Smart Cities

28 pages, 9695 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

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)

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

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)

► Show Figures

Figure 1

39 pages, 3645 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

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

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

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

► Show Figures

Figure 1

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

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

► Show Figures

Figure 1

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

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

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)

► Show Figures

Figure 1

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

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

► Show Figures

Figure 1

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

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)

► Show Figures

Figure 1

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

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)

► Show Figures

Figure 1

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

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

► Show Figures

Figure 1

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

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)

► Show Figures

Graphical abstract

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

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)

► Show Figures

Figure 1

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

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)

► Show Figures

Figure 1

24 pages, 925 KB

Open AccessReview

GeoBIM for Geothermal Energy Efficiency in Buildings and Smart Cities: A Review

by Hugo Alexandre Silva Pinto, Luis M. Ferreira Gomes, Luis J. Andrade Pais, Miguel Nepomuceno, Luís Filipe Almeida Bernardo, Vanessa Gonçalves, Maria Vitoria Morais and Leonardo Marchiori

Smart Cities 2026, 9(3), 54; https://doi.org/10.3390/smartcities9030054 - 23 Mar 2026

Abstract

The global drive toward energy transition and carbon neutrality requires integrated and data-driven approaches for managing buildings and smart cities. Existing urban energy assessment frameworks remain fragmented and often lack multiscale interoperability between building-level models and territorial datasets. At the same time, shallow [...] Read more.

The global drive toward energy transition and carbon neutrality requires integrated and data-driven approaches for managing buildings and smart cities. Existing urban energy assessment frameworks remain fragmented and often lack multiscale interoperability between building-level models and territorial datasets. At the same time, shallow geothermal energy is emerging as an efficient and renewable solution for sustainable heating and cooling. To address these gaps, this study examines the potential of GeoBIM, the integration of Building Information Modeling (BIM) and Geographic Information Systems (GIS), as a unified framework for multiscale energy analysis and for supporting shallow geothermal applications. A systematic literature review was conducted based on the PRISMA framework, combining a systematic literature review using the Scopus database with the critical examination of representative case studies. The results show that GeoBIM-based modeling improves data quality, enhances thermal performance assessments, and supports the implementation of shallow geothermal systems, including energy piles and district-scale ground-coupled networks. Reported applications demonstrate energy consumption reductions exceeding 40% in certain urban contexts. Several research gaps and challenges were identified, particularly data interoperability issues, lack of standardization, computational complexity, and the need for specialized training. Overall, the review indicates that GeoBIM offers a promising pathway for optimizing resources, supporting informed decision-making, and advancing resilient and sustainable smart buildings and cities. Full article

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

► Show Figures

Figure 1

15 pages, 1328 KB

Open AccessArticle

Clustering of Driver Behavioral Strategies During Speed Cushion Traversal: A Driving Simulator Study

by Gaetano Bosurgi, Alessia Ruggeri, Giuseppe Sollazzo, Orazio Pellegrino and Domenico Passeri

Smart Cities 2026, 9(3), 53; https://doi.org/10.3390/smartcities9030053 - 20 Mar 2026

Abstract

Traffic calming measures are widely used to reduce operating speeds and mitigate crash risk in urban corridors; however, the way drivers adapt their control strategy when traversing Berlin speed cushions is still poorly described from a multivariate behavioral perspective. This study proposes a [...] Read more.

Traffic calming measures are widely used to reduce operating speeds and mitigate crash risk in urban corridors; however, the way drivers adapt their control strategy when traversing Berlin speed cushions is still poorly described from a multivariate behavioral perspective. This study proposes a behavior-oriented analysis to identify recurring speed-cushion traversal strategies using driving simulator telemetry. A fixed-base simulator reproduced a real urban corridor, and trajectories were segmented in device-centered spatial windows capturing approach, traversal, and immediate recovery. Each segment was summarized by three indicators describing longitudinal and lateral control: mean speed, peak braking demand, and average lane position deviation. Features were standardized and clustered using k-means. The number of clusters was selected primarily through mean silhouette evaluation, while resampling-based checks and a Gaussian mixture modeling comparison were used as supportive evidence rather than competing decision rules. Three traversal profiles emerged: smooth cautious, reactive cautious, and unmoderated fast. The introduction of speed cushions shifted the distribution of segments towards cautious profiles, while driver-level concentration within a single profile was moderate. Overall, results indicate that speed cushions influence the whole vehicle control strategy, offering a quantitative basis for behavior-oriented evaluation of local traffic calming interventions in smart-city contexts. Full article

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

► Show Figures

Figure 1

21 pages, 511 KB

Open AccessReview

Smart Urban Logistics and Tube-Based Freight Systems: A Review of Technological Integration and Implementation Barriers

by Fellaki Soumaya, Molk Oukili Garti, Arif Jabir and Jawab Fouad

Smart Cities 2026, 9(3), 52; https://doi.org/10.3390/smartcities9030052 - 19 Mar 2026

Abstract

Background: Smart urban logistics has emerged as a key element of sustainable city development, with direct effects on economic performance, environmental quality, and urban livability. Issues with traffic, pollutants, infrastructure strain, and last-mile delivery efficiency have become more pressing due to rapid urbanization [...] Read more.

Background: Smart urban logistics has emerged as a key element of sustainable city development, with direct effects on economic performance, environmental quality, and urban livability. Issues with traffic, pollutants, infrastructure strain, and last-mile delivery efficiency have become more pressing due to rapid urbanization and the expansion of e-commerce. In this regard, underground or enclosed corridor-based tube-based freight transit systems have surfaced as a viable smart infrastructure option for automated and low-impact commodities delivery. Methods: This study adopts an analytical literature review complemented by a structured case study analysis to examine the potential role of tube-based freight transport systems in future urban logistics. Key technological concepts, including pneumatic tubes, automated capsule transport, and integration with digital platforms, the Physical Internet, and smart city management systems, are examined through a structured analytical review of the literature. Results: The outcome of the reviewed studies indicates that tube-based systems can contribute to congestion alleviation, emission reduction, and improved delivery reliability by shifting selected freight flows away from surface transport networks. However, governance frameworks, infrastructure integration, and institutional coordination mechanisms continue to have a significant impact on claimed performance outcomes. Conclusions: Tube-based freight systems represent a promising but conditional pathway toward smarter and more sustainable urban logistics. Their large-scale deployment is forced by high capital costs, standardization challenges, regulatory uncertainty, and social acceptance issues. Coordinated investment plans, encouraging legal frameworks, and integrated urban planning techniques in line with smart city goals are needed to overcome these obstacles. Full article

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

► Show Figures

Figure 1

28 pages, 22141 KB

Open AccessArticle

Detection of P-Wave Arrival as a Structural Transition in Seismic Signals: An Approach Based on SVD Entropy

by Margulan Ibraimov, Zhanseit Tuimebayev, Alua Maksutova, Alisher Skabylov, Dauren Zhexebay, Azamat Khokhlov, Lazzat Abdizhalilova, Aliya Aktymbayeva, Yuxiao Qin and Serik Khokhlov

Smart Cities 2026, 9(3), 51; https://doi.org/10.3390/smartcities9030051 - 19 Mar 2026

Abstract

Early and reliable detection of P-wave arrivals is critical for seismic monitoring and earthquake early warning, particularly under low signal-to-noise ratio (SNR) and non-stationary noise conditions. This study presents an automatic detection method based on singular value decomposition (SVD) entropy computed in sliding [...] Read more.

Early and reliable detection of P-wave arrivals is critical for seismic monitoring and earthquake early warning, particularly under low signal-to-noise ratio (SNR) and non-stationary noise conditions. This study presents an automatic detection method based on singular value decomposition (SVD) entropy computed in sliding time windows with local signal filtering. Within this framework, the P-wave onset is interpreted as a local structural change in the signal rather than a simple energy increase. SVD entropy captures the redistribution of energy among dominant signal components, providing high sensitivity to the initial P-wave arrival even at moderate and low noise levels (

S N R \geq 2

). The method was validated using real seismic data from four regional stations operating under different noise conditions. Analysis of detection parameters revealed strong station dependence. For stations affected by low-frequency drift, polynomial detrending was identified as a necessary preprocessing step to ensure a stable entropy response and reliable detection. The proposed approach achieves detection accuracies of up to 93–98% at

S N R \geq 2

, significantly outperforming the classical STA/LTA algorithm and demonstrating performance comparable to modern deep learning methods. Since the method does not require model training or labeled datasets, it provides an interpretable and computationally efficient solution for automatic seismic monitoring. These properties make the proposed approach particularly suitable for real-time seismic monitoring systems and distributed sensor networks operating under limited computational resources. All computational stages were performed at the Farabi Supercomputer Centre of Al-Farabi Kazakh National University. The method requires no model training or labeled data, making it an interpretable, robust, and computationally efficient solution for automatic seismic monitoring and early warning systems. Full article

► Show Figures

Figure 1

20 pages, 5148 KB

Open AccessArticle

Towards Supporting Real-Time Estimation of Vehicle Fuel Consumption and CO₂ Emissions in Smart City Applications

by Abrar Alali and Stephan Olariu

Smart Cities 2026, 9(3), 50; https://doi.org/10.3390/smartcities9030050 - 18 Mar 2026

Abstract

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches [...] Read more.

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously absent in the literature. We identify optimal powertrain efficiency values that are directly derived from publicly available vehicle specifications, ensuring transparency and accessibility. Our findings demonstrate that this simple, physics-based model accurately estimates fuel consumption and CO₂ emissions for standard EPA cycles and can be effectively generalized to user-defined scenarios. This establishes a computationally efficient, interpretable, and robust method for environmental impact assessment, policy evaluation, and real-time emissions estimation. Full article

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

► Show Figures

Figure 1

Journal Description

Smart Cities

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI