Smart Cities

28 pages, 7163 KB

Open AccessArticle

An Intelligent Arterial Traffic Control Framework for Visible Light-Connected Vehicles

by Gonçalo Galvão, Manuela Vieira, Manuel Augusto Vieira, Mário Véstias and Paula Louro

Smart Cities 2026, 9(4), 72; https://doi.org/10.3390/smartcities9040072 - 20 Apr 2026

Viewed by 351

Inefficient urban traffic management remains a critical challenge, as conventional signal controllers—built on fixed timing plans—cannot cope with the dynamic nature of modern city traffic. This study addresses this limitation by developing a decentralized MARL-based framework capable of coordinating five interconnected intersections as [...] Read more.

Inefficient urban traffic management remains a critical challenge, as conventional signal controllers—built on fixed timing plans—cannot cope with the dynamic nature of modern city traffic. This study addresses this limitation by developing a decentralized MARL-based framework capable of coordinating five interconnected intersections as a unified traffic cell. Central to the proposed solution is the Strategic Anti-Blocking Phase Adjustment (SAPA) module, which enables intersections to autonomously modify phase durations in response to real-time traffic conditions. The framework is designed to handle heterogeneous demand patterns, with particular emphasis on arterial corridors connecting urban centers to peripheral zones. Integration of a Visible Light Communication (VLC) network allows continuous monitoring of key variables, including vehicle kinematics and pedestrian activity, feeding the agents with rich environmental feedback. Experimental evaluation confirms the effectiveness of the approach: the SAPA-augmented DQN achieves roughly 33% shorter vehicle queues and a ~70% reduction in pedestrian waiting counts relative to a standard DQN baseline. Remarkably, these gains bring the value-based method to a performance level comparable to MAPPO, a considerably more complex multi-agent policy optimization algorithm, establishing SAPA as an efficient and scalable enhancement for intelligent urban traffic control. Full article

(This article belongs to the Special Issue Intelligent Control and Planning for Urban Network Efficiency and Safety Optimization)

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32 pages, 5970 KB

Open AccessSystematic Review

Reframing BIM and Digital Twins for Intelligent Built Environments

by Abdullahi Abdulrahman Muhudin, Md Shafiullah, Baqer Al-Ramadan, Mohammad Sharif Zami, Mohammad Tahir Zamani and Lazhari Herzallah

Smart Cities 2026, 9(4), 71; https://doi.org/10.3390/smartcities9040071 - 17 Apr 2026

Viewed by 994

Abstract

The integration of Building Information Modeling [BIM] and Digital Twins [DT] has emerged as a central driver of digital transformation in the architecture, engineering, and construction sector. Yet, its systemic impact remains constrained by conceptual fragmentation and uneven institutional adoption. This study synthesizes [...] Read more.

The integration of Building Information Modeling [BIM] and Digital Twins [DT] has emerged as a central driver of digital transformation in the architecture, engineering, and construction sector. Yet, its systemic impact remains constrained by conceptual fragmentation and uneven institutional adoption. This study synthesizes contemporary BIM–DT scalability and each to identify dominant technological and application dimensions, examine the governance conditions shaping scalability, and develop an analytical framework that advances understanding beyond technology-centered syntheses. A two-stage analytical design was employed, combining bibliometric keyword co-occurrence analysis of 1295 Scopus-indexed records with systematic qualitative synthesis of 56 peer-reviewed journal articles published between 2020 and 2025, following PRISMA guidelines. Six interrelated analytical dimensions characterize the current BIM–DT research landscape: BIM–DT integration advancements and applications; interoperability and visualization; safety enhancement; energy efficiency; data-driven decision making; and stakeholder collaboration. Across these dimensions, a persistent misalignment emerges between technological capability and organizational readiness, with deficiencies in standards, governance, and sociotechnical coordination constituting the principal barriers to large-scale deployment. The findings reframe BIM–DT convergence not as a discrete technological upgrade but as the emergence of a coordinated socio-technical information ecosystem spanning the full building lifecycle. By foregrounding governance conditions, data stewardship, and institutional coordination, this study extends understanding of how digital twins expand BIM from design coordination to operational governance and establishes a foundation for more systematic implementation of intelligent, resilient, and sustainable built-environment systems. Full article

(This article belongs to the Section Buildings in Smart Cities)

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19 pages, 4764 KB

Open AccessArticle

Wavelet–Deep Learning Framework for High-Resolution Fault Detection, Classification, and Localization in WMU-Enabled Distribution Systems

by Dariush Salehi, Navid Vafamand, Shayan Soltani, Innocent Kamwa and Abbas Rabiee

Smart Cities 2026, 9(4), 70; https://doi.org/10.3390/smartcities9040070 - 16 Apr 2026

Viewed by 566

Abstract

Timely fault detection, classification, and localization are fundamental to enabling fast service restoration in modern distribution networks, and are especially vital for maintaining the reliability and resilience of smart city electricity infrastructures. A new AI-based method for classifying and localizing fault types is [...] Read more.

Timely fault detection, classification, and localization are fundamental to enabling fast service restoration in modern distribution networks, and are especially vital for maintaining the reliability and resilience of smart city electricity infrastructures. A new AI-based method for classifying and localizing fault types is presented in this paper, which enhances situational awareness in smart distribution grids that supply dense urban loads and critical smart city services. The proposed approach targets various fault conditions, which include three-phase-to-ground, three-phase, two-phase-to-ground, two-phase, and single-phase-to-ground faults. The proposed method utilizes a wavelet-based signal processing technique to analyze the feeder’s current data captured by waveform measurement units (WMUs) and extracts features for fault analysis. As a result of these features, a multi-stage machine learning architecture incorporating deep learning components is developed to accurately determine the occurrence, type, and location of faults. To evaluate the performance of the proposed approach, simulations were conducted on a 16-bus distribution network. Results show a high level of accuracy in fault detection, classification, and localization. This indicates that the method can be a valuable tool for enhancing the resilience and intelligence of future power grids, as well as supporting self-healing and fast service restoration in smart city services. Full article

(This article belongs to the Special Issue Intelligent Control and Planning for Urban Network Efficiency and Safety Optimization)

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22 pages, 1136 KB

Open AccessArticle

Co-Optimized Scheduling of a Multi-Microgrid System Based on a Reputation Point Trading Mechanism

by Jiankai Fang, Dongmei Yan, Hongkun Wang, Hui Deng, Xinyu Meng and Hong Zhang

Smart Cities 2026, 9(4), 69; https://doi.org/10.3390/smartcities9040069 - 15 Apr 2026

Viewed by 368

Abstract

With the rapid integration of distributed energy resources, achieving a balance between economic efficiency and environmental sustainability in multi-microgrid (MMG) systems is critical. However, existing studies typically treat microgrid operators as fully compliant entities. They often neglect the “trust-risk” dimension along with potential [...] Read more.

With the rapid integration of distributed energy resources, achieving a balance between economic efficiency and environmental sustainability in multi-microgrid (MMG) systems is critical. However, existing studies typically treat microgrid operators as fully compliant entities. They often neglect the “trust-risk” dimension along with potential default behaviors in decentralized markets. This paper proposes a novel co-optimized scheduling model for urban MMG systems, centered on a unified “Social–Economic–Physical” coupling framework. To ensure transaction integrity, a robust reputation evaluation framework is developed using Root Mean Square Error (RMSE), mean absolute error (MAE), plus Dynamic Time Warping (DTW). This framework effectively identifies fraudulent data or contractual breaches. Furthermore, to enhance fairness while promoting decarbonization, the model integrates a dynamic network pricing strategy based on the Shapley value. It works alongside a reputation-weighted reward–penalty step-type carbon trading scheme. The proposed model is formulated as a mixed-integer linear programming (MILP) problem and solved using MATLAB R2025b with CPLEX 12.10. Simulation results demonstrate that the integrated approach significantly optimizes system performance. Total carbon emissions are reduced by 49.6 tons. Meanwhile, revenues for the MMG Alliance, individual microgrids, and shared energy storage operators increase by 4.08% to 33.00%. The proposed framework provides a practical governance solution for Smart City multi-microgrid systems, effectively addressing the “trust-risk” challenge in decentralized urban energy markets. The findings validate that the proposed mechanism effectively fosters a trustworthy trading environment, achieving a “win-win” outcome for economic profitability and urban energy resilience. Full article

(This article belongs to the Section Smart Urban Energies and Integrated Systems)

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28 pages, 9705 KB

Open AccessArticle

Enhancing Smart Building Energy Resilience: A Novel Parallel-Series PV Architecture for Urban Partial Shading Mitigation

by Tanveer Abbas, Syed Talha Safeer Gardezi, Noman Khan, Adnan Khan, Shakeel Ahmed and Kambiz Tehrani

Smart Cities 2026, 9(4), 68; https://doi.org/10.3390/smartcities9040068 - 13 Apr 2026

Viewed by 395

Abstract

Building-integrated photovoltaic systems are essential components of smart buildings and sustainable urban infrastructure, contributing to energy efficiency and carbon footprint reduction in smart cities. Mismatch loss, particularly under partial shading, is one of the concerns in photovoltaic (PV) systems, especially in urban environments [...] Read more.

Building-integrated photovoltaic systems are essential components of smart buildings and sustainable urban infrastructure, contributing to energy efficiency and carbon footprint reduction in smart cities. Mismatch loss, particularly under partial shading, is one of the concerns in photovoltaic (PV) systems, especially in urban environments where buildings, trees, and other structures create complex shading patterns. It leads to significant power loss and poor efficiency. Several methods, such as string converters, multi-string converters, central converters, and micro-inverters/power optimizers, have been widely employed to address this issue. These methods suffer from hardware complexity and are good in certain shading patterns only; they remain ineffective otherwise. Power optimizers lead in efficiency under all the shading patterns, whereas string converters lead in hardware simplicity. We propose a novel parallel-series converter to mitigate mismatch losses in smart building applications that is as efficient as power optimizers and as simple as converters. In the proposed parallel-series converter design, multiple PV modules are connected in parallel to a very simple converter, and many such converters are then connected in series to get the final output. The proposed converter is rigorously evaluated for various shading patterns using MATLAB/SIMULINK. A prototype system of

3 \times 2

PV panels is also developed for hardware evaluation. The simulation and hardware results show that the proposed parallel-series converter dominantly competes with power optimizers with much simpler hardware and outperforms the other converters, making it particularly suitable for smart building energy systems where cost-effectiveness and reliability are critical. Full article

(This article belongs to the Topic Application of Smart Technologies in Buildings)

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22 pages, 1362 KB

Open AccessArticle

Towards a Temporal City: Time of Day as a Structural Dimension of Urban Accessibility

by Irfan Arif, Fahim Ullah, Siddra Qayyum and Mahboobeh Jafari

Smart Cities 2026, 9(4), 67; https://doi.org/10.3390/smartcities9040067 - 10 Apr 2026

Viewed by 604

Abstract

Urban accessibility is commonly evaluated using static spatial indicators, which assume stable travel conditions throughout the day. Road congestion, network saturation, and service variability change the function and experience of the built environment (BE). This study tests the Temporal City Framework (TCF) by [...] Read more.

Urban accessibility is commonly evaluated using static spatial indicators, which assume stable travel conditions throughout the day. Road congestion, network saturation, and service variability change the function and experience of the built environment (BE). This study tests the Temporal City Framework (TCF) by examining how time of day (TOD) reshapes urban accessibility and travel behaviour with varying levels of congestion. Using 30,288 trip records from the 2022 US National Household Travel Survey (NHTS), duration is operationalised as a sixth dimension of the BE. A time-normalised impedance metric, measured in minutes per mile (MPM), is used that captures realised congestion independently of distance. Temporal impedance (TI) varies strongly with TOD, with substantially higher MPM during peak and midday periods than at night. Compared with nighttime conditions, midday travel requires approximately 19% more time per mile. This indicates a measurable contraction in functional accessibility under identical BE conditions. The TI model outperforms duration-only models, with impedance remaining dominant when both measures are included. These results support interpreting duration as a structural dimension of urban accessibility. TI significantly increases the relative likelihood of active and public transport compared to private cars, even after accounting for absolute trip duration. Hired transport modes (taxi and ride-hailing services) are most prevalent at night, reflecting a greater reliance on on-demand services outside regular daytime schedules. This study tests duration as a structural dimension of the BE by operationalising time-normalised TI. Associations are interpreted as trip-level behavioural constraints rather than causal effects. Planning frameworks based on static travel times systematically misrepresent exposure, equity, and travel mode feasibility. Time-stratified accessibility metrics should therefore be integrated into transport and land-use evaluation and associated policies. Full article

(This article belongs to the Special Issue Paving the Future: Sustainable Road Design and Urban Mobility in Smart Cities, 2nd Edition)

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36 pages, 4855 KB

Open AccessArticle

A Timed Petri Net-Based Dynamic Visitor Guidance Model for Mountain Scenic Areas During Peak Periods

by Binyou Wang, Liyan Lu, Changyong Liang, Xiaohan Yan, Shuping Zhao and Wenxing Lu

Smart Cities 2026, 9(4), 66; https://doi.org/10.3390/smartcities9040066 - 10 Apr 2026

Viewed by 246

Abstract

Tourist congestion and load imbalance during peak periods pose critical challenges to the safe operation and experience assurance of large scenic areas. To address the limitations of traditional management approaches in capturing the dynamic and stochastic nature of tourist flows, this study develops [...] Read more.

Tourist congestion and load imbalance during peak periods pose critical challenges to the safe operation and experience assurance of large scenic areas. To address the limitations of traditional management approaches in capturing the dynamic and stochastic nature of tourist flows, this study develops a dynamic visitor guidance modeling and analysis framework based on a Timed Petri Net. The proposed model provides a formal representation of tourist movements, scenic spot load evolution, and guidance decision mechanisms within a scenic area. Under unified parameter settings and controlled random conditions, multiple visitor guidance strategies with different information coverage scopes are designed, and minute-level simulation experiments are conducted using the Huangshan Scenic Area as a case study. The simulation results show that, compared with unguided tourist flows, the proposed strategies significantly reduce average load levels, alleviate spatial load imbalance, and enhance TS. Using mean–standard deviation analysis, distributional analysis, and dynamic evolution analysis, differences among guidance strategies in terms of load control, visitor experience, and operational stability are systematically evaluated. Furthermore, a quantitative relationship model between tourist satisfaction and scenic area load is constructed, revealing a consistent inverted-U pattern. Robustness tests under multiple random seeds indicate that the main conclusions are not sensitive to specific stochastic realizations. Overall, the simulation results suggest that dynamic visitor guidance may improve load control, visitor experience, and system stability by optimizing the spatiotemporal distribution of tourist flows, thereby providing simulation-based quantitative insights for peak-period management in large scenic areas. Full article

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33 pages, 3926 KB

Open AccessArticle

BiLSTM Guided LPA Planning, Re-Planning, and Backtracking for Effective and Efficient Emergency Evacuation

by Ramzi Djemai, Hamza Kheddar, Mohamed Chahine Ghanem, Karim Ouazzane and Erivelton Nepomuceno

Smart Cities 2026, 9(4), 65; https://doi.org/10.3390/smartcities9040065 - 7 Apr 2026

Viewed by 394

Abstract

Emergency evacuation in complex and dynamic building environments requires robust and adaptive routing strategies capable of responding to evolving hazards, blocked passages, and changing crowd behaviour. Most existing evacuation planners rely on static geometric representations and lack semantic awareness of the environment, limiting [...] Read more.

Emergency evacuation in complex and dynamic building environments requires robust and adaptive routing strategies capable of responding to evolving hazards, blocked passages, and changing crowd behaviour. Most existing evacuation planners rely on static geometric representations and lack semantic awareness of the environment, limiting their ability to perform informed re-planning and backtracking when routes become unsafe. This paper proposes a neuro-symbolic evacuation planning framework that integrates Lifelong Planning A* (LPA*) with ontology-driven semantic reasoning and a Bidirectional Long Short-Term Memory (BiLSTM) prediction model. The building’s spatial and semantic knowledge is represented using the Web Ontology Language (OWL) and Resource Description Framework (RDF), enabling automated inference of implicit connections and enforcement of safety policies. The BiLSTM model learns temporal patterns from ontology-consistent evacuation trajectories and provides guidance for remaining-cost estimation and early prediction of routes likely to require backtracking, which is combined with a bounded semantic heuristic to preserve admissibility and optimality guarantees. Simulation results in a multi-floor academic building show that the proposed BiLSTM-guided semantic LPA* framework reduces average evacuation time by up to 9.6%, decreases node expansions by up to 32%, and increases evacuation success rates to 96.2% compared with a purely semantic baseline. The BiLSTM model also achieves strong predictive performance, with a test AUC of 0.92 for backtracking prediction and a next-state accuracy of 87.1%. The proposed framework is designed to support explainable, policy-compliant, and incrementally adaptable evacuation guidance under rapidly evolving emergency conditions. Full article

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24 pages, 1396 KB

Open AccessReview

The Role and Significance of Rail Transport in the Decarbonisation of the EU Transport Sector

by Mladen Bošnjaković, Robert Santa and Maja Čuletić Čondrić

Smart Cities 2026, 9(4), 64; https://doi.org/10.3390/smartcities9040064 - 7 Apr 2026

Viewed by 602

Abstract

Globally, the transport sector accounts for almost a quarter of CO₂ emissions from fuel combustion and generates large amounts of pollutants, placing significant pressure on the environment and human health. By 2050, the European Green Deal requires a 90% reduction in transport-related [...] Read more.

Globally, the transport sector accounts for almost a quarter of CO₂ emissions from fuel combustion and generates large amounts of pollutants, placing significant pressure on the environment and human health. By 2050, the European Green Deal requires a 90% reduction in transport-related emissions, making sustainability necessary across all modes of transport. Based on the relevant literature, this study examines the role and potential of railways in decarbonising the EU transport sector. Railway is highly efficient, consuming just 1.9% of transport sector energy while handling 16.9% of freight and 5.1% of passenger transport in the EU, yet is responsible for only 0.4% of total emissions. According to studies, greenhouse gas emissions can be reduced by improving energy efficiency, using low-carbon or renewable energy, and expanding train electrification. The greatest potential for decarbonisation lies in a modal shift to rail. However, this requires significant infrastructure investment: raising line speeds to at least 160 km/h, expanding networks, building terminals, digitalisation, and alignment with TEN-T standards. Although the EU supports the modal shift with funding programmes, the transition is not progressing as expected—the share of road freight transport increased from 74% in 2013 to 78% in 2023. Stronger investment is needed in Member States’ national policies for the development and modernisation of railways. The authors developed a Path Evaluation Matrix (PEM), a quantitative decision framework integrating the fields of energy, transport, politics, and economics. The PEM results indicate that BEMU (battery electric multiple units) is optimal for 68% of secondary lines in south-eastern Europe. Full article

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4 pages, 166 KB

Open AccessEditorial

Next Generation of Smart Grid Technologies

by Luis M. Fernández-Ramírez, Chun Sing Lai and Payman Dehghanian

Smart Cities 2026, 9(4), 63; https://doi.org/10.3390/smartcities9040063 - 5 Apr 2026

Viewed by 623

Abstract

The emergence of smart cities demands a fundamental transformation in how energy is managed, positioning smart grid technologies as a key driver in urban progress [...] Full article

(This article belongs to the Special Issue Next Generation of Smart Grid Technologies)

24 pages, 1281 KB

Open AccessArticle

Rethinking Pooled Ride-Hailing as Large-Scale Simulations Reveal System Limits

by Haitam Laarabi, Zachary A. Needell, Rashid A. Waraich and C. Anna Spurlock

Smart Cities 2026, 9(4), 62; https://doi.org/10.3390/smartcities9040062 - 1 Apr 2026

Viewed by 769

Abstract

Over nearly two decades, ride-hailing has become a major component of urban travel, and its tendency to increase vehicle miles traveled (VMT) and worsen congestion is now well established. What remains poorly understood is why pooling, the most frequently proposed remedy, consistently falls [...] Read more.

Over nearly two decades, ride-hailing has become a major component of urban travel, and its tendency to increase vehicle miles traveled (VMT) and worsen congestion is now well established. What remains poorly understood is why pooling, the most frequently proposed remedy, consistently falls short of theoretical expectations. With access to proprietary platform data still limited, high-fidelity simulation offers a promising path to untangle these dynamics. Here, we implement three pooling algorithms alongside a demand-following repositioning algorithm, within Berkeley Lab’s BEAM (Behavior, Energy, Autonomy, and Mobility), an open-source, agent-based regional transportation model. In a high ride-hailing adoption scenario for the San Francisco Bay Area, we find a counterintuitive result: the more stringently point-to-point pooling is promoted, the more detour burdens erode matching feasibility and reduce vehicle occupancy rather than increase it, thereby compounding rather than offsetting VMT and congestion impacts. Sensitivity analysis further identifies inflection points in pooling match rates and repositioning sensitivity beyond which deadheading and negative network feedbacks begin to dominate. These results show that pooled ride-hailing has a constrained ability to reduce network-wide impacts and that effective shared mobility requires treating pooling, repositioning, and fleet sizing as interdependent levers. Full article

(This article belongs to the Special Issue Cost-Effective Transportation Planning for Smart Cities)

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29 pages, 2066 KB

Open AccessArticle

Intelligence Collision Detection Using a Combination of Tuning Base Methods and Convolutional Long Short Term Memory Models

by Mohammed Hilfi and Lubna Alazzawi

Smart Cities 2026, 9(4), 61; https://doi.org/10.3390/smartcities9040061 - 31 Mar 2026

Viewed by 641

Abstract

Effective traffic control using Artificial Intelligence (AI) is essential to ensure safe passage for all road users. AI-based collision detection systems offer advanced mechanisms to prevent accidents and improve highway safety. This research investigates two distinct collision scenarios: vehicle–pedestrian and vehicle–motorcyclist interactions. The [...] Read more.

Effective traffic control using Artificial Intelligence (AI) is essential to ensure safe passage for all road users. AI-based collision detection systems offer advanced mechanisms to prevent accidents and improve highway safety. This research investigates two distinct collision scenarios: vehicle–pedestrian and vehicle–motorcyclist interactions. The proposed method in this research involves the bidirectional Long Short Term Memory (LSTM), Convolutional Neural Network with LSTM (CNN–LSTM), and transformer models. The model is furthermore tuned using random or grid search. For the pedestrian–vehicle scenario, the CNN–LSTM model achieved 99.76% accuracy, 99.77% precision, and 99.76% recall, highlighting its strong classification performance. In the vehicle–motorcyclist scenario, the bidirectional LSTM reached 99.73% accuracy with precision and recall of 99.15%, demonstrating its effectiveness in detecting imminent crashes. The optimized CNN-LSTM by random search has focused on decreasing the false-positive rate and increasing the positive rate. It has achieved superior results compared to previous research. These results suggest that the system could be effectively implemented as an early collision warning solution on edge devices. Full article

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19 pages, 3217 KB

Open AccessArticle

Cost-Effective Planning of Station-Based Car-Sharing Systems: Increasing Efficiency While Emphasizing User Comfort

by Nico Nachtigall and Markus Lienkamp

Smart Cities 2026, 9(4), 60; https://doi.org/10.3390/smartcities9040060 - 28 Mar 2026

Viewed by 599

Abstract

Station-based car-sharing has been shown to reduce resource-intensive private car ownership. However, only a small proportion of the population uses station-based car-sharing, which could be improved by redesigning the service to reduce walking distances and increase availability. We developed a method for designing [...] Read more.

Station-based car-sharing has been shown to reduce resource-intensive private car ownership. However, only a small proportion of the population uses station-based car-sharing, which could be improved by redesigning the service to reduce walking distances and increase availability. We developed a method for designing an efficient and cost-effective station-based car-sharing network for smart cities that emphasizes user comfort and convenience, while reducing the number of needed cars. To quantify the placements, we created a high-resolution synthetic population for Munich, Germany as a case study. The population was based on census and OpenStreetMap data, and each person was assigned to a suitable mobility plan derived from two mobility surveys. Since car ownership and station-based car-sharing are particularly associated with trips for vacations, we supplemented the mobility plans with long-distance travel data from a one-year tracking dataset. This allowed us to perform a spatial and temporal analysis of the theoretical potential of various station placements for station-based car-sharing. The tested station networks varied in user comfort, especially in the distance to the nearest station and the group size of car-sharing users. Our findings indicate that the best trade-off between convenience and efficiency is a station design with a group size of 217–949 people. We further found that the car-sharing fleet size is strongly influenced by long-distance trips, and that a substitution rate of 1:1.25 to 3.3 with private cars is possible. Full article

(This article belongs to the Special Issue Cost-Effective Transportation Planning for Smart Cities)

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32 pages, 4751 KB

Open AccessArticle

Advanced Multivariate Deep Learning Methodology for Forecasting Wind Speed and Solar Irradiation

by Md Shafiullah, Abdul Rahman Katranji, Mannan Hassan, Md Mahfuzur Rahman and Sk. A. Shezan

Smart Cities 2026, 9(4), 59; https://doi.org/10.3390/smartcities9040059 - 27 Mar 2026

Viewed by 867

Abstract

The transition to smart cities is accelerating distributed wind and solar deployment. However, their intermittency challenges grid operation, thereby making accurate machine-learning-based prediction of wind speed and global horizontal irradiance (GHI) crucial. This study presents a cost-effective approach that enhances prediction accuracy by [...] Read more.

The transition to smart cities is accelerating distributed wind and solar deployment. However, their intermittency challenges grid operation, thereby making accurate machine-learning-based prediction of wind speed and global horizontal irradiance (GHI) crucial. This study presents a cost-effective approach that enhances prediction accuracy by extracting additional features from timestamp records for deep learning models used to forecast GHI and wind speed. Unlike conventional methods that require onsite meteorological measurements, the proposed approach uses only date and time information as inputs to multivariate deep neural networks, including recurrent neural networks, gated recurrent units, long short-term memory (LSTM), bidirectional LSTM, and convolutional neural networks. For wind speed prediction, the proposed configuration achieves R² up to 0.9987, with RMSE as low as 0.067 m/s for 3 d ahead forecasting, outperforming univariate baselines and matching models. For GHI forecasting, the time-based configuration attains R² values above 0.9994 in 12 h ahead predictions, with the RMSE reduced to approximately 4.47 W/m², representing a substantial improvement over univariate models. The proposed framework maintains strong performance, particularly under clear and sunny conditions. These results demonstrate that timestamp-engineered features can deliver forecasting accuracy comparable to conventional multivariate meteorological models while significantly reducing infrastructure requirements, making the approach well-suited for scalable smart city energy management. Full article

(This article belongs to the Special Issue Energy Strategies of Smart Cities, 2nd Edition)

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46 pages, 2508 KB

Open AccessArticle

Urban Communication in Smart Cities: Stakeholder Participation Motivators

by Laura Minskere, Diana Kalnina, Jelena Salkovska and Anda Batraga

Smart Cities 2026, 9(4), 58; https://doi.org/10.3390/smartcities9040058 - 26 Mar 2026

Viewed by 775

Abstract

The smart city concept has become a dominant framework for contemporary urban governance, largely driven by advances in digital technologies and data-driven decision-making. However, the prevailing technocratic orientation of smart city development risks marginalising the sociopolitical dimensions of urban governance, particularly citizen and [...] Read more.

The smart city concept has become a dominant framework for contemporary urban governance, largely driven by advances in digital technologies and data-driven decision-making. However, the prevailing technocratic orientation of smart city development risks marginalising the sociopolitical dimensions of urban governance, particularly citizen and stakeholder participation. Although smart governance frameworks increasingly recognise participation as a normative principle, limited empirical attention has been paid to the participation motivators that drive engagement among different urban stakeholder groups. This study addresses this gap by analysing the key motivators influencing stakeholder participation in urban development within a smart city context. Building on established behavioural and participation theories, the article develops an Urban Participation Motivator Model comprising four core motivators: social pressure, emotional trigger, rational motivation, and reward for participation. The model is empirically tested using quantitative survey data from 620 respondents representing four stakeholder groups in Riga, Latvia: municipal residents, municipal employees, municipal politicians, and real estate developers. Data are analysed using descriptive statistics and non-parametric methods, including the Kruskal–Wallis test. The results reveal statistically significant differences in the perceived importance of participation motivators across stakeholder groups. Emotional triggers and social pressure emerge as the most influential motivators overall, while rational motivation is particularly salient for professional stakeholders. Reward for participation plays a weaker but differentiated role, being most relevant for municipal employees. These findings highlight the need for differentiated motivator-sensitive urban communication and participation strategies to enhance inclusiveness, democratic legitimacy, and long-term engagement in smart city development. Full article

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32 pages, 1462 KB

Open AccessArticle

Startup-Driven Air-Front Smart City Policy Evaluation Using Integrated Accessibility Index: A Case Study of Aichi, Singapore, and Munich

by Mustafa Mutahari, Nao Sugiki, Tsuyoshi Takano, Hiroyoshi Morita, Yoshitsugu Hayashi and Kojiro Matsuo

Smart Cities 2026, 9(4), 57; https://doi.org/10.3390/smartcities9040057 - 25 Mar 2026

Viewed by 1215

Abstract

The Air-front Smart City (ASC) concept is proposed to address the stagnation of industries in developed countries and stimulate economic growth in developing countries while maintaining a higher quality of life for people and contributing to decarbonization and overall United Nations SDGs in [...] Read more.

The Air-front Smart City (ASC) concept is proposed to address the stagnation of industries in developed countries and stimulate economic growth in developing countries while maintaining a higher quality of life for people and contributing to decarbonization and overall United Nations SDGs in an existing study. However, no studies have been conducted to assess ASC policies. Therefore, this study integrates the integrated accessibility index into the quality of life (QOL) and quality of business (QOB) evaluation models to assess the startup ecosystem in Aichi, Singapore, and Munich within the ASC concept. The study uses survey data conducted in Aichi to estimate monetary values of QOL and QOB component indicators, calculates the integrated accessibility indices, and estimates QOL and QOB. Furthermore, the study sets scenarios to assess the impacts of living and business urban policies in Aichi. Additionally, the study using Aichi parameters compares the startup ecosystem in Singapore and Munich. The result shows that the key drivers of startup attraction are corporate tax rate, economic growth, and safety; enhancing these indicators directly increases startups’ QOB, business partners, and residents’ QOL. It was found that QOB in Singapore is comparatively higher, whereas QOL is higher in Aichi. Full article

(This article belongs to the Collection Smart Governance and Policy)

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24 pages, 4011 KB

Open AccessArticle

Comparative Evaluation of Traffic Load Prediction Models for Intelligent Transportation Systems Using High-Resolution Urban Data

by Sara Atef

Smart Cities 2026, 9(4), 56; https://doi.org/10.3390/smartcities9040056 - 25 Mar 2026

Cited by 1 | Viewed by 666

Abstract

Short-term traffic load prediction is a fundamental component of intelligent transportation systems (ITSs), supporting real-time monitoring, congestion mitigation, and adaptive traffic management in smart cities. Owing to the dynamic and nonlinear nature of urban traffic, identifying prediction models that align with real-world traffic [...] Read more.

Short-term traffic load prediction is a fundamental component of intelligent transportation systems (ITSs), supporting real-time monitoring, congestion mitigation, and adaptive traffic management in smart cities. Owing to the dynamic and nonlinear nature of urban traffic, identifying prediction models that align with real-world traffic dynamics remains a key challenge. This study presents a comparative evaluation of data-driven traffic load prediction models using high-resolution one-minute traffic data collected from a major urban roundabout in Jeddah, Saudi Arabia. The evaluated models include regression-based machine learning approaches and recurrent deep learning architectures, which are assessed under consistent preprocessing and evaluation conditions. Model performance is evaluated using standard error metrics and complemented by temporal and residual analyses to examine prediction behavior under different traffic regimes. The optimized GRU model achieved the best predictive accuracy with an RMSE of 149.12 veh/h, followed closely by the optimized LSTM model (RMSE = 150.85 veh/h). The results indicate that while conventional machine learning models can effectively capture overall traffic trends under relatively stable conditions, recurrent deep learning models demonstrate stronger capability in modeling nonlinear temporal dependencies and rapid traffic fluctuations when properly configured. In addition, a variability-based regime analysis was conducted to evaluate model robustness under different traffic demand dynamics, revealing that model performance advantages are context-dependent rather than universal. The findings highlight the importance of systematic comparative evaluation and data-driven model selection for developing reliable traffic prediction components in real-time ITS applications and sustainable urban mobility planning. Full article

(This article belongs to the Section Smart Urban Mobility, Transport, and Logistics)

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32 pages, 9463 KB

Open AccessArticle

Smart Tourism for All: Optimizing Rental Hub Locations for Specialized Off-Road Wheelchairs Using Spatial Analysis

by Marcin Jacek Kłos and Marcin Staniek

Smart Cities 2026, 9(4), 55; https://doi.org/10.3390/smartcities9040055 - 24 Mar 2026

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Abstract

The development of Smart Tourism often overlooks the “Wilderness Last Mile”, leading to the spatial exclusion of people with disabilities in mountain areas. This problem exists because standard tourist maps and urban-centric accessibility models rely on averaged terrain data, failing to identify critical [...] Read more.

The development of Smart Tourism often overlooks the “Wilderness Last Mile”, leading to the spatial exclusion of people with disabilities in mountain areas. This problem exists because standard tourist maps and urban-centric accessibility models rely on averaged terrain data, failing to identify critical micro-scale barriers (e.g., short, sudden steep ascents) that pose severe safety and traction risks for off-road wheelchair users. To address this gap, this article presents a novel GIS methodology for planning accessible off-road tourism for electric Specialized Off-Road Wheelchairs. The proposed four-stage analytical model includes (1) graph-based trail network topologization to enable precise routing; (2) traction safety verification utilizing high-resolution (1 × 1 m) Digital Elevation Model (DEM) micro-segmentation to detect hidden slope barriers; (3) multi-criteria evaluation combining a user-calibrated Difficulty Index (EDI) and a Tourism Quality Index (TQI); and (4) a hub optimization algorithm that prioritizes locations maximizing the diversity of accessible routes. The method was empirically tested in a case study of the Bieszczady Mountains (Poland), calibrating the model with the technical limits (25% max slope) of a prototype wheelchair. The experimental results clearly validate the model’s superiority over traditional approaches: the micro-segmentation successfully identified hidden terrain traps, disqualifying 55% of the standard trail network that would have otherwise been deemed safe by average-slope assessments. Furthermore, the model identified a contiguous safe network of 153 km and pinpointed the optimal rental hub location, ensuring the highest inclusivity and route variety. Ultimately, this approach transforms raw spatial data into safe, ready-made tourism products, providing a precise tool with which to implement Universal Design in natural environments. Full article

(This article belongs to the Special Issue Paving the Future: Sustainable Road Design and Urban Mobility in Smart Cities, 2nd Edition)

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Smart Cities, Volume 9, Issue 4 (April 2026) – 18 articles

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