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Sustainability
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Research on Sustainable Transportation and Urban Traffic—3rd Edition
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Research on Sustainable Transportation and Urban Traffic—3rd Edition

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2838

Special Issue Editors

Dr. Vincenzo Gallelli

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Calabria, Arcavacata Campus, 87036 Rende, Italy
Interests: transportation modeling; sustainable mobility; transportation planning; ITS mobility management; traffic simulation; road pavement surface performances; sustainable road materials; road safety; traffic microsimulation; surrogate safety indicators; road geometric design and performance analysis of roundabouts
Special Issues, Collections and Topics in MDPI journals
Dr. Rosolino Vaiana

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Calabria, Arcavacata Campus, 87036 Rende, Italy
Interests: road pavement surface performances; bituminous materials; recycled and sustainable road materials; sustainable mobility; road safety and driver behavior; traffic microsimulation; geometric design and performance analysis of roundabouts; surrogate safety indicators
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The role of transport in sustainable development is fundamental to enhance economic growth and improve accessibility. Sustainable transport achieves better integration of the economy while respecting the environment and improving social equity, health, and resilience of cities. There is an urgent need for transformative action that will accelerate the transition to sustainable transport, especially in urban areas. The increasing evident effects of climate change are driving us to find innovative transport solutions, again especially in urban areas. This difficult situation has amplified the centrality of transport in sustainable development, emphasizing existing challenges and creating new ones. Scientific advances and the rapid development of new technologies are essential for the transition to sustainable transport: environmentally friendly fuels and engines, artificial intelligence technology, big data analysis, autonomous vehicles, and intelligent transport systems have become central features of the transport innovation landscape.

Therefore, the aim of this Special Issue is to focus attention on the new challenges around sustainable transportation and urban traffic, focusing on how to use intelligent transport systems, big data analysis technology, and IoT applications to improve transportation systems, especially in urban areas.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • IoT sensing, applications, and technologies for smart sustainable cities;
  • Use of new devices to evaluate traffic congestion, traffic emission, and improving traffic sustainability.
  • Intelligent transport systems for urban smart mobility;
  • New forms of innovative public transport;
  • New traffic-calming systems to improve the safety of vulnerable road users in urban areas;
  • Pedestrians and cyclists’ influence on traffic flow parameters and road safety;
  • Infrastructure-based sensor networks for urban road/traffic monitoring;
  • Big data applications in sustainable transport and transportation planning;
  • Artificial intelligence systems that can assist traffic control network managers in planning, monitoring, and management tasks;
  • Systems to improve the vulnerability level of urban intersections.

We look forward to receiving your contributions.

Dr. Vincenzo Gallelli
Dr. Rosolino Vaiana
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 250 words) can be sent to the Editorial Office for assessment.

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 road transport
  • traffic flow modeling
  • smart cities
  • smart roads
  • innovative public transport solutions
  • transportation planning
  • intelligent transportation systems
  • fuel consumption and emissions
  • safety of vulnerable road users
  • urban intersections

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Related Special Issue

Published Papers (3 papers)

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Research

30 pages, 5358 KB  
Article
Peak Shaving and Solar Utilization for Sustainable Campus EV Charging Using Reinforcement Learning Approach
by Heba M. Abdullah, Adel Gastli, Lazhar Ben-Brahim and Shirazul Islam
Sustainability 2026, 18(6), 2737; https://doi.org/10.3390/su18062737 - 11 Mar 2026
Viewed by 490
Abstract
To reduce the carbon footprint, electric vehicles (EVs) are considered an alternative transportation choice. However, increased use of EVs could lead to overloading the existing power network when accounting for all installed chargers. With the increasing deployment of EV chargers, universities are potential [...] Read more.
To reduce the carbon footprint, electric vehicles (EVs) are considered an alternative transportation choice. However, increased use of EVs could lead to overloading the existing power network when accounting for all installed chargers. With the increasing deployment of EV chargers, universities are potential locations for the oversized power network issue. This paper applies reinforcement learning (RL) to optimize for EV charging infrastructure at the university scale using real-world data, directly contributing to sustainable energy management by reducing grid burden and increasing renewable energy utilization. The RL-based charger aims to reduce the burden on the grid while increasing renewable energy utilization. This study investigated practical relevance in real-world systems, considering three demand scenarios: random, stochastic historical demand from Qatar University, and actual online data from Caltech University. Three RL algorithms—Deep Q-Network (DQN), Advantage Actor–Critic (A2C), and Proximal Policy Optimization (PPO)—are applied. While training, the historical stochastic data requires more tuning of the RL framework than the random demand, emphasizing the importance of realistic demand profiles. The performance of the RL approach depends on the type of demand. The results show that the proposed RL approach can efficiently mitigate the peak charging currents. For the Qatar University historical demand scenario, the PPO algorithm minimized the peak charging currents by 50% relative to uncontrolled charging (160 A to 80 A) and Model Predictive Control maintained the energy transfer capability at 99.710%. For the random demand type, the peak charging currents are minimized by 38.3% as compared to uncontrolled charging (128 A to 79 A), with a nominal reduction in energy transfer capability to 95.89%. Scalability is tested by integrating the model into the IEEE-33 bus network. Without solar integration, the proposed RL-based EV charging management model improves the voltage drop by 0.05 p.u., leading to reduction in the line losses by 17% as compared to the MPC benchmark method and by 32% as compared to the uncontrolled charging scheme. Further, the proposed RL approach leads to a 9% reduction in line current during peak hours in the IEEE-33 bus system. With solar integration into the IEEE-bus system, the proposed framework of the RL approach improved the sustainability of the charging infrastructures by enhancing solar energy utilization by 42.5%. These findings validate the applicability of the proposed model used for optimizing the sustainable EV charging infrastructure while managing the charging coordination problem. Full article
(This article belongs to the Special Issue Research on Sustainable Transportation and Urban Traffic—3rd Edition)
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26 pages, 2846 KB  
Article
Electric Minibus Taxis in Cape Town: Energy Demand, Emissions, and Costs
by Joshua Tokollo Sello, Mienke Knipe, Maria Elizabeth Marais, Salma Abdelgadir, Christo Venter and Marthinus Johannes Booysen
Sustainability 2026, 18(4), 2122; https://doi.org/10.3390/su18042122 - 21 Feb 2026
Cited by 2 | Viewed by 1092
Abstract
Minibus taxis are Cape Town’s ubiquitous public transport mode, carrying about 69% of public transport users. As electric mobility accelerates, the implications of electrifying this paratransit fleet must be quantified. We present a multi-perspective assessment of energy, environmental and operator impacts of electric [...] Read more.
Minibus taxis are Cape Town’s ubiquitous public transport mode, carrying about 69% of public transport users. As electric mobility accelerates, the implications of electrifying this paratransit fleet must be quantified. We present a multi-perspective assessment of energy, environmental and operator impacts of electric minibus taxis (eMBTs). Using a high-resolution tracking dataset representative of MBT operations in Cape Town, South Africa, we estimate daily charging energy demand, compare greenhouse gas and particulate matter emissions for electric and internal combustion operation under South Africa’s coal-dominated grid, and evaluate the operator’s total cost of ownership. Key results show that eMBTs require approximately 50.8 kWh of energy per day, based on the fleet’s median daily distance. Under current grid conditions, diesel minibus taxis emit 14.37% less CO2e than eMBTs, but eMBTs drastically reduce noise pollution and particulate matter emissions when compared to diesel vehicles. Despite higher purchase prices, eMBTs can reduce operating costs and become financially attractive under favourable electricity prices and financing conditions. The study provides evidence to guide charging infrastructure planning, grid policy and incentive design for paratransit electrification in developing regions. Full article
(This article belongs to the Special Issue Research on Sustainable Transportation and Urban Traffic—3rd Edition)
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20 pages, 1905 KB  
Article
Feasibility Study of School-Centred Peer-to-Peer Energy Trading with Households and Electric Motorbike Loads
by Lerato Paulina Molise, Jason Avron Samuels and Marthinus Johannes Booysen
Sustainability 2026, 18(2), 978; https://doi.org/10.3390/su18020978 - 18 Jan 2026
Viewed by 623
Abstract
South Africa faces high energy costs, highlighting the urgent need for sustainable and cost-effective energy solutions. This study investigates the design of a cost-effective photovoltaic energy system that maximises savings and revenue for the school through energy trading. In this study, the school [...] Read more.
South Africa faces high energy costs, highlighting the urgent need for sustainable and cost-effective energy solutions. This study investigates the design of a cost-effective photovoltaic energy system that maximises savings and revenue for the school through energy trading. In this study, the school trades with 14 neighbouring households and 125 electric motorbikes. This research first applies Latin Hypercube Sampling to explore the solution space and determine which system parameters have a significant impact on supply reliability, investment costs, revenue and savings. Optimal solutions are generated using Non-Dominated Sorting Genetic Algorithm II for a range of system scenarios. Following this, the most promising scenario is selected and applied to 53 schools in the Western Cape. The results show that number of panels strongly correlates with both supply reliability and revenue, thus reducing the break-even years, while battery capacity affects investment costs and, to some extent, break-even years. Among the configurations tested, scenarios where schools traded with both households and electric motorbikes, particularly when both included their own battery systems, achieved the most favourable financial performance for the school, with break-even periods of less than five years under sufficient roof area and improved reliability for the external entities, with an average improvement of 60%. These findings demonstrate that peer-to-peer energy trading between schools and communities can enhance the financial feasibility and sustainability of decentralised solar systems, offering a scalable model for improving energy access and affordability in South Africa and possibly other developing countries. Full article
(This article belongs to the Special Issue Research on Sustainable Transportation and Urban Traffic—3rd Edition)
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