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The notion of the smart city involves embedding Industry 4.0 technologies to improve the lives of inhabitants in urban environments. Within this context, smart city data layers (SCDLs) concern the integration of extra tiers of information for the purposes of improving communication potential. Under the Industry 4.0 technology grouping, advanced communication technologies, such as virtual reality (VR), further the opportunities to model, recreate, evaluate and communicate scenarios that potentially improve citizens lives at multiple SCDL scales in a smart cities context. The use of added contextual information in SCDLs is of special interest for emergency planning situations at the building scale. In this research, a literature review to understand the current approaches for the use, development and evaluation of VR applications in the context of emergency planning was conducted. The results indicated four main categories of relevant challenges for these types of applications, for which recommendations and a roadmap for VR development are presented. In total, the study identified 10 commonly occurring challenges (e.g., optimization and discomfort) and 19 solution directions (e.g., model construction and spatial directions) in related articles when considering the development of VR for spatial planning and emergency situations. [ABSTRACT FROM AUTHOR]

Community outdoor public spaces are indispensable to urban residents' daily lives. Analyzing community outdoor public spaces from a behavioral perspective is crucial and an effective way to support human-centered development in urban areas. Traditional behavioral analysis often relies on manually collected behavioral data, which is time-consuming, labor-intensive, and lacks data breadth. With the use of sensors, the breadth of behavioral data has greatly increased, but its accuracy is still insufficient, especially in the fine-grained differentiation of populations and behaviors. Computer vision is more efficient in distinguishing populations and recognizing behaviors. However, most existing computer vision applications face some challenges. For example, behavior recognition is limited to pedestrian trajectory recognition, and there are few that recognize the diverse behaviors of crowds. In view of these gaps, this paper proposes a more efficient approach that employs computer vision tools to examine different populations and different behaviors, obtain important statistical measures of spatial behavior, taking the Bajiao Cultural Square in Beijing as a test bed. This population and behavior recognition model presents several improvement strategies: Firstly, by leveraging an attention mechanism, which emulates the human selective cognitive mechanism, it is capable of accentuating pertinent information while disregarding extraneous data, and the ResNet backbone network can be refined by integrating channel attention. This enables the amplification of critical feature channels or the suppression of irrelevant feature channels, thereby enhancing the efficacy of population and behavior recognition. Secondly, it uses public datasets and self-made data to construct the dataset required by this model to improve the robustness of the detection model in specific scenarios. This model can distinguish five types of people and six kinds of behaviors, with an identification accuracy of 83%, achieving fine-grained behavior detection for different populations. To a certain extent, it solves the problem that traditional data face of large-scale behavioral data being difficult to refine. The population and behavior recognition model was adapted and applied in conjunction with spatial typology analysis, and we can conclude that different crowds have different behavioral preferences. There is inconsistency in the use of space by different crowds, there is inconsistency between behavioral and spatial function, and behavior is concentrated over time. This provides more comprehensive and reliable decision support for fine-grained planning and design. [ABSTRACT FROM AUTHOR]

The integration of Industry 4.0 and automated vehicles into the smart cities concept is a topical issue in the urbanization of cities and technological innovation within cities. As it is a relatively modern issue, many aspects of this field have not yet been explored; as a consequence, this paper is concerned with the search for synergies between Industry 4.0 and automated vehicles in smart city infrastructures. There is a lack of contributions in this field that summarize these synergies in a single article and address a wide range of aspects, including transport, energy, communication, and citizen participation. As the field lacks a complete and clear summary of what is already known, which would help multiple stakeholders, the authors decided to conduct this review. The article elucidates the above-stated aspects through a clear and in-depth literature review, which is complemented by specific examples from practice. Of course, the article also includes a description of the synergy potential and the impact on the inhabitants, the environment, and, last but not least, on the overall city life. The main hypothesis of this article is that the integration of Industry 4.0 technologies and automated vehicles within smart city infrastructure will result in significant improvements in transportation efficiency, resource utilization, and overall urban sustainability. The article discusses the positives and negatives of such integration, highlighting, on the one hand, the benefits in terms of reducing environmental impact and improving citizens' quality of life, but on the other hand, also highlighting the various ethical, legal, and social issues that such integrations may bring. Several methods have been used within the article, namely analysis, synthesis, comparison, and historical interpretation. The final discussion highlights the benefits, as well as the challenges, that such integration faces and must deal with if it is to be successful. It can be concluded that the synergistic potential of automated vehicles and Industry 4.0 in smart city infrastructure is enormous and that such integration offers promising solutions for enhancing transportation efficiency, energy management, and overall urban sustainability. It is also highlighted in the article that, in order to reap the benefits of such synergies, a wide-ranging collaboration of policymakers, industry stakeholders, and urban planners is needed. [ABSTRACT FROM AUTHOR]

Featured Application: The road traffic noise predictive model presented in this paper can be calibrated without field measurements and can be applied in several traffic conditions, with an average error lower than 2 dBA. The assessment of road traffic noise is very important for the health of people living in urban areas. Noise is usually assessed by field measurements, and predictive models play an important role when experimental data are not available. Nevertheless, when they are based on regression techniques, predictive models suffer from the drawback of strong dependence on the calibration data. In this paper, the authors present a regressive model calibrated on computed noise levels without the need for field measurements. The independence from field measurements makes the model flexible and adjustable for any road traffic condition possible. A multilinear regression technique is applied to establish the correlation between the computed equivalent noise levels and several independent variables, including, among others, traffic flow and distance. The model is then validated on a large field measurement database to check its efficiency in terms of prediction accuracy. The validation is performed both via error distribution analysis and using different error metrics. The results are encouraging, showing that the model provides good results in terms of the average error (less than 2 dBA) and is not susceptible to the presence of outliers in the input data that correspond to unconventional conditions of the traffic flow. [ABSTRACT FROM AUTHOR]

Utilizing idle space for agricultural planting can generate production profits while greening the urban environment. How can the space available for agricultural planting in cities be inventoried and quantified, and what is the production potential? With the help of GIS, spatial analysis was carried out, and more objective conclusions were drawn through calculation. This paper takes the roof space in Shibei District of Qingdao as an example to study: First, we analyzed the influencing factors and inventory standards of roof agricultural planting; second, we selected inventory standards for idle roof space inventory and conducted statistical analysis of the data obtained from the inventory. Furthermore, combining the annual yield of vegetables per unit area and the annual consumption standards of vegetables by citizens, we calculated the yield and supply and demand; next, we summarized the research results, analyzed their reliability and the advantages and disadvantages of this inventory, and derived formulas for calculating the agricultural production potential of rooftops and urban spaces. Finally, we concluded that the idle roof area in cities and the potential for agricultural production are significant. The research results indicate that the roof area of urban buildings in the Shibei District of Qingdao is 383.3 hectares, with an annual vegetable production potential of 25,800 tons, which can supply the urban population of 184,300. Based on the above research, it is proposed that rooftop agriculture should be given priority in the future development of urban agriculture, and it is recommended that other urban spaces should be inventoried to promote the development of urban agriculture. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to improve energy efficiency and sustainability in urban environments. The ongoing climate change is causing increasingly important consequences for cities and their inhabitants. Temperatures are rising and human thermal comfort conditions are becoming worse. For this reason, it is essential to evaluate how parts of cities react to these phenomena and how they could improve their behavior. To do this, the area of interest has to be analyzed from various aspects, starting with an assessment of the microclimatic conditions. Through these analyses, it is possible to observe the interactions between climate and the urban context on a macro-scale. The actual results, such as surface temperature and air temperature, will be useful for hypothesizing where different paved surfaces need to be restored with mitigative actions. Another aspect that needs to be considered for a comprehensive analysis of the area's potential concerns the study of solar potential. We will describe how this topic was approached, making in-depth evaluations of the quality of the results obtained through the analysis of simplified models. The portability of these data within a spatial domain was also evaluated, integrating the values on a territorial database. Another important topic that needs to be analyzed to plan an improvement of an area in terms of energy production is the installation of new solar active production systems. The appropriate inclusion of photovoltaic panels could lead to the near self-sustainability of buildings by decreasing the external energy demand. The results obtained by applying the methodology in a case study highlight that all these aspects must be taken into account simultaneously to improve the existing conditions of entire city areas, leading to a more sustainable urban environment. [ABSTRACT FROM AUTHOR]

Climate change is a global problem facing mankind, and achieving peak CO2 emissions and carbon neutrality is an important task for China to respond to global climate change. The quantitative evaluation of the trends of urban energy consumption and carbon emissions is a premise for achieving this goal. Therefore, from the perspective of urban expansion, this paper analyzes the complex relationship between the mutual interactions and feedback between urban population, land expansion, economic growth, energy structure and carbon emissions. STELLA simulation software is used to establish a system dynamics model of urban-level carbon emissions effects, and Changsha city is used for the case study. The simulated outputs of energy consumption and carbon emissions cover the period from 1949 to 2016. From 1949 to 2016, Changsha's total energy consumption and carbon emissions per capita have continuously grown. The total carbon emissions increased from 0.66 Mt-CO2 to 60.95 Mt-CO2, while the per capita carbon emissions increased from 1.73 t-CO2/10,000 people to 18.3 Mt-CO2/10,000 people. The analysis of the structure of carbon emissions shows that the industrial sector accounted for the largest proportion of emissions, but it had gradually dropped from between 60% and 70% to about 40%. The carbon emissions of residential and commercial services accounted for less than 25%, and the proportion of transportation carbon emissions fluctuated greatly in 2013 and 2016. From the perspective of carbon emissions effects, carbon emissions per unit of GDP had a clear downward trend, from 186.11 t-CO2/CNY104 to 1.33 t-CO2/CNY104, and carbon emissions per unit of land showed two inflection points: one in 1961 and the other in 1996. The general trend showed an increase first, followed by a decrease, then a stabilization. There is a certain linear correlation between the compactness of urban shape and the overall trend of carbon emissions intensity, while the urban shape index has no linear correlation with the growth rate of carbon emissions. The carbon emissions assessment model constructed in this paper can be used by other municipalities, and the assessment results can provide guidance for future energy planning and decision making. [ABSTRACT FROM AUTHOR]

Featured Application: Determining the intensity and spatial pattern of health risk caused by heavy metals in street dust helps in the proper management of the city's environment. Various pollutants, including heavy metals, present in street dust can pose a threat to the health of city dwellers. So far, studies on levels of this threat have been carried out mainly in large cities, characterised by considerable road traffic and industrial activity. This paper assesses the levels of hazard index and cancer risk for Cd, Cr, Cu, Ni, Pb and Zn contained in street dust collected in 2013 and 2018 at 62 points located in different parts of a small/medium-sized city (Lublin, E Poland). Heavy metals contents were analysed by means of XRF spectrometry (in the fraction <63 µm). Despite the fact that the concentrations of some elements (Zn, Cd and Cu) in street dust are 6–7 times higher than the geochemical background, this does not pose a risk of non-carcinogenic effects. The average hazard index (HI) for the individual elements reaches very low levels (<0.01). Cancer risk (CR) for adults is below the less strict limit of 10−4, and in the case of Pb, it is even lower than values of the order of 10−6, whereas for children, CR levels exceed the standards and are of the order of 10−4, except for Pb. For all metals except Cr, the health risk was higher in 2013 than in 2018. [ABSTRACT FROM AUTHOR]

Health improvement is an important social development goal for every country. By using a geographical weighted regression (GWR) model on the 5th and 6th censuses data, this paper analyzes the spatially varied influencing factors of the change in life expectancy of residents in Chinses cities. The results indicate that: (1) The initial level of life expectancy may have a negative correlation with its increase, indicating that life expectancy in different areas may eventually converge to a higher level; moreover, the degree of convergence of life expectancy in cities with different economic development levels is variant. (2) Results of geographically weighted regression model demonstrate significant spatial heterogeneity in the effects of the level of economic development, medical conditions, demographic structure, and natural environment on health improvement. Natural conditions, such as topography, dictate the change in life expectancy in most cities in the middle eastern region of China. Change of educational level is the leading factor in the vast western region while the change in birth rate is the most critical in Xinjiang. Thus, local-based strategies are critical for solving health problems, especially with a focus on promoting health conditions in middle-income and low-income areas. [ABSTRACT FROM AUTHOR]

Rapid urban population growth that boosts increased waste generation and electricity demand has led to a possible alternative waste-to-energy solution in Southeast Asia. Despite some issues related to the development of the waste-to-energy sector such as public perception, all stakeholder involvement, public–private partnerships, funding, and climate factors, some Southeast Asian countries have made a reasonably successful step toward the developed technologies. Therefore, this study aimed to highlight an overview of the waste-to-energy sector in Southeast Asian countries to specify the status, challenges, opportunities, and selection of the technologies suited for the specific country. In order to achieve this aim, the study collected, synthesized, and evaluated data about waste resources, current waste management, waste-to-energy utilization, and its potential in the region based on published research papers and policy reports. It was found that the major waste-to-energy technologies in the region are incineration, landfills with gas capture, and anaerobic digestion. The total quantity of the waste-to-energy capacity from landfill biogas plants, incineration plants, and other waste-to-energy practices in the region accounts for over 323 MW at present and is expected to grow to double its current size by 2022. Meanwhile, by 2030, the realizable generation potential from renewable municipal waste in six Southeast Asian countries (Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam) amounts to 17.26 terawatt-hours (TWh). The study also specifies the requirements and considerations for the selection of waste-to-energy technologies, as well as the dimensions related to the development of the technologies. Additionally, four major aspects—technical, financial, environmental, and social and political—regarding the challenges and opportunities for the development of these technologies are considered. The challenges and opportunities related to the development of waste-to-energy in the region reveal how to overcome the drawbacks and to grasp the benefits at present and in the near future. Finally, the study is concluded with suggestions for the selection of the technologies in the region. [ABSTRACT FROM AUTHOR]

The current perspective about urban development expects 70% of energy consumption will be concentrated in the cities in 2050. In addition, a growing density of people in the urban context leads to the need for increased security and safety for citizens, which imply a better lighting infrastructure. Smart solutions are required to optimize the corresponding energy effort. In developing countries, the cities' lighting is limited and the lighting world map is strongly significant about the urban density of the different areas. Nevertheless, in territories where the illumination level is particularly high, such as urban contexts, the conditions are not homogenous at the microscale level and the perceived security is affected by artificial urban lighting. As an example, 27.2% of the families living in the city of Milan, ombardy Region, Italy, consider critical the conditions of lighting in the city during the night, although the region has diffused infrastructure. The paper aims to provide a local illuminance geographic information system (GIS) mapping at the neighborhood level that can be extended to the urban context. Such an approach could unveil the need to increase lighting to enhance the perceived safety and security for the citizens and promote a higher quality of life in the smart city. Lighting mapping can be matched with car accident mapping of cities and could be extended to perceived security among pedestrians in urban roads and green areas, also related to degradation signs of the built environment. In addition, such an approach could open new scenarios to the adaptive street lighting control used to reduce the energy consumption in a smart city: the perceived security of an area could be used as an additional index to be considered during the modulation of the level of the luminosity of street lighting. An example of a measurement set-up is described and tested at the district level to define how to implement an extensive monitoring campaign based on an extended research schema. [ABSTRACT FROM AUTHOR]

Featured Application: This work analyzes the contribution of energy self-sufficiency in cities (smart cities) to energy security and the environmental sustainability of the countries. Cities are at the center of the transition to a decarbonized economy. The high consumption of electricity in these urban areas causes them to be the main focus of greenhouse gas emissions. However, they have a high margin of capacity to increase energy efficiency and local energy generation. Along these lines, the smart urban management model has been proposed as a solution to the unsustainability of cities. Due to the global trend of population concentration in urban areas, cities tend to be representative of the population, energy consumption, and energy sustainability of their countries. Based on this hypothesis, this paper studied the relationship between the smart city model and the concept of energy sustainability. First, the research analyzed the relationship between urban population growth and energy sustainability; and then the self-consumption capacity of photovoltaic electricity of the main cities of the countries classified in the energy sustainability indicator (Energy Trilemma Index 2017) of the World Energy Council was analyzed. According to the results, the scope of action for self-consumption of photovoltaic electricity is broad and cities have the capacity to contribute significantly to the energy sustainability of their countries. Following the approach of other authors, the development of energy sustainability objectives and the installation of smart systems in distribution grids must be aligned with national objectives. [ABSTRACT FROM AUTHOR]

Urban tree canopy (UTC) provides urban residents with numerous benefits, including positive mental and physical health, the mitigation and prevention of urban heat islands, and a sense of place. Numerous studies have shown that as the wealth of a community decreases, so does the amount of UTC found in the community; thus, wealthier communities are more likely to enjoy the benefits that urban forests provide. Emerging technologies in remote sensing and GIS are allowing for new opportunities to study and understand the relationships between urban neighborhoods and UTC. In this study, land cover data for Chattanooga, Tennessee were derived from high-resolution (50 cm) multispectral imagery to assess the previously unknown extent and distribution of UTC and to measure the extent of UTC by neighborhood and census block group level. Using exploratory regression analysis, variables representing income, population density, race, educational attainment, and urban heat islands were analyzed to investigate the influence of UTC on neighborhood characteristics. This study found that UTC represented half of the total land cover composition, the tree equity was not as profound as shown in other cities, and the lack of UTC likely influences the prevalence of urban heat islands. This study also shows the importance and utility of using high-resolution imagery and land cover to assess and understand the impact and distribution of UTC in urban environments. [ABSTRACT FROM AUTHOR]

This study utilizes taxi trajectory data to uncover urban residents' travel patterns, offering critical insights into the spatial and temporal dynamics of urban mobility. A fusion clustering algorithm is introduced, enhancing the clustering accuracy of trajectory data. This approach integrates the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm, modified to incorporate time factors, with kernel density analysis. The fusion algorithm demonstrates a higher noise point detection rate (15.85%) compared with the DBSCAN algorithm alone (7.31%), thus significantly reducing noise impact in kernel density analysis. Spatial correlation analysis between hotspot areas and paths uncovers distinct travel behaviors: During morning and afternoon peak hours on weekdays, travel times (19–40 min) exceed those on weekends (16–35 min). Morning peak hours see higher taxi utilization in residential and transportation hubs, with schools and commercial and government areas as primary destinations. Conversely, afternoon peaks show a trend towards dining and entertainment zones from the abovementioned places. In the evening rush, residents enjoy a vibrant nightlife, and there are numerous locations for picking up and dropping off people. A chi-square test on weekday travel data yields a p-value of 0.023, indicating a significant correlation between the distribution of travel hotspots and paths. [ABSTRACT FROM AUTHOR]

The subway is one of urban residents' main means of public transportation. The design of entrance pavilions to subway stations has shown a diverse development trend over time. Since most of the subway building space is underground, it is very difficult for subway users or the public to view the structure fully. In this instance, the ground-level entrance pavilion becomes the most eye-catching element in the subway system. From the perspective of the urban spatial environment, the external form of entrance pavilions is one of the components of urban architecture. The design of the external form of entrance pavilions has an important impact on the urban spatial form and affects people's feelings about the urban space. Therefore, it is essential to study the external form of entrance pavilions. This study investigated 42 subway entrance pavilions in 17 cities in China. The following features were studied: exterior contour, façade permeability, cultural characteristics, decorative materials, and the number of exterior colors. The photostimulation method was adopted to evaluate the influence of the physical features of the external form of the entrance pavilions on respondents' visual impact assessment. The data analysis showed that respondents with different demographic characteristics provided different visual impact assessments of the external form of the entrance pavilions, and all five physical features influenced their assessment. This study offers a valuable reference for constructing entrance pavilions and urban spaces in Chinese cities in the future. [ABSTRACT FROM AUTHOR]

This research article examines the impact of economic, health, environmental, and social-economic factors on diverse forms of pro-environmental consumption: energy conservation, water conservation, and recycling. Primary data concerning these variables were collected from 430 individuals using a structured questionnaire following the cluster sampling methodology. Results indicate that one unit increase in environmental, economic, and health concerns improve pro-environment behavior by 52, 64, and 25 units, respectively. In contrast, a 1 unit increase in income deteriorates pro-environment behavior by 0.01 units. Education, age, gender, and owning a home have an insignificant impact on pro-environmental habits. The model explains a 52% variation in pro-environmental habits. The study recommends that effective electronic and social media campaigns increase environmental, economic, and health concerns and improve green behavior. More courses on environmental sustainability in schools and universities can effectively increase ecological knowledge and concerns. [ABSTRACT FROM AUTHOR]

The organic fraction of municipal waste, OFMW, management is one of the main concerns for urban waste managers in developed countries. Composting this biodegradable urban waste and using the compost in agricultural activities is a quickly growing method and is a viable option to manage urban waste in both the developed and the developing world. This research presents the example of the management proposal for Cartagena city in Spain, in which the technical and environment feasibility has been studied. This work aimed to evaluate the composting of a mixture of different residues, namely organic waste, pruning, and Posidonia oceanica (L.) Delile, which was collected from the beaches of the municipality while cleaning. Mixtures 1, M1 (composed of 20% OFMW and 40% pruning and seaweed) and 3, M3 (composed of 30% OFMW and 70% pruning) proved to be the best to reach the ideal compost (1.23-0.08-1.28 NPK and 2.22-0.33-3.45 NPK, respectively). An extreme mixture, M2, was evaluated (50% PO; 50% pruning) but poor results were obtained due to a non-optimal initial C/N by not containing OFMW. At the same time, the volumes by urban nucleus and the viability of different composting strategies have been studied, proposing that 180,000 inhabitants use the centralized composting plant and almost 31,000 inhabitants use self-composting and community composting. Considering the carbon footprint of this management, the value was 50% lower than the total management in a centralized plant. Moreover, the final use of compost is optimum with self-composting and community composting because it is nearest to agricultural consumptions, also reducing the transport of the final product. [ABSTRACT FROM AUTHOR]

A smart city is a sustainable and effectual urban center which offers a maximal quality of life to its inhabitants with the optimal management of their resources. Energy management is the most difficult problem in such urban centers because of the difficulty of energy models and their important role. The recent developments of machine learning (ML) and deep learning (DL) models pave the way to design effective energy management schemes. In this respect, this study introduces an artificial jellyfish optimization with deep learning-driven decision support system (AJODL-DSSEM) model for energy management in smart cities. The proposed AJODL-DSSEM model predicts the energy in the smart city environment. To do so, the proposed AJODL-DSSEM model primarily performs data preprocessing at the initial stage to normalize the data. Besides, the AJODL-DSSEM model involves the attention-based convolutional neural network-bidirectional long short-term memory (CNN-ABLSTM) model for the prediction of energy. For the hyperparameter tuning of the CNN-ABLSTM model, the AJO algorithm was applied. The experimental validation of the proposed AJODL-DSSEM model was tested using two open-access datasets, namely the IHEPC and ISO-NE datasets. The comparative study reported the improved outcomes of the AJODL-DSSEM model over recent approaches. [ABSTRACT FROM AUTHOR]

Bus service is of great significance to urban residents. With the convergence of bus lines and the formation of bus hubs, a multi-line operation mode which can realize the centralized management of vehicles is applied to the daily bus service planning. To solve the bus service planning problem systematically in the multi-line operation mode, we propose an integrated framework for bus timetabling (TT) and vehicle scheduling (VS), which are the two fundamental processes of bus service planning. Firstly, the determination processes of TT and VS are correlated by constructing the multiple vehicles' trip-link chains with departure time information to facilitate simultaneous optimization. Secondly, a multi-objective optimization model is constructed, which considers higher service quality and lower operating costs as objectives. Logic and operational rules are also considered as constraints to ensure the implementation of the solutions. Thirdly, we propose and implement a heuristic solution algorithm based on neighborhood search to achieve high-performance solutions. Finally, we validate the efficiency and effectiveness of our framework under the actual bus operation scenario in Chongqing, China. The Pareto frontier solutions are provided to bus operators as alternative operation schemes. [ABSTRACT FROM AUTHOR]

Most cities worldwide suffer from serious air-quality problems, which have received increasing attention in the past decade. The most probable reason for the air-quality problems is the urban population growth, combined with a change in land use due to increasing urban areas. The emission of air pollutants is caused by different anthropogenic processes which can be categorized into the sources of urban traffic, industry, and domestic heating. Dispersion and dilution of air pollutants are strongly influenced by meteorological conditions, especially by wind direction, wind speed, turbulence, and atmospheric stability. With an increasing number of people living in cities, there is the need to examine the correlation between air pollution, local climate, and the effects these changes have on global climate. New interdisciplinary research studies are needed to increase our understanding of the interactions among these aspects. The aim is to analyze the pollutant condition in Rome and the other provinces of the Lazio region with qualitative and quantitative analysis, in order to understand which are the main pollutant sources and what is the correlation of habits of the population on air pollutant emissions. [ABSTRACT FROM AUTHOR]

The current quantitative and qualitative development of bike-sharing systems worldwide involves particular implications regarding the level of sustainability of urban development and city residents' quality of life. To make these implications as large as possible as well as the most positive, it is essential that the people who use municipal bikes on a regular basis to the largest extent possible abandon car travel at the same time. Thanks to their operational characteristics, electric bikes should enable meeting the transport needs of a wider group of city residents compared with traditional bicycles. The main aim of this study was therefore to check whether the municipal electric bike system (MEVO) in Gdańsk-Gdynia-Sopot metropolitan area of Poland lived up to the hopes placed upon it by policymakers. Therefore, the article tests the hypothesis indicating that the municipal electric bike systems constitute a substitutable form of transportation against passenger cars to a larger extent than against collective urban transport and walking trips. The analysis was performed based on the results of primary studies conducted among the users of MEVO. The data show that the MEVO was a substitutable form of transportation against collective transport and walking trips to a larger extent than against passenger cars. Through logistic regression analysis, the variables concerning the probability of replacing car trips by MEVO bicycles were determined. Among the analyzed variables, the following turned out to be statistically significant: age, the number of people in the household, the number of cars in the household, the distance from work, and gender. The results therefore indicate that substituting in favor of electro bikes was more probable for younger people with fewer people in the household and a distance to travel below 3 km, whereas it was less probable for people with more cars in the household or traveling a distance longer than 10 km. Additionally, females were more likely to choose the bike system. [ABSTRACT FROM AUTHOR]

The human health risk associated with PM10 exposure was assessed for the residents of Cluj-Napoca city, Romania, for a best case-scenario based on the monthly average PM10 and for a worst-case scenario based on the monthly 90th percentile of PM10 concentration. As no toxicity value for PM10 was available, for the calculation of the hazard quotient, the toxicity value was considered to be equal to the annual limit value (40 µm/m3) set in the European Union (EU), and to air quality guidelines (20 µm/m3) set by the World Health Organization (WHO). The daily PM10 concentrations for the period 2009–2019, at the four monitoring stations existing in Cluj-Napoca, were obtained from the National Air Quality Monitoring Network. The annual PM10 values ranged between 20.3 and 29.5 µg/m3, and were below the annual limit value (40 µg/m3) set by European and national legislation, but above the annual air quality guideline (20 µg/m3) set by WHO. Generally, the monthly PM10 concentrations were higher from October to March than in the rest of the year. The monthly air quality index (AQI) showed the good to moderate quality of the air during the whole decade; however, there were days when the air quality was unhealthy for sensitive population groups. The air quality was more or less constant during the warm months, and improved significantly for the cold months from 2009 to 2019. In the best-case scenario, calculated using the EU annual limit value for PM10, the potential non-carcinogenic chronic health risk was present only in 2009 and 2010, but in the worst-case scenario, in each year there were periods, especially in the cold months, in which health risk was present. When considering the WHO air quality guidelines in the calculation of the health risk, the potential non-carcinogenic chronic health risk was present between October and March in each year in the best-case scenario, and in most of the months in the worst-case scenario. [ABSTRACT FROM AUTHOR]

Featured Application: This article applies the neural network of one-stage target detection to detect and count the urban traffic flows in different scenarios and weather conditions. This research can be used to provide information-based support for the development and optimization of the transportation systems of a modern smart city. When the data of detections and statistical analyses of traffic flows has been further applied, traffic management departments can make better decisions on road infrastructure optimization or traffic limits to avoid a large number of traffic congestion and traffic accidents, and that can improve the life quality and convenience of urban people. In the intelligent traffic system, real-time and accurate detections of vehicles in images and video data are very important and challenging work. Especially in situations with complex scenes, different models, and high density, it is difficult to accurately locate and classify these vehicles during traffic flows. Therefore, we propose a single-stage deep neural network YOLOv3-DL, which is based on the Tensorflow framework to improve this problem. The network structure is optimized by introducing the idea of spatial pyramid pooling, then the loss function is redefined, and a weight regularization method is introduced, for that, the real-time detections and statistics of traffic flows can be implemented effectively. The optimization algorithm we use is the DL-CAR data set for end-to-end network training and experiments with data sets under different scenarios and weathers. The analyses of experimental data show that the optimized algorithm can improve the vehicles' detection accuracy on the test set by 3.86%. Experiments on test sets in different environments have improved the detection accuracy rate by 4.53%, indicating that the algorithm has high robustness. At the same time, the detection accuracy and speed of the investigated algorithm are higher than other algorithms, indicating that the algorithm has higher detection performance. [ABSTRACT FROM AUTHOR]

The recycling of restaurant food waste can bring environmental benefits and improve food safety for urban residents. We here assessed the entire life cycle of the anaerobic digestion–aerobic composting technique of restaurant food waste recycling using Lanzhou as a case study. We used the CML2001 method provided with the Gabi software and compared the results to those produced using the traditional treatment techniques (landfill and incineration). This work includes a sensitivity analysis of the results. It is here concluded that the anaerobic digestion–aerobic composting technique had the smallest environmental impact of the methods here examined. The life cycle of anaerobic digestion–aerobic composting primarily consumes water, clay, coal, crude oil, and natural gas. The pre-processing phase consumes the most resources, and anaerobic digestion showed the greatest environmental impact. Specific environmental impacts in order from the highest to lowest potential to exacerbate global warming were found to be photochemical ozone production, acidification, eutrophication, marine aquatic ecotoxicity, human toxicity, freshwater aquatic ecotoxicity, and terrestrial ecotoxicity. The main factors associated with different environmental impacts and the environmental impacts themselves were found to differ across different phases. Some environmental impacts were shown to be sensitive to electricity, and the eutrophication potential and photochemical ozone creation potential showed the least sensitivity to all variables. To reduce the environmental impact of the anaerobic digestion–aerobic composting treatment technique, the energy structure and consumption of electricity, water, and diesel need to be optimized. [ABSTRACT FROM AUTHOR]

Accelerated urbanization and the ensuing rapid increase in urban populations led to the need for a tremendous number of parking spaces. Automated parking systems coupled with new parking lot layouts can effectively address the need. However, most automated parking systems available on the market today use ultrasonic sensors to detect vacant parking spaces. One limitation of this method is that a reference vehicle must be parked in an adjacent space, and the accuracy of distance information is highly dependent on the positioning of the reference vehicle. To overcome this limitation, an around view monitoring-based method for detecting parking spaces and algorithms analyzing the vacancy of the space are proposed in this study. The framework of the algorithm comprises two main stages: parking space detection and space occupancy classification. In addition, a highly robust analysis method is proposed to classify parking space occupancy. Two angles of view were used to detect features, classified as road or obstacle features, within the parking space. Road features were used to provide information regarding the possible vacancy of a parking space, and obstacle features were used to provide information regarding the possible occupancy of a parking space. Finally, these two types of information were integrated to determine whether a specific parking space is occupied. The experimental settings in this study consisted of three common settings: an indoor parking lot, an outdoor parking lot, and roadside parking spaces. The final tests showed that the method's detection rate was lower in indoor settings than outdoor settings because lighting problems are severer in indoor settings than outdoor settings in around view monitoring (AVM) systems. However, the method achieved favorable detection performance overall. Furthermore, we tested and compared performance based on road features, obstacle features, and a combination of both. The results showed that integrating both types of features produced the lowest rate of classification error. [ABSTRACT FROM AUTHOR]

Featured Applications: Urban big data analyses in urban studies, spatial science or applied social science, shaping the smart cities of the future, support the process of increasing the activity of residents and their social participation in creating a smart city. The article proposes the concept of modeling that uses multi-agent systems of mutual interactions between city residents as well as interactions between residents and spatial objects. Adopting this perspective means treating residents, as well as buildings or other spatial objects, as distinct agents that exchange multifaceted packages of information in a dynamic and non-linear way. The exchanged information may be reinforced or diminished during the process, which may result in changing the social activity of the residents. Utilizing Latour's actor–network theory, the authors developed a model for studying the relationship between demographic and social factors, and the diversified spatial arrangement and the structure of a city. This concept was used to model the level of residents' trust spatiotemporally and, indirectly, to study the level of social (geo)participation in a smart city. The devised system, whose test implementation as an agent-based system was done in the GAMA: agent-based, spatially explicit, modeling and simulation platform, was tested on both model and real data. The results obtained for the model city and the capital of Poland, Warsaw, indicate the significant and interdisciplinary analytical and scientific potential of the authorial methodology in the domain of geospatial science, geospatial data models with multi-agent systems, spatial planning, and applied social sciences. [ABSTRACT FROM AUTHOR]