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Advances in Transportation and Smart City
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Advances in Transportation and Smart City

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 1323

Special Issue Editors

Prof. Dr. Rose Luke

E-Mail Website
Guest Editor
Institute of Transport and Logistics Studies (Africa), University of Johannesburg, Johannesburg 2006, South Africa
Interests: urban mobility; transport-related social exclusion; smart transport; smart logistics; smart mobility; transport in developing economies
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hossana Twinomurinzi

E-Mail Website
Guest Editor
Centre for Applied Data Science, College of Business & Economics, University of Johannesburg, Johannesburg 1709, South Africa
Interests: ICT4D; digital skills; applied data science
Prof. Dr. Joash Mageto

E-Mail Website
Guest Editor
Institute of Transport and Logistics Studies (Africa), University of Johannesburg, Johannesburg 2006, South Africa
Interests: logistics outsourcing; supply chain management; mixed methods; structural equation modelling; smart mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart cities can enhance the quality of life and achieve excellent levels of efficiency if they are underpinned by excellent and smart transport systems. This Special Issue focuses on developments in transportation to further the aims of smart cities. Topics include, but are not limited to, the following:

  • Connected transport;
  • IoT in transport;
  • Transport technologies;
  • Sustainable transport in smart cities;
  • Advances in last-mile deliveries;
  • Intelligent transport systems;
  • Societal impact of smart transport systems;
  • Machine learning;
  • Smart charging / smart grid;
  • Crowd shipping;
  • Mobility as a service (MaaS);
  • Vehicle route optimization;
  • Smart transport.

Prof. Dr. Rose Luke
Prof. Dr. Hossana Twinomurinzi
Prof. Dr. Joash Mageto
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. Applied Sciences 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

  • smart cities
  • transport advances
  • smart transport
  • intelligent transport
  • sustainability

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

22 pages, 841 KB  
Article
Hidden Carbon Emissions Induced by Functional Curbside Capacity Loss in Urban Freight Systems
by Angel Gil Gallego, María Pilar Lambán, Jesús Royo Sánchez, Juan Carlos Sánchez Catalán and Paula Morella Avinzano
Appl. Sci. 2026, 16(9), 4367; https://doi.org/10.3390/app16094367 - 29 Apr 2026
Abstract
Curbside saturation in dense commercial corridors compromises the sustainability of last mile logistics. This study investigates the impact of “authorized but non functional occupancy” (Class S (Service)), referring to service and tradespeople vehicles, on the operational capacity of loading and unloading zones ( [...] Read more.
Curbside saturation in dense commercial corridors compromises the sustainability of last mile logistics. This study investigates the impact of “authorized but non functional occupancy” (Class S (Service)), referring to service and tradespeople vehicles, on the operational capacity of loading and unloading zones (LUZ). Based on direct field observations of 474 real vehicle entries in a zone in Zaragoza (Spain), an Erlang B no wait queuing model (M/M/1/1) using weighted occupancy time was applied to contrast current saturation levels with a regulated functional scenario. The results demonstrate that the infrastructure is structurally sufficient: removing inefficient uses reduces traffic intensity from 1.31 to 0.48 Erlangs, increasing service potential by 121.84%. Class S was identified as consuming 36.62% of the lost capacity, exceeding the impact of unauthorized private cars. Total Hidden Carbon Emissions (HCE) amounted to 45.34 kg CO2, establishing an environmental impact of 0.066 kg CO2 per misused linear meter. The study concludes that proper utilization of loading zones is sufficient to accommodate logistics demand and effectively reduce CO2 emissions. Full article
(This article belongs to the Special Issue Advances in Transportation and Smart City)
32 pages, 550 KB  
Article
Resilient Multi-Agent State Estimation for Smart City Traffic: A Systems Engineering Approach to Emission Mitigation
by Ahmet Cihan
Appl. Sci. 2026, 16(8), 3972; https://doi.org/10.3390/app16083972 - 19 Apr 2026
Viewed by 212
Abstract
Uninterrupted traffic flow monitoring is a prerequisite for optimal resource allocation and minimizing vehicular emissions in smart cities. However, centralized traffic management architectures are highly vulnerable to single points of failure. When structural sensor malfunctions occur, the resulting network unobservability paralyzes dynamic signalization, [...] Read more.
Uninterrupted traffic flow monitoring is a prerequisite for optimal resource allocation and minimizing vehicular emissions in smart cities. However, centralized traffic management architectures are highly vulnerable to single points of failure. When structural sensor malfunctions occur, the resulting network unobservability paralyzes dynamic signalization, triggering cascading traffic congestion, extended idling times, and severe greenhouse gas emissions. To address this cyber-ecological vulnerability, we propose the Hybrid Multi-Agent State Estimation (H-MASE) protocol, a fully decentralized decision-support framework designed from an applied systems reliability engineering perspective. By deploying PSAs and VLAs directly onto IoT-enabled edge devices at smart intersections, H-MASE leverages a hop-by-hop edge computing topology to collaboratively track macroscopic route flow dynamics. Mathematically, this distributed estimation process is formulated as a network-wide least-squares convex optimization problem, where local projection operators function as exact Distributed Gradient Descent steps to minimize the global residual sum of squares. The distributed consensus mechanism acts as a spatial variance reduction tool, effectively dampening measurement noise and stochastic demand fluctuations. Furthermore, we introduce an autonomous anomaly detection logic that isolates severe structural faults rapidly, which is mathematically structured to prevent false alarms under bounded disturbance conditions. Numerical simulations demonstrate that the protocol yields a highly resilient optimality gap (e.g., a Root Mean Square Error of merely 0.81 vehicles per estimated state) even under catastrophic hardware failures. Ultimately, H-MASE provides a robust, fail-safe data foundation for sustainable urban logistics and green-wave signalization, ensuring that smart cities maintain ecological resilience and optimal resource utilization under severe structural disruptions. Full article
(This article belongs to the Special Issue Advances in Transportation and Smart City)
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23 pages, 2975 KB  
Article
Large-Scale Metro Train Timetable Rescheduling via Multi-Agent Deep Reinforcement Learning: A High-Dimensional Optimization Approach in Flatland Environment
by Jufen Yang, Haozhe Yang, Weikang Wang and Chengyang Xia
Appl. Sci. 2026, 16(7), 3338; https://doi.org/10.3390/app16073338 - 30 Mar 2026
Viewed by 266
Abstract
Metro train timetable rescheduling (TTR) is a critical task for ensuring the reliability of urban rail transit systems. However, with the increasing density of railway networks and the growing number of operational trains, TTR has evolved into a typical high-dimensional and large-scale optimization [...] Read more.
Metro train timetable rescheduling (TTR) is a critical task for ensuring the reliability of urban rail transit systems. However, with the increasing density of railway networks and the growing number of operational trains, TTR has evolved into a typical high-dimensional and large-scale optimization problem. Traditional mathematical programming and heuristic approaches often struggle with the “curse of dimensionality” and fail to provide real-time responses under stochastic disturbances. To address these challenges, this paper proposes a novel framework based on Multi-Agent Deep Reinforcement Learning (MADRL). Specifically, we model the TTR problem as a decentralized cooperative process and utilize the Multi-Agent Advantage Actor-Critic (MAA2C) algorithm to optimize train schedules dynamically. The proposed framework is implemented within the Flatland simulation environment, which allows for the representation of complex arbitrary topologies. We design a composite reward function that minimizes total delay deviation while maximizing passenger satisfaction, subject to constraints such as headway, operating time, and train capacity. Furthermore, to enhance the robustness of the model against high-dimensional state uncertainties, random disturbances following a negative exponential distribution are introduced during training. Experimental results across various scenarios—ranging from simple dual-track to complex random networks—demonstrate that the MAA2C-based approach significantly outperforms traditional baselines. It not only achieves faster convergence in small-scale scenarios but also demonstrates superior computational efficiency and scalability in large-scale environments, effectively minimizing passenger waiting times. This study validates the potential of MADRL in solving high-dimensional traffic control problems for intelligent transportation systems. Full article
(This article belongs to the Special Issue Advances in Transportation and Smart City)
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25 pages, 4805 KB  
Article
Visual-Guidance Interventions for Child Pedestrian Behavior: An Empirical Study Employing Multimodal Experiments
by Wenjie Peng, Xinyu Zhang, Bingmiao Zhu, Shimeng Hao and Quan Jing
Appl. Sci. 2025, 15(24), 12919; https://doi.org/10.3390/app152412919 - 8 Dec 2025
Viewed by 560
Abstract
As urbanization accelerates, children’s safety when crossing urban streets has become an increasingly prominent concern. However, current street designs and visual guidance facilities are largely configured around adult users and tend to overlook children’s distinct cognitive and perceptual characteristics. In this study, we [...] Read more.
As urbanization accelerates, children’s safety when crossing urban streets has become an increasingly prominent concern. However, current street designs and visual guidance facilities are largely configured around adult users and tend to overlook children’s distinct cognitive and perceptual characteristics. In this study, we used seven virtual reality (VR) street-crossing scenarios and combined questionnaires, eye tracking, and motion capture to evaluate how five types of visual guidance elements—Footprint (stop) markings and Traffic bollard, Color-Coded Arrows, Look left markings, Tactile Paving Patterns, and Stop line—affect children’s street-crossing behavior. The results show that Footprint (stop) markings and Traffic bollard clearly enhance children’s Stopping–Scanning Awareness, prompting them to slow down and briefly pause within the decision zone. The Look left markings provide only limited cues for Left–Right Scanning in both adults and children. Tactile Paving Patterns and Color-Coded Arrows effectively attract children’s visual attention, but may weaken their judgement of street-crossing risk. The Stop line strengthens the visual boundary and increases environmental monitoring awareness among all participants; however, this study did not observe a clear improvement in Gait variability. By extending theories of children’s traffic behavior, this study also highlights that some facilities labeled as “child-friendly” may be over-designed or cognitively misaligned with children’s actual perceptual and decision-making processes. These findings provide empirical evidence for optimizing street facilities and for developing related technical standards and public policies. Full article
(This article belongs to the Special Issue Advances in Transportation and Smart City)
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