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AI in Smart Cities and Urban Mobility
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AI in Smart Cities and Urban Mobility

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: 13 February 2027 | Viewed by 895

Special Issue Editor

Dr. Ammar Abulibdeh

E-Mail Website
Guest Editor
Department of Earth and Environmental Sciences, Faculty of Science, University of Waterloo, Waterloo, ON, Canada
Interests: sustainable transportation; smart mobility; transport equity and accessibility; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid urbanization and climate change demand transformative approaches to urban mobility. Artificial Intelligence (AI) is revolutionizing smart cities by enabling data-driven decision-making, optimizing transportation networks, and enhancing sustainability. This research area is critical as cities worldwide grapple with traffic congestion, air pollution, and inequitable access to transportation. At the same time, AI offers unprecedented capabilities to address these challenges through predictive analytics, real-time optimization, and intelligent infrastructure management.

This Special Issue aims to advance the integration of AI technologies in sustainable urban mobility systems, directly aligning with Sustainability's focus on environmental, social, and economic dimensions of sustainable development. We seek cutting-edge research on AI applications that reduce carbon emissions, improve public transit efficiency, promote transport equity, and support the transition to climate-resilient cities, core themes aligned with the journal's commitment to the 2030 Agenda for Sustainable Development.

For this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: AI-driven traffic management and congestion prediction; machine learning for mobility-as-a-service (MaaS) optimization; deep learning applications in autonomous vehicles and shared mobility; AI-powered equity analysis in transportation accessibility; predictive models for electric vehicle charging infrastructure; smart parking systems using computer vision; natural language processing for citizen mobility feedback; reinforcement learning for adaptive signal control; and AI applications in multimodal transport integration.

Contributions to sustainability include: reducing transportation-related greenhouse gas emissions through AI-optimized routing and mode choice; enhancing social equity by identifying and addressing mobility gaps in underserved communities; improving economic efficiency through reduced congestion and optimized resource allocation; supporting sustainable urban planning with AI-generated insights on travel patterns; and enabling real-time environmental monitoring of air quality and noise pollution from transportation systems.

We look forward to receiving your valuable contributions.

Dr. Ammar Abulibdeh
Guest Editor

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

  • artificial intelligence
  • smart cities
  • sustainable mobility
  • machine learning
  • urban transportation
  • transport equity
  • intelligent transportation systems
  • data analytics
  • mobility as a service
  • climate resilience

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Published Papers (2 papers)

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31 pages, 1374 KB  
Article
Sustainable Transportation Decision-Making Enabled by Specialized Large Language Models: A Supervised Fine-Tuning Framework for Route Planning
by Chuqiao Chen, Yifan Wang, Yiming Guo, Haonan Yang, Hengpeng Zhang and Zhiwu Dong
Sustainability 2026, 18(10), 4683; https://doi.org/10.3390/su18104683 - 8 May 2026
Viewed by 267
Abstract
Large language models (LLMs) have shown promise in intelligent transportation systems, but their direct use in constrained route planning remains unreliable because such tasks require exact numerical consistency and strict compliance with operational constraints. This challenge is particularly important in urban freight and [...] Read more.
Large language models (LLMs) have shown promise in intelligent transportation systems, but their direct use in constrained route planning remains unreliable because such tasks require exact numerical consistency and strict compliance with operational constraints. This challenge is particularly important in urban freight and logistics, where routing errors can reduce efficiency and undermine sustainability. To address this issue, this study proposes a supervised fine-tuning (SFT) framework that specializes a general-purpose LLM as an orchestration agent for route planning. Instead of generating routes directly, the model translates natural-language requests into structured function calls that invoke deterministic optimization solvers for the Traveling Salesperson Problem (TSP), Capacitated Vehicle Routing Problem (CVRP), and Vehicle Routing Problem with Time Windows (VRPTW). Experiments on a controlled synthetic benchmark with thousands of routing instances show that direct generation is ineffective for constrained routing, while tool augmentation substantially improves reliability. More importantly, SFT further strengthens function-calling performance, especially on the most challenging VRPTW task, where the overall success rate of the 8B model increases from 0.408 in the zero-shot setting to 0.792 after fine-tuning. The fine-tuned 8B model also outperforms a much larger zero-shot 235B model while requiring far fewer computational resources. These findings indicate that reliable LLM-based transportation decision support is better achieved by combining compact language models with deterministic optimization tools rather than relying on larger models for direct route generation, offering a lightweight and more sustainable path for real-world logistics deployment. Full article
(This article belongs to the Special Issue AI in Smart Cities and Urban Mobility)
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32 pages, 2316 KB  
Article
Energy-Efficient and Maintenance-Aware Control of a Residential Split-Type Air Conditioner Using an Enhanced Deep Q-Network
by Natdanai Kiewwath, Pattaraporn Khuwuthyakorn and Orawit Thinnukool
Sustainability 2026, 18(7), 3578; https://doi.org/10.3390/su18073578 - 6 Apr 2026
Viewed by 411
Abstract
Residential air conditioning systems are a major contributor to household electricity consumption in tropical regions, where environmental factors such as climate variability and particulate pollution (PM10) can further increase cooling demand and accelerate equipment degradation. This study proposes an Enhanced Deep Q-Network (Enhanced [...] Read more.
Residential air conditioning systems are a major contributor to household electricity consumption in tropical regions, where environmental factors such as climate variability and particulate pollution (PM10) can further increase cooling demand and accelerate equipment degradation. This study proposes an Enhanced Deep Q-Network (Enhanced DQN) for energy-efficient and maintenance-aware control of residential split-type air conditioners under dynamic environmental conditions. The proposed method integrates several stability-oriented reinforcement learning mechanisms, including Double Q-learning, a dueling architecture, prioritized experience replay, multi-step returns, Bayesian-style regularization via Monte Carlo dropout, and entropy-aware exploration. The framework is evaluated through a two-stage process consisting of a diagnostic benchmark on LunarLander-v3 to assess learning stability, followed by a realistic 365-day simulation driven by Thai weather and PM10 data. Compared with a fixed 25 °C baseline, the proposed controller reduced annual electricity consumption from 5116.22 kWh to as low as 4440.03 kWh, corresponding to a saving of 13.22%. The learned policy also exhibited environmentally adaptive behavior under high PM10 conditions, indicating maintenance-aware characteristics. These findings demonstrate that reinforcement learning can provide robust, adaptive, and sustainable control strategies for residential cooling systems in tropical environments. Full article
(This article belongs to the Special Issue AI in Smart Cities and Urban Mobility)
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