Search Results (4,109)

The Internet of Things refers to a network of interconnected devices, objects, and systems, that can interact with one another without human intervention. The adoption of IoT technology has expanded rapidly, significantly impacting various fields, including smart healthcare, intelligent transportation, agriculture, and smart homes. This paper focuses on smart street lighting, which represents the core piece of the smart city and the key public service for citizens’ safety. Nevertheless, it poses substantial challenges related to energy consumption, especially during energy crises. This work aims to provide an advanced solution that enables intelligent control of street lighting, enhances human safety, reduces CO₂ emissions and light pollution, and optimizes energy consumption, as well as facilitates maintenance of the lighting network. The solution is twofold: First, it introduces IoT-based smart street lighting referential models; second, it presents a framework for controlling smart street lighting based on the referential models. The proposal uses an IoT-based fuzzy multi-agent systems approach to address the challenges of smart street lighting. The approach leverages the strengths and properties of fuzzy logic and multi-agent systems to address the system requirements. This is illustrated through a testbed case study conducted on a concrete IoT prototype. Full article

This paper focuses on studying the concept of walkability in Riyadh, Saudi Arabia, after the implementation of the Humanizing Neighborhoods Initiative. Al-Falah neighborhood was selected as a case study because it was one of the first neighborhoods to implement walkability strategies. By adopting a qualitative critical approach, this research collected data through a literature review and on-site observation to assess walkability in the neighborhood. Analysis of the observations highlighted the challenges and opportunities of walkability in Riyadh and the wider Gulf cities. Recommendations for promoting walkable communities in Riyadh include enhancing pedestrian infrastructure, incorporating mixed-use developments, integrating green infrastructure, implementing smart urban planning principles, and integrating public transportation systems. This paper emphasizes the importance of community engagement, preserving local identity, and involving various stakeholders to transform Riyadh into a pedestrian-friendly city that prioritizes public health and sustainability. Full article

Autonomous vehicles (AVs) represent a transformative advancement in transportation technology, promising to enhance travel efficiency, reduce traffic accidents, and revolutionize our road systems. Central to the operation of AVs is the integration of artificial intelligence (AI), which enables these vehicles to navigate complex environments with minimal human intervention. This review critically examines the potential dangers associated with the increasing reliance on AI in AV navigation. It explores the current state of AI technologies, highlighting key techniques such as machine learning and neural networks, and identifies significant challenges including technical limitations, safety risks, and ethical and legal concerns. Real-world incidents, such as Uber’s fatal accident and Tesla’s crash, underscore the potential risks and the need for robust safety measures. Future threats, such as sophisticated cyber-attacks, are also considered. The review emphasizes the importance of improving AI systems, implementing comprehensive regulatory frameworks, and enhancing public awareness to mitigate these risks. By addressing these challenges, we can pave the way for the safe and reliable deployment of autonomous vehicles, ensuring their benefits can be fully realized. Full article

This study aims to scientifically justify the identification of suitable urban properties for urban green infrastructure (UGI) interventions to optimize its natural regulating functions for long-term pollution mitigation and secondary dust reduction. This study adheres to the perception that planning urban transformations to improve ambient air quality (AQ) requires a thorough understanding of urban structural heterogeneity and its interrelationship with the local microclimate. We apply an approach in which UGI and its potential multifunctionality are explored as a structural–functional element of urban local climatic zones. The same (100 × 100 m) spatial framework is used to develop place-based adapted solutions for intervention in UGI. A complex geospatial analysis of Burgas City, the second largest city (by area) in Bulgaria, was conducted by integrating 12 indicators to reveal the spatial disbalance of AQ regulation’ demand and UGI’s potential to supply ecosystem services. A total of 174 municipally owned properties have been identified, of which 79 are of priority importance, including for transport landscaping, inner-quarter spaces, and social infrastructure. Indicators of population density and location of social facilities were applied with the highest weight in the process of prioritizing sites. The study relies on public data and information from the integrated city platform of Burgas, in cooperation with the city’s government. The results have been discussed with stakeholders and implemented by the Municipality of Burgas in immediate greening measures in support of an ongoing program for Burgas Municipality AQ improvement. Full article

With the growing demand and projected shortage of rare earth elements in the near future, the urgent task of developing energy-efficient electrical equipment with less dependence on rare earth magnets has become paramount. The use of permanent magnet-assisted synchronous reluctance motors (PMaSynRMs), which reduce the consumption of rare earth magnets, can help solve this problem. This article presents a theoretical analysis of the characteristics of PMaSynRM in a subway train drive. Options with rare earth and ferrite magnets are considered. Optimization of the motor designs considering the train movement cycle is carried out using the Nelder-Mead method. Characteristics of the motors, such as losses, torque ripple, and inverter power rating, as well as the mass and cost of active materials, are compared. Full article

Carbon emission reduction strategies are being implemented in the transportation sector by encouraging the adoption of eco-friendly vehicles and introducing demand management policies such as Mobility as a Service (MaaS). Nevertheless, the efficacy of MaaS in reducing carbon emissions remains uncertain. This study introduces Sustainable Public Transit (SPT) as a public transit alternative consisting of only green modes to promote sustainability. We explore the preferences of SPT in a commuting context, incorporating individual preference heterogeneity in a discrete choice model. We systematically identify the relationship between choice behaviors and individual heterogeneity in alternative attributes and psychological factors stemming from socio-demographic characteristics. The integrated choice and latent variable (ICLV) model with a mixed logit form is adopted, and the key findings can be summarized as follows: Preference heterogeneity is observed in the travel cost variable, which can be explained by characteristics such as the presence of a preschooler, household size, and income. CO₂ emissions do not have a statistically significant impact on choices. Furthermore, psychological factors are also explained through socio-demographic characteristics, and it is found that low-carbon knowledge positively influences low-carbon habits. Psychological factors significantly affect choices. Respondents who dislike transfers and prioritize punctuality are less likely to choose SPT, while those who have positive low-carbon attitudes are more likely to do so. Finally, scenario analysis is conducted to forecast mode share based on improvements in SPT alternative attributes and variations in attribute levels. Policy implications are then provided to enhance the acceptability of SPT. Full article

Lifts, or elevators, are transportation facilities that are indispensable for countless end users in high-rise buildings. They require proper maintenance to ensure safe operation. In addition to technological applications, effective management and legislative controls play a crucial role in ensuring lift safety. Given the limited understanding of an optimal regulatory model for governing lift maintenance, a cross-discipline comparative study was conducted to examine lift maintenance regulations in regions with different legal systems. Following a systematic and comparative review approach, this study focused on regulatory controls across civil and common law jurisdictions, specifically Beijing, Hong Kong, and London. Relevant statutes and publications were searched from engineering, law and management databases, which included Scopus, JSTOR, Lexis+, Lexis China, Lexis Advance Hong Kong, and Westlaw Asia. Through scrutinizing the retrieved documents, key features of the regulations were identified and compared in terms of lift classifications, types and frequencies of mandatory maintenance works, qualifications for authorized parties, and legal liabilities for non-compliance. Validated by industry experts, the results reveal both similarities and differences in the regulations among the three jurisdictions. While these findings serve as valuable references for policymakers in formulating optimal legislative controls to enhance lift safety in the future, further research could expand the scope of this study to examine the regulations in other regions and investigate the effectiveness of existing statutory controls on lift maintenance. Full article

Urban rail transit, a convenient and fast public transportation mode with rapid construction and development, occupies fewer land resources and accommodates large passenger volumes. However, thermal comfort should be given more attention. Stations above ground level experience poor thermal comfort on the platforms, especially in hot summers. This study combines field research with a simulation analysis to propose a strategy for improving thermal comfort on above-ground urban rail transit platforms. This study analyzed the effects of the skylight opening rate, side window opening rate, design of transparent maintenance structure shading, and the platform profile shape on the thermal comfort of above-ground stations using field research, comparative experiments, and a simulation analysis with the PHOENICS (Command Prompt) software. The results indicate that adding longitudinal sunshade louvers to the skylight of the station platform is a cost-effective method to reduce the average temperature and PMV value, thereby improving thermal comfort. Increasing the skylight opening rate can result in a temperature rise. Adjusting the opening rate of the side windows to 20% and adding sun-shading louvers can also significantly enhance the station’s thermal comfort. Taking Wudaokou Station on Beijing Line 13 as an example, the simultaneous installation of additional longitudinal skylight shading and side window shading and increasing the side window opening rate to 20% on the platform resulted in a 2.6 °C decrease in the average temperature, a 4.7% increase in the average wind speed, and a 0.62 decrease in the PMV value, significantly enhancing thermal comfort for passengers. This study confirms that optimizing shading and ventilation systems can significantly reduce the platform temperature and improve passengers’ thermal comfort. This study provides theoretical support and innovative methods for optimizing thermal environments in similar environments. Full article

The coastal zone is constantly exposed to marine erosion, rising water levels, waves, tides, sea currents, and debris transport. As a result, there are dynamic changes in the coastal zone topography, which may have negative effects on the aquatic environment and humans. Therefore, in order to monitor the changes in landform taking place in the coastal zone, periodic bathymetric and photogrammetric measurements should be carried out in an appropriate manner. The aim of this review is to develop a methodology for performing bathymetric and photogrammetric measurements using an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) in a coastal zone. This publication shows how topographic and bathymetric monitoring should be carried out in this type of zone in order to obtain high-quality data that will be used to develop a Digital Terrain Model (DTM). The methodology for performing photogrammetric surveys with the use of a drone in the coastal zone should consist of four stages: the selection of a UAV, the development of a photogrammetric flight plan, the determination of the georeferencing method for aerial photos, and the specification as to whether there are meteorological conditions in the studied area that enable the implementation of an aerial mission through the use of a UAV. Alternatively, the methodology for performing bathymetric measurements using a USV in the coastal zone should consist of three stages: the selection of a USV, the development of a hydrographic survey plan, and the determination of the measurement conditions in the studied area and whether they enable measurements to be carried out with the use of a USV. As can be seen, the methodology for performing bathymetric and photogrammetric measurements using UAV and USV vehicles in the coastal zone is a complex process and depends on many interacting factors. The correct conduct of the research will affect the accuracy of the obtained measurement results, the basis of which a DTM of the coastal zone is developed. Due to dynamic changes in the coastal zone topography, it is recommended that bathymetric measurements and photogrammetric measurements with the use of UAV and USV vehicles should be carried out simultaneously on the same day, before or after the vegetation period, to enable the accurate measurement of the shallow waterbody depth. Full article

This paper examines how spatial dynamics have impacted natural capital and the provision of ecosystem services. The units outlined by the National Strategy for Inland Areas (SNAI) have been used as the territorial units for this study. The SNAI is a public policy focused on enhancing the quality of services to citizens (such as transportation, healthcare, and education). It proposes the economic revitalization of inland areas undergoing processes of marginalization. Our focus on inland areas stems from two primary reasons: first, no previous studies in Italy have analyzed the changes in ecosystem services in SNAI areas; and second, SNAI areas are well-suited to providing ecosystem services that are in demand by urban areas. Although this study does not cover all aspects inherent to the topic, it represents a starting point aimed at understanding the links between environmental and socio-economic dynamics and ecosystem service changes. This is essential for both current and future generations. By analyzing the processes of permanence and transformation, modifications in the supply–demand balance have hereby been studied, as well as the economic variations in ecosystem services. The period considered runs from 1990 to 2018. These findings could help governmental institutions in developing sustainable governance models, in line with spatial policies and strategies. Full article

Coronavirus 2019 (COVID-19) is a major global public health threat that has impeded health infrastructures in low- and middle-income countries. This systematic review examines the impact of COVID-19 on maternal health service uptake and perinatal outcomes in Sub-Saharan Africa. We searched four databases in August 2020 and updated the search on 22 December 2023: PubMed/MEDLINE, CINAHL, Maternity and Infant Care, and EMBASE. Data extraction was performed using a standardised Joana Briggs Institute data extraction format for the eligibility of articles, and any discrepancies were solved through discussion and consensus. This systematic review includes 36 studies that met the inclusion criteria. Antenatal care attendance and institutional childbirth significantly decreased during the COVID-19 pandemic, and home births increased. Fear of contracting the virus, a lack of transport, a shortage of logistic supplies, a lack of personal protective equipment, lockdown policies, economic and food security, stigmatisation of sick persons, long waiting times in the hospital, and health system weakness were barriers to accessing maternity care. The findings of this review showed a significant decrease in antenatal care attendance and institutional birth during the COVID-19 pandemic. Based on our findings, we recommend that stakeholders ensure the availability of essential medical supplies in the hospital. Full article

As a fundamental element of the transportation system, traffic signs are widely used to guide traffic behaviors. In recent years, drones have emerged as an important tool for monitoring the conditions of traffic signs. However, the existing image processing technique is heavily reliant on image annotations. It is time consuming to build a high-quality dataset with diverse training images and human annotations. In this paper, we introduce the utilization of Vision–language Models (VLMs) in the traffic sign detection task. Without the need for discrete image labels, the rapid deployment is fulfilled by the multi-modal learning and large-scale pretrained networks. First, we compile a keyword dictionary to explain traffic signs. The Chinese national standard is used to suggest the shape and color information. Our program conducts Bootstrapping Language-image Pretraining v2 (BLIPv2) to translate representative images into text descriptions. Second, a Contrastive Language-image Pretraining (CLIP) framework is applied to characterize not only drone images but also text descriptions. Our method utilizes the pretrained encoder network to create visual features and word embeddings. Third, the category of each traffic sign is predicted according to the similarity between drone images and keywords. Cosine distance and softmax function are performed to calculate the class probability distribution. To evaluate the performance, we apply the proposed method in a practical application. The drone images captured from Guyuan, China, are employed to record the conditions of traffic signs. Further experiments include two widely used public datasets. The calculation results indicate that our vision–language model-based method has an acceptable prediction accuracy and low training cost. Full article

The functioning of modern urban environments relies heavily on the public transport system. Given spatial, economic, and sustainability criteria, public transport in larger urban areas is unrivaled. The system’s role depends on the quality of service it offers. Achieving the desired service quality requires a design that meets transport demands. This paper uses a data-driven approach to address headway deviations in public transport lines and explores ways to improve regularity during the design phase. Headway is a critical dynamic element for transport organization and passenger quality. Deviations between planned and actual headways represent disturbances. On lines with headways under 15 min, passengers typically do not consult schedules, making punctuality less crucial. Reduced headway regularity affects the average travel time, travel time uncertainty, and passenger comfort. Ideally, the public transport system operates with regular headways. However, disturbances can spread and affect subsequent departures, leading to vehicle bunching. While previous research focused on single primary disturbances, this study, with the help of AI (reinforcement learning), examines multiple primary disturbances in the cities of Belgrade, Novi Sad, and Niš. The goal is to model the cumulative impact of these disturbances on vehicle movement. By ranking parameter influences and using the automatic optimization of static line elements, this research aims to improve headway regularity and increase system resilience to disturbances. The results of this research could also be useful in developing adaptive public transport management systems that leverage AI and IoT technologies to continuously optimize headway regularity in response to real-time data, ultimately enhancing service quality and passenger satisfaction. Full article

Electric vehicles (EVs) have been widely considered an essential element to contribute to green and smart transportation, which will further enhance the development of smart cities. Hong Kong, as one of the largest metropolises in the world, has promoted the deployment of EVs for both the private and public transportation sectors over the past decade, with substantial financial subsidies and encouraging policy incentives. With the rapid penetration of EVs, especially in the market of private passenger cars, Hong Kong may face the challenge of insufficient charging facilities in the next few years. As such, the research study aims to develop a mathematical model using a topological method to map out feasible locations for new EV charging facilities on Ap Lei Chau Island, to construct a small Python program to optimize the mapping process of these feasible locations, and to estimate energy consumption and associated economic analysis to foster the spatial planning of EV charging facility networks. In conclusion, optimal locations for new charging facilities for EVs have been revealed to match the rapid growth of EV usage and facilitate the emergence of green and smart transportation. Full article

In the pursuit of sustainable mobility and the decarbonization of transport systems, public authorities are increasingly scrutinizing the impact of travel speed on emissions within both low-speed and high-speed environments. This study critically examines the evidence concerning emission impacts associated with speed limit changes in different traffic environments by conducting a systematic review of the literature in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 25 studies that met the eligibility criteria were assessed. The results reveal mixed evidence for reducing emissions through speed limit reductions in low-speed areas. However, emerging evidence suggests that reduced urban speeds may abate emissions through enhanced traffic flow and a shift in modal preferences away from personal vehicle use. Additionally, in urban areas, minor observed emission reduction per vehicle can add up to large overall reductions due to the high number of vehicles. In high-speed contexts, the evidence is much clearer, showing that reduced speed limits correlate with significant reductions in NOx, CO₂, and particulate matter emissions. The extent of these reductions is highly variable and contingent upon the specific speed limits or limit reductions, the local context, the vehicle type, and the baseline types and levels of pollutants. Notably, there is a lack of research on the effects of speed on emissions, especially in low- and middle-income countries (LMICs), highlighting a critical area for future investigation. The findings of this study underscore the potential environmental benefits of speed management policies and advocate for the promotion of smoother and less aggressive driving behavior to mitigate emissions and enhance sustainable mobility in both low-speed and high-speed settings. Full article