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Towards Sustainable Intelligent Transport Systems: Methods, Modelling, and Insights

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

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 8249

Special Issue Editors


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Guest Editor
Institute of Transport and Logistics Studies, University of Sydney, Darlington, Australia
Interests: transport planning; intelligent transport systems; sustainability in transport; network modelling; traffic simulation; machine learning; traffic flow theory; multi-modal transport systems; multi-scale transport models

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Guest Editor
School of Civil and Environmental, Faculty of Engineering, Queensland University of Technology, Brisbane, Australia
Interests: traffic flow theory; connected and automated vehicles; intelligent transport systems; traffic simulation; human factors; statistical and econometric modelling; machine learning

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Guest Editor
C.V. Raman Postdoctoral Research Fellow, Center for infrastructure, Sustainable Transportation & Urban Planning (CiSTUP), Indian Institute of Science (IISc) Bangalore, Karnataka, India
Interests: traffic flow theory; intelligent transport systems; microscopic modelling and simulation; human factors in traffic

Special Issue Information

Dear Colleagues,

Intelligent transport systems (ITS) originally emerged as an answer to maximise the efficiency of existing road/rail transport infrastructure and services. This provides an alternative to creating new, typically more costly, infrastructure and/or services. However, ITS also has the potential to make our current and future transport systems more sustainable. In the best case, the sustainability aspect of ITS is considered as a nice-to-have by-product in both research and practice. However, frequently the aspect of sustainability is hardly considered at all. This is unfortunate since there are a plethora of ways ITS can help governments/project developers reach increasingly stringent sustainability goals.

In this special issue, we focus on pointed research, ideally focusing on one particular finding or aspect, that highlights potential or realised synergies between ITS and sustainability. This synergy can take on many forms, but we particularly welcome papers that explicitly consider sustainability aspect in ITS in the context of:

  • Traffic management
  • Mobility management and Mobility as a service (MAAS) in particular
  • Impact of electrification, in both private and public transport fleets
  • Impact of connected and/or autonomous vehicles, in both private and public transport
  • Inclusion of non-motorised/active modes
  • Human factors in ITS for sustainable transportation
  • ITS for traffic safety and sustainability
  • Insights and future directions on ITS and sustainability (review papers)

Some suggested sustainability aspects that would be welcomed as topics of analysis are:

  • Direct/Indirect emissions, and or pollution
  • Inclusion/promotion of active modes, non-motorised modes
  • Types of propulsion (combustion, electric, fuel cell, etc.)
  • Private vs public vs shared modes, or multi-modal perspectives and impacts

We especially encourage concise submissions with a clear and focussed topic that have a methodological or modelling component.

Special issue specific restrictions:

  • Word limit: no more than 8,000 words
    • Excluding: title page, figures, abstract, equations, and appendices
    • Including: Main text, references

Dr. Mark Raadsen
Dr. Yasir Ali
Dr. Anshuman Sharma
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 Transport Systems
  • sustainable public transport
  • transport modelling
  • sustainable MAAS
  • future transport systems

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Published Papers (2 papers)

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Research

20 pages, 4080 KiB  
Article
Travel Time Prediction for Traveler Information System in Heterogeneous Disordered Traffic Conditions Using GPS Trajectories
by Gurmesh Sihag, Manoranjan Parida and Praveen Kumar
Sustainability 2022, 14(16), 10070; https://doi.org/10.3390/su141610070 - 14 Aug 2022
Cited by 10 | Viewed by 2542
Abstract
Precise travel time prediction allows travelers and system controllers to be aware of the future conditions on roadways and helps in pre-trip planning and traffic control strategy formulation to lessen the travel time and mitigate traffic congestion problems. This research investigates the possibility [...] Read more.
Precise travel time prediction allows travelers and system controllers to be aware of the future conditions on roadways and helps in pre-trip planning and traffic control strategy formulation to lessen the travel time and mitigate traffic congestion problems. This research investigates the possibility of using the GPS trajectory dataset for travel time prediction in Indian traffic conditions having heterogeneous disordered traffic and improvement in prediction accuracy by shifting from the traditional historical average method to modern machine learning algorithms such as linear regressions, decision tree, random forest, and gradient boosting regression. The present study uses massive location data consisting of historical trajectories that were collected by installing GPS devices on the probe vehicles. A 3.6 km long stretch of the Delhi–Noida Direct (DND) flyway is selected as a case study to predict the travel time and compare the performance as well as the efficiency of various travel time prediction algorithms. Full article
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22 pages, 4406 KiB  
Article
Towards Sustainable Road Safety in Saudi Arabia: Exploring Traffic Accident Causes Associated with Driving Behavior Using a Bayesian Belief Network
by Muhammad Muhitur Rahman, Md Kamrul Islam, Ammar Al-Shayeb and Md Arifuzzaman
Sustainability 2022, 14(10), 6315; https://doi.org/10.3390/su14106315 - 22 May 2022
Cited by 19 | Viewed by 4760
Abstract
Understanding the causes and effects of road accidents is critical for developing road and action plans in a country. The causation hypothesis elucidates how accidents occur and may be applied to accident analysis to more precisely anticipate, prevent, and manage road safety programs. [...] Read more.
Understanding the causes and effects of road accidents is critical for developing road and action plans in a country. The causation hypothesis elucidates how accidents occur and may be applied to accident analysis to more precisely anticipate, prevent, and manage road safety programs. Driving behavior is a critical factor to consider when determining the causes of traffic accidents. Inappropriate driving behaviors are a set of acts taken on the roadway that can result in aberrant conditions that may result in road accidents. In this study, using Al-Ahsa city in Saudi Arabia’s Eastern Province as a case study, a Bayesian belief network (BBN) model was established by incorporating an expectation–maximization algorithm. The model examines the relationships between indicator variables with a special focus on driving behavior to measure the uncertainty associated with accident outcomes. The BBN was devised to analyze intentional and unintentional driving behaviors that cause different types of accidents and accident severities. The results showed when considering speeding alone, there is a 26% likelihood that collision will occur; this is a 63% increase over the initial estimate. When brake failure was considered in addition to speeding, the likelihood of a collision jumps from 26% to 33%, more than doubling the chance of a collision when compared to the initial value. These findings demonstrated that the BBN model was capable of efficiently investigating the complex linkages between driver behavior and the accident causes that are inherent in road accidents. Full article
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