reducing greenhouse gas emissions, encouraging active lifestyles, improving health and social connections in urban environments, and so on.

This Special Issue aims to discuss and advance the body of knowledge related to the current trends in sustainable urban mobility. In particular, the planning, designing, operational, and policymaking aspects of sustainable urban mobility will be discussed. Submissions related to any type of sustainable transportation mode, e.g., public transport, shared transport, active mobility, micromobility, electric mobility, etc., will be considered.

This Special Issue welcomes original research articles or review articles on the following specific research themes:

  • Modeling and simulation of sustainable urban transportation systems
  • Role of autonomous and connected vehicles in sustainable urban mobility
  • ITS (intelligent transport systems) aspects of sustainable urban mobility
  • Artificial intelligence and machine learning approaches in sustainable urban transportation systems
  • Big data analytics and data science in sustainable urban mobility
  • Novel and advanced data collection and analysis methods related to sustainable urban mobility
  • Survey methods and related statistical analyses on user perceptions, preferences, and behaviors
  • Safety aspects of sustainable urban mobility
  • Optimization techniques in sustainable urban transportation systems
  • Life-cycle assessment in the context of sustainable urban mobility
  • Environmental impact assessment in the context of sustainable urban mobility

Dr. Charitha Dias
Dr. Hiroaki Nishiuchi
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

  • sustainable urban mobility
  • sustainable transport
  • public transport
  • walking
  • cycling
  • micromobility
  • e-scooters
  • shared mobility
  • carpooling
  • ride sharing
  • intelligent transport systems (ITS)
  • mobility as a service (MaaS)
  • electric vehicles
  • traffic safety management
  • traffic accident analysis
  • greenhouse gas emissions in transport

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

26 pages, 6468 KiB  
Article
Using Deep Learning and Google Street View Imagery to Assess and Improve Cyclist Safety in London
by Luís Rita, Miguel Peliteiro, Tudor-Codrin Bostan, Tiago Tamagusko and Adelino Ferreira
Sustainability 2023, 15(13), 10270; https://doi.org/10.3390/su151310270 - 28 Jun 2023
Cited by 3 | Viewed by 3364
Abstract
Cycling is a sustainable mode of transportation with significant benefits for society. The number of cyclists on the streets depends heavily on their perception of safety, which makes it essential to establish a common metric for determining and comparing risk factors related to [...] Read more.
Cycling is a sustainable mode of transportation with significant benefits for society. The number of cyclists on the streets depends heavily on their perception of safety, which makes it essential to establish a common metric for determining and comparing risk factors related to road safety. This research addresses the identification of cyclists’ risk factors using deep learning techniques applied to a Google Street View (GSV) imagery dataset. The research utilizes a case study approach, focusing on London, and applies object detection and image segmentation models to extract cyclists’ risk factors from GSV images. Two state-of-the-art tools, You Only Look Once version 5 (YOLOv5) and the pyramid scene parsing network (PSPNet101), were used for object detection and image segmentation. This study analyzes the results and discusses the technology’s limitations and potential for improvements in assessing cyclist safety. Approximately 2 million objects were identified, and 250 billion pixels were labeled in the 500,000 images available in the dataset. On average, 108 images were analyzed per Lower Layer Super Output Area (LSOA) in London. The distribution of risk factors, including high vehicle speed, tram/train rails, truck circulation, parked cars and the presence of pedestrians, was identified at the LSOA level using YOLOv5. Statistically significant negative correlations were found between cars and buses, cars and cyclists, and cars and people. In contrast, positive correlations were observed between people and buses and between people and bicycles. Using PSPNet101, building (19%), sky (15%) and road (15%) pixels were the most common. The findings of this research have the potential to contribute to a better understanding of risk factors for cyclists in urban environments and provide insights for creating safer cities for cyclists by applying deep learning techniques. Full article
(This article belongs to the Special Issue Current Movement in Sustainable Urban Mobility)
Show Figures

Figure 1

Back to TopTop