Smart Cities, Volume 8, Issue 4 (August 2025) – 25 articles

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue in recent years, is lacking. The team proposes a traffic prediction method that leverages K-means clustering, long short-term memory (LSTM) neural network, and Fourier transform (FT) for long-term traffic prediction. The proposed method was evaluated on a real-world dataset from the U.S. Travel Monitoring Analysis System (TMAS) database, which enhances practical relevance and potential impact on transportation planning and management. The forecasting performance is evaluated with real-world traffic flow data in the state of California, in the western USA. Results show good forecasting accuracy on traffic trends and counts over a one-year period, capturing periodicity and variation. Full article

Urban socioeconomic modeling has predominantly concentrated on extensive location and neighborhood-based features, relying on the localized population footprint. However, networks in urban systems are common, and many urban modeling methods do not account for network-based effects. Additionally, network-based research has explored a multitude of data from urban landscapes. However, achieving a comprehensive understanding of urban mobility proves challenging without exhaustive datasets. In this study, we propose using commute information records from the census as a reliable and comprehensive source to construct mobility networks across cities. Leveraging deep learning architectures, we employ these commute networks across U.S. metro areas for socioeconomic modeling. We show that mobility network structures provide significant predictive performance without considering any node features. Consequently, we use mobility networks to present a supervised learning framework to model a city’s socioeconomic indicator directly, combining Graph Neural Network and Vanilla Neural Network models to learn all parameters in a single learning pipeline. In experiments in 12 major U.S. cities, the proposed model achieves considerable explanatory performance and is able to outperform previous conventional machine learning models based on extensive regional-level features. Providing researchers with methods to incorporate network effects in urban modeling, this work also informs stakeholders of wider network-based effects in urban policymaking and planning. Full article

Society’s adaptation to shared mobility services is a growing topic that requires detailed understanding of the local circumstances of potential and current users. This paper focuses on analyzing barriers to the adoption of urban bike-sharing systems in post-industrial cities, using a case study of the Silesian agglomeration in Poland. Methodologically, the article integrates quantitative survey methods with multivariate statistical analysis to analyze the demographic, socioeconomic, and motivational factors that underline the adoption of shared micromobility. The study highlights a detailed segmentation of users by income, age, professional status, and gender, as well as the observation of profound disparities in access and perceived usefulness. Of note is the study’s identification of a highly concentrated segment of young, low-income users (mostly students), which largely accounts for the general perception of economic and infrastructural barriers. These include the use of factor analysis and regression to plot the interaction patterns between individual user characteristics and certain system-level constraints, such as cost, infrastructure coverage, weather, and health. The study’s findings prioritize problem-specific interventions in urban mobility planning: bridging equity gaps between user groups. This research contributes to the current literature by providing detailed insights into the heterogeneity of user mobility behavior, offering evidence-based recommendations for inclusive and adaptive options for shared transportation infrastructure in a changing urban context. Full article

This study explores digital inequality as a socio-spatial phenomenon within the context of smart inclusion, focusing on the Regional Unit of Xanthi, Greece—a region marked by ethno-cultural diversity and pronounced urban–rural contrasts. Using a mixed-methods design, this research integrates secondary quantitative data with qualitative insights from semi-structured interviews, aiming to uncover how spatial, demographic, and cultural variables shape digital engagement. Geographic Information System (GIS) tools are employed to map disparities in internet access and ICT infrastructure, revealing significant gaps linked to geography, education, and economic status. The findings demonstrate that digital inequality is particularly acute in rural, minority, and economically marginalized communities, where limited infrastructure intersects with low digital literacy and socio-economic disadvantage. Interview data further illuminate how residents navigate exclusion, emphasizing generational divides, perceptions of technology, and place-based constraints. By bridging spatial analysis with lived experience, this study advances the conceptualization of digitally inclusive smart regions. It offers policy-relevant insights into how territorial inequality undermines the goals of smart development and proposes context-sensitive interventions to promote equitable digital participation. The case of Xanthi underscores the importance of integrating spatial justice into smart city and regional planning agendas. Full article

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $4–$4.8 billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes. Full article

The central role of heating and cooling in energy transition has been recognised in recent years, especially with geopolitical developments since February 2022 which demand an acceleration in deploying local energy sources to increase the resilience of the energy sector. Geothermal energy is a promising and vital option to optimize heating and cooling systems, promoting sustainability of urban environments. To this end, a proper design is of paramount importance to guarantee the energy performance of the whole system. This work deals with the optimization of the technical and geometrical characteristics of borehole heat exchangers (BHEs) as part of a shallow geothermal plant that is assumed to be integrated in an already operating gas-fired DH grid. Thermal performances of three different configurations were analysed according to the geological information that revealed an aquifer at −36 m overlying a poorly permeable marly succession. Numerical simulations validated the geological, hydrogeological, and thermo-physical models by back-analysing the experimental results of a thermal response test (TRT) on a pilot 150 m deep BHE. Five-year simulations were then performed to compare 150 m and 36 m polyethylene 2U, and 36 m steel coaxial BHEs. The coaxial configuration shows the best performance both in terms of specific power (74.51 W/m) and borehole thermal resistance (0.02 mK/W). Outcomes of the study confirm that coupling the best geological and technical parameters ensure the best energy performance and economic sustainability. Full article

Understanding automated vehicle (AV) behavior in complex road environments and user attitudes in such contexts is critical for their safe and effective integration into smart cities. Despite growing deployment, limited public data exist on AV performance in construction zones; highly dynamic settings marked by irregular lane markings, shifting detours, and unpredictable human presence. This study investigates AV behavior in these conditions through qualitative, video-based analysis of user-documented experiences on YouTube, focusing on Tesla’s supervised Full Self-Driving (FSD) and Waymo systems. Spoken narration, captions, and subtitles were examined to evaluate AV perception, decision-making, control, and interaction with humans. Findings reveal that while AVs excel in structured tasks such as obstacle detection, lane tracking, and cautious speed control, they face challenges in interpreting temporary infrastructure, responding to unpredictable human actions, and navigating low-visibility environments. These limitations not only impact performance but also influence user trust and acceptance. The study underscores the need for continued technological refinement, improved infrastructure design, and user-informed deployment strategies. By addressing current shortcomings, this research offers critical insights into AV readiness for real-world conditions and contributes to safer, more adaptive urban mobility systems. Full article

Urbanization is accompanied by an increased use of building materials. However, the lack of high-resolution building material stock (BMS) maps limits our understanding of the relationship between BMS and urban heat. To address this, we estimated BMS across eight typical Chinese megacities using multi-source geographic data and investigated the relationship between BMS and land surface temperature (LST). The results showed that (1) the total BMS for the eight megacities was 9175.07 Mt, with Beijing and Shanghai having the largest shares. While BMS correlated significantly with population, growth patterns varied across cities. (2) Spatial autocorrelation between BMS and LST was evident. Around 16% of urban areas exhibited High–High clustering between BMS and LST, decreasing to 10% during the daytime. The relationship between BMS and LST is nonlinear, and also prominent at night, especially in Beijing. (3) Diverse building forms, especially building height, contribute to a nonlinear relationship between BMS and LST. Full article

Smart cities use advanced infrastructure and technology to improve the quality of life for their citizens. Collaborative services in smart cities are making the smart city ecosystem more reliable. These services are required to enhance the operation of interoperable systems, such as smart transportation services that share their data with smart safety services to execute emergency response, surveillance, and criminal prevention measures. However, an important issue in this ecosystem is data security, which involves the protection of sensitive data exchange during the interoperability of heterogeneous smart services. Researchers have addressed these issues through blockchain integration and the implementation of smart contracts, where collaborative applications can enhance both the efficiency and security of the smart city ecosystem. Despite these facts, complexity is an issue in smart contracts since complex coding associated with their deployment might influence the performance and scalability of collaborative applications in interconnected systems. These challenges underscore the need to optimize smart contract code to ensure efficient and scalable solutions in the smart city ecosystem. In this article, we propose a new framework that integrates generative AI with blockchain in order to eliminate the limitations of smart contracts. We make use of models such as GPT-2, GPT-3, and GPT4, which natively can write and optimize code in an efficient manner and support multiple programming languages, including Python 3.12.x and Solidity. To validate our proposed framework, we integrate these models with already existing frameworks for collaborative smart services to optimize smart contract code, reducing resource-intensive processes while maintaining security and efficiency. Our findings demonstrate that GPT-4-based optimized smart contracts outperform other optimized and non-optimized approaches. This integration reduces smart contract execution overhead, enhances security, and improves scalability, paving the way for a more robust and efficient smart contract ecosystem in smart city applications. Full article

Indonesia’s commitment to digital transformation is exemplified by the Gerakan 100 Smart City program, aiming to enhance public sector performance through technology integration. This study examines how information technology capability and 21st century digital skills influence public sector performance, mediated by managerial digital readiness and moderated by digital leadership. Grounded in Dynamic Capability Theory and Upper Echelon Theory, data from 1380 civil servants were analyzed using PLS-SEM via SmartPLS 4.1.0.9. Results show that both IT capability and digital skills significantly improve managerial digital readiness, which in turn positively impacts public sector performance. Managerial readiness mediates the effect of both predictors on performance, while digital leadership strengthens these relationships. Theoretically, this study frames managerial digital readiness as a dynamic capability shaped by leadership cognition. Practically, it highlights the importance of aligning infrastructure, skills, and leadership development to advance digital governance. Future research should consider longitudinal, multilevel, and qualitative designs to deepen insights. Full article

Smart cities necessitate ultra-secure and scalable communication frameworks to manage billions of interconnected IoT devices, particularly in the face of the emerging quantum computing threats. This paper proposes the QESIF, a novel Quantum-Enhanced Secure IoT Framework that integrates Quantum Key Distribution (QKD) with classical IoT infrastructures via a hybrid protocol stack and a quantum-aware intrusion detection system (Q-IDS). The QESIF achieves high resilience against eavesdropping by monitoring quantum bit error rate (QBER) and leveraging entropy-weighted key generation. The simulation results, conducted using datasets TON IoT, Edge-IIoTset, and Bot-IoT, demonstrate the effectiveness of the QESIF. The framework records an average QBER of 0.0103 under clean channels and discards over 95% of the compromised keys in adversarial settings. It achieves Attack Detection Rates (ADRs) of 98.1%, 98.7%, and 98.3% across the three datasets, outperforming the baselines by 4–9%. Moreover, the QESIF delivers the lowest average latency of 20.3 ms and the highest throughput of 868 kbit/s in clean scenarios while maintaining energy efficiency with 13.4 mJ per session. Full article

Logistics transports of various resources such as production materials, foods, and products support the operation of smart cities. The ability to trace the states of logistics transports requires an efficient storage and retrieval of the states of logistics transports and locations of logistics objects. However, the restriction of sharing states and locations of logistics objects across organizations makes it hard to deploy a centralized database for supporting traceability in a cross-organization logistics system. This paper proposes a semantic data model on Blockchain to represent a logistics process based on the Semantic Link Network model, where each semantic link represents a logistics transport of a logistics object between two organizations. A state representation model is designed to represent the states of a logistics transport with semantic links. It enables the locations of logistics objects to be derived from the link states. A mapping from the semantic links into the blockchain transactions is designed to enable the schema of semantic links and the states of semantic links to be published in blockchain transactions. To improve the efficiency of tracing a path of semantic links on a blockchain platform, an algorithm is designed to build shortcuts along the path of semantic links to enable a query on the path of a logistics object to reach the target in logarithmic steps on the blockchain platform. A reward–penalty policy is designed to allow participants to confirm the states of links on the blockchain. Analysis and simulation demonstrate the flexibility, effectiveness, and efficiency of the Semantic Link Network on immutable blockchain for implementing logistics traceability. Full article

This paper presents a novel system designed to enhance pedestrian safety in urban environments by utilizing real-time video analysis and machine learning techniques. With a focus on the bustling streets of Macao, known for its high pedestrian traffic and complex road conditions, the proposed model alerts drivers to the presence of pedestrians, significantly reducing the risk of accidents. Leveraging the You Only Look Once algorithm, this research demonstrates how timely alerts can be generated based on risk assessments derived from video footage. The model is rigorously tested against diverse driving scenarios, providing robust accuracy in detecting potential hazards. A comparative analysis of various machine learning algorithms, including Gradient Boosting and Logistic Regression, underscores the effectiveness and reliability of the system. The key finding of this research indicates that dataset refinement and enhanced feature differentiation could lead to improved model performance. Ultimately, this work seeks to contribute to the development of smart city initiatives that prioritize safety through advanced technological solutions. This approach exemplifies a vision for more responsive and responsible urban transport systems. Full article

Smart governance—the integration of digital technologies into urban governance—is increasingly recognized as a transformative approach to addressing complex urban challenges such as rapid urbanization, climate change, social inequality, and resource constraints. As a foundational pillar of the smart city paradigm, it enhances decision-making, service delivery, transparency, and civic participation through data-driven tools, digital platforms, and emerging technologies such as AI, IoT, and blockchain. While often positioned as a pathway toward sustainability and inclusivity, existing research on smart governance remains fragmented, particularly regarding its relationship to urban sustainability. This study addresses that gap through a systematic literature review using the PRISMA methodology, synthesizing theoretical models, empirical findings, and diverse case studies. It identifies key enablers—such as digital infrastructure, data governance, citizen engagement, and institutional capacity—and highlights enduring challenges including digital inequity, data security concerns, and institutional inertia. In response to this, the study proposes a multidimensional framework that integrates governance, technology, and sustainability, offering a holistic lens through which to understand and guide urban transformation. This framework underscores the importance of balancing technological innovation with equity, resilience, and inclusivity, providing actionable insights for policymakers and planners navigating the complexities of smart cities and urban development. By aligning smart governance practices with the United Nations’ sustainable development goals (SDG)—particularly SDG 11 on sustainable cities and communities—the study offers a strategic roadmap for fostering resilient, equitable, and digitally empowered urban futures. Full article

The increased uptake of Electric Vehicles (EVs) requires the installation of charging stations in parking lots, both to facilitate charging while running daily errands and to support EV owners with no access to home charging. Photovoltaic (PV) generation is ideal for powering up EVs, both for environmental reasons and for the benefit it creates for Charging Point Operators (CPOs). In this paper, we propose a centralised V1G Smart Charging (SC) algorithm for EV parking lots, considering real EV charging dynamics, which minimises both the EV charging costs for their owners and the CPO electricity provision costs or the related CO₂ emissions. We also introduce an innovative SC benefit-splitting algorithm that makes sure SC savings are fairly split between EV owners. Eight scenarios are described, considering costs or emissions minimisation, with and without a PV system. The centralised algorithm is benchmarked against a decentralised one, and tested in an exemplary workplace parking lot in Denmark, that includes includes 12 charging stations and one PV system, owned by the same entity. Reductions of up to 11% in EV charging costs, 67% in electricity provision costs for the CPO, and 8% in CO₂ emissions are achieved by making smart use of a 35 kWp rooftop PV system. Additionally, the SC benefit-splitting algorithm successfully ensures that EV owners save money when adopting SC. Full article

This paper explores the intersection of blockchain technology and smart cities to support the transition toward decentralized, secure, and sustainable urban systems. Drawing on co-word analysis and BERTopic modeling applied to the literature published between 2016 and 2025, this study maps the thematic and technological evolution of blockchain in urban environments. The co-word analysis reveals blockchain’s foundational role in enabling secure and interoperable infrastructures, particularly through its integration with IoT, edge computing, and smart contracts. These systems underpin critical urban services such as transportation, healthcare, energy trading, and waste management by enhancing data privacy, authentication, and system resilience. The application of BERTopic modeling further uncovers a shift from general technological exploration to more specialized and sector-specific applications. These include real-time mobility systems, decentralized healthcare platforms, peer-to-peer energy exchanges, and blockchain-enabled drone coordination. The results demonstrate that blockchain increasingly supports cross-sectoral innovation, enabling transparency, trust, and circular flows in urban systems. Overall, the current study identifies blockchain as both a technological backbone and an ethical infrastructure for smart cities that supports secure, adaptive, and sustainable urban development. Full article

In the context of intelligent and fine-grained urban governance, the coordinated configuration of spatial capacity and locational value has become a key proposition for optimizing urban resource utilization. Using Shanghai as a case study, this paper represents spatial capacity with population density and locational value with land values. By quantifying the degree of spatial mismatch between population density and land values, the study reveals the imbalance between spatial capacity and locational value. First, the research calibrates the population grid data to obtain the population distribution within the study area; subsequently, a composite land value prediction model is constructed to compute the land value distribution across the study region; finally, the spatial mismatch index is calculated using the regression residual method to quantify the degree to which population density deviates from land values. The results indicate that there is a significant spatial mismatch between population density and land values in Shanghai, which unveils an imbalance in the allocation of spatial capacity within the city. This framework can be integrated into smart city digital twins and real-time monitoring platforms, providing 100 m resolution decision support for spatial resource optimization. Full article

Climate change presents serious challenges for infrastructure, regional planning, and public awareness. However, effectively understanding and analyzing large-scale climate discussions remains difficult. Traditional methods often struggle to extract meaningful insights from unstructured data sources, such as social media discourse, making it harder to track climate-related concerns and emerging trends. To address this gap, this study applies Natural Language Processing (NLP) techniques to analyze large volumes of climate-related data. By employing supervised and weak supervision methods, climate data are efficiently labeled to enable targeted analysis of regional- and infrastructure-specific climate impacts. Furthermore, BERT-based Named Entity Recognition (NER) is utilized to identify key climate-related terms, while sentiment analysis of platforms like Twitter provides valuable insights into trends in public opinion. AI-driven visualization tools, including predictive modeling and interactive mapping, are also integrated to enhance the accessibility and usability of the analyzed data. The research findings reveal significant patterns in climate-related discussions, supporting policymakers and planners in making more informed decisions. By combining AI-powered analytics with advanced visualization, the study enhances climate impact assessment and promotes the development of sustainable, resilient infrastructure. Overall, the results demonstrate the strong potential of AI-driven climate analysis to inform policy strategies and raise public awareness. Full article

The increasing demand for IoT solutions in smart cities, coupled with the increasing use of sensors and actuators and automation in these environments, has highlighted the need for efficient communication between Internet of Things (IoT) devices. The success of such systems relies on interactions between devices that are governed by communication protocols which define how information is exchanged. However, the heterogeneity of sensor networks (wired and wireless) often leads to incompatibility issues, hindering the seamless integration of diverse devices. To address these challenges, standardisation is essential to promote scalability and interoperability across IoT systems. The IEEE 1451 standard provides a solution by defining a common interface that enables plug-and-play integration and enhances flexibility across diverse IoT devices. This standard enables seamless communication between devices from different manufacturers, irrespective of their characteristics, and ensures compatibility via the Transducer Electronic Data Sheet (TEDS) and the Network Capable Application Processor (NCAP). By reducing system costs and promoting adaptability, the standard mitigates the complexities posed by heterogeneity in IoT systems, fostering scalable, interoperable, and cost-effective solutions for IoT systems. The IEEE 1451 standard addresses key barriers to system integration, enabling the full potential of IoT technologies. This paper aims to provide a comprehensive review of the challenges transducer networks face around IoT applications, focused on the context of smart cities. This review underscores the significance and potential of the IEEE 1451 standard in establishing a framework that enables the harmonisation of IoT applications. The primary contribution of this work lies in emphasising the importance of adopting the standards for the development of harmonised and flexible systems. Full article

Urban transportation systems face increasing pressure to reduce car dependency and greenhouse gas emissions while supporting sustainable growth. This study addresses the lack of integrated modeling approaches that capture both spatial and temporal dynamics in transport planning. It develops a novel framework combining System Dynamics (SD) and Geographic Information Systems (GIS) to assess the sustainability of Transit-Oriented Development (TOD) strategies and parking policies in two brownfield redevelopment sites in Montreal. The framework embeds spatial metrics, such as proximity to transit, parking availability, and active transportation infrastructure into dynamic feedback loops. Using scenario analysis, the study compares a baseline reflecting current norms with an intervention scenario emphasizing higher density near transit, reduced parking ratios, and improved walkability and bike infrastructure. The results suggest that aligning TOD principles with targeted parking limits and investments in active mobility can substantially reduce car ownership and emissions. While primarily conceptual, the model provides a foundation for location-sensitive, feedback-driven planning tools that support sustainable urban mobility. Full article

Achieving a climate neutral economy by 2050 in Europe in line with the European Green Deal places specific responsibility on the transportation sector, which contributes to greenhouse gas (GHG) emissions. For the transportation domain to reduce its GHG emissions, there is need to advance urban mobility solutions in local communities via the use of data in all modes of transportation. Accordingly, to intelligently improve mobility solutions, huge amounts of data are needed from citizens in local communities to improve mobility services. However, the access, usage, and ownership of data in the transportation sector continue to be hindered due to issues including privacy, security, and trust concerns, among others. However, to improve smarter mobility solutions, there is a need for clarification of digital sovereignty, which today hinders data flow among different actors in the transportation sector. Therefore, research is needed to provide an approach that enables digital sovereignty while providing innovative mobility services and applications to citizens. Accordingly, this article carried out a systematic review to explore how to maintain digital sovereignty to improve urban mobility services in local communities. Based on grounded theory and a literature review, this study explores the factors that influence digital sovereignty from local communities’ point of view. More importantly, a policy framework is proposed to improve sovereign data usage control for citizens. Additionally, recommendations for achieving digital sovereignty are presented to foster data ecosystem business opportunities for mobility service providers and to increase data autonomy, trust, and transparency for citizens. Full article

Reliable wireless communication is essential for mobile robotic systems operating in dynamic environments, particularly in the context of smart mobility and cloud-integrated urban infrastructures. This article presents an experimental study analyzing the impact of robot motion dynamics on wireless network performance, contributing to the broader discussion on data reliability and communication efficiency in intelligent transportation systems. Measurements were conducted using a quadruped robot equipped with an onboard edge computing device, navigating predefined trajectories in a laboratory setting designed to emulate real-world variability. Key wireless parameters, including signal strength (RSSI), latency, and packet loss, were continuously monitored alongside robot kinematic data such as speed, orientation (roll, pitch, yaw), and movement patterns. The results show a significant correlation between dynamic motion—especially high forward velocities and rotational maneuvers—and degradations in network performance. Increased robot speeds and frequent orientation changes were associated with elevated latency and greater packet loss, while static or low-motion periods exhibited more stable communication. These findings highlight critical challenges for real-time data transmission in mobile IoRT (Internet of Robotic Things) systems, and emphasize the role of network-aware robotic behavior, interoperable communication protocols, and edge-to-cloud data integration in ensuring robust wireless performance within smart city environments. Full article

Carsharing has long supported trip purposes typically made by private vehicles, with grocery shopping especially benefiting from the carrying capacity of a personal vehicle. BlueLA is a one-way, station-based electric vehicle (EV) carsharing service in Los Angeles aimed at improving access in low-income neighborhoods. We hypothesize that BlueLA improves grocery access for underserved households by increasing their spatial-temporal reach to diverse grocery store types. We test two hypotheses: (1) accessibility from BlueLA stations to grocery stores varies by store type, traffic conditions, and departure times; and (2) Standard (general population) and Community (low-income) members differ in perceived grocery access and station usage. Using a mixed-methods approach, we integrate walking and driving isochrones, store data (n = 5888), trip activity data (n = 59,112), and survey responses (n = 215). Grocery shopping was a key trip purpose, with 69% of Community and 61% of Standard members reporting this use. Late-night grocery access is mostly limited to convenience stores, while roundtrips to full-service stores range from 55 to 100 min and cost USD 12 to USD 20. Survey data show that 84% of Community and 71% of Standard members reported improved grocery access. The findings highlight the importance of trip timing and the potential for carsharing and retail strategies to improve food access. Full article

This study evaluates deep learning models for vehicle detection in urban environments, focusing on the integration of regional data and standardized evaluation protocols. A central contribution is the creation of DebStreet, a novel dataset that captures images from a specific urban setting under varying weather conditions, providing regionally representative information for model development and evaluation. Using DebStreet, four state-of-the-art architectures were assessed: Faster R-CNN, YOLOv8, DETR, and Side-Aware Boundary Localization (SABL). Notably, SABL and YOLOv8 demonstrated superior precision and robustness across diverse scenarios, while DETR showed significant improvements with extended training and increased data volume. Faster R-CNN also proved competitive when carefully optimized. These findings underscore how the combination of regionally representative datasets with consistent evaluation methodologies enables the development of more effective, adaptable, and context-aware vehicle detection systems, contributing valuable insights for advancing intelligent urban mobility solutions. Full article

The rapid adoption of electric vehicles (EVs) and the increasing use of photovoltaic (PV) generation have introduced new operational challenges for unbalanced power distribution systems. These include elevated power losses, voltage imbalances, and adverse environmental impacts. This study proposed a hybrid objective optimization framework to address these issues by minimizing real and reactive power losses, voltage deviations, voltage imbalance indexes, and CO₂ emissions. Nineteen simulation cases were analyzed under various configurations incorporating EV integration, PV deployment, reactive power compensation, and zonal control strategies. An improved gray wolf optimizer (IGWO) was employed to determine optimal placements and control settings. Among all cases, Case 16 yielded the lowest objective function value, representing the most effective trade-off between technical performance, voltage stability, and sustainability. The optimized configuration significantly improved the voltage balance, reduced system losses, and maintained the average voltage within acceptable limits. Additionally, all optimized scenarios achieved meaningful reductions in CO₂ emissions compared to the base case. The results were validated with an objective function $\left(F_{best}\right)$ as a reliable composite performance index and demonstrated the effectiveness of coordinated zone-based optimization. This approach provides practical insights for future smart grid planning under dynamic, renewable, rich, and EV-dominated operating conditions. Full article