Transport of goods in a city is a basic prerequisite for meeting the needs of the population. The transport of dangerous goods, especially in urban areas, represents a risk that can result in a dangerous situation as well as unwanted consequences. According to the data, the percentage of dangerous goods transported in the European Union is 4% of the total amount of transported goods. The perception of road users is one of the basic factors for the safe and smooth flow of traffic. Bearing the above in mind, this paper conducted an analysis aimed at determining the differences in the perception of large danger lists and orange boards for marking transport units. 288 respondents participated in the research. The results show that there are statistically significant differences between the perception of large danger lists and orange boards for marking transport units, viewed according to gender, age, place of residence, education, occupation of the respondents and according to the driver's license category.

The burgeoning expansion of the Internet of Things (IoT) technology has propelled Intelligent Traffic Systems (ITS) to the forefront of IoT applications, with accurate highway traffic flow prediction models playing a pivotal role in their development. Such models are essential for mitigating highway traffic congestion, reducing accident rates, and informing city planning and traffic management strategies. Given the inherent periodicity, non-linearity, and variability of highway traffic data, an innovative model leveraging a Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention Mechanism (AM) is proposed. In this model, feature extraction is accomplished via the CNN, which subsequently feeds into the BiLSTM for processing temporal dependencies. The integration of an AM enhances the model by weighting and fusing the BiLSTM outputs, thereby refining the prediction accuracy. Through a series of experiments and the application of diverse evaluation metrics, it is demonstrated that the proposed CNN-BiLSTM-AM model surpasses existing models in prediction accuracy and explainability. This advancement positions the model as a significant contribution to the field, offering a robust and insightful tool for highway traffic flow prediction.

This investigation delves into the critical challenges of urban development and management, employing a comprehensive evaluation of four strategic alternatives: transit-oriented development, green infrastructure investment, smart city technologies, and community-based development. These alternatives are rigorously assessed against a set of eight meticulously chosen criteria. Distinct from conventional analyses, the study adopts the sophisticated Criteria Importance Through Inter-criteria Correlation (CRITIC)-Weighted Aggregated Sum Product Assessment (WASPAS) methodology, utilizing spherical fuzzy sets (SFS). This approach mitigates uncertainties inherent in decision-making processes, thereby refining the accuracy of the evaluation. The CRITIC-WASPAS method, with its innovative application in this context, augments the precision of the assessments, yielding a detailed appraisal of each alternative's merits and limitations. Through assigning weighted criteria and systematically ranking these alternatives, the study furnishes pivotal insights for urban planners and policymakers. This contribution is instrumental in guiding decisions that promote resilience, equity, and environmental sustainability in urban environments. The novel integration of the CRITIC-WASPAS method in this domain not only propels the field forward but also lays a robust foundation for informed and effective decision-making. The outcomes of this research are poised to significantly impact the discourse on sustainable urban development, offering a data-driven framework that is essential for sculpting the future of cities amidst evolving urban challenges.

The advent of China's “dual carbon” objectives necessitates stringent carbon emission reductions across all sectors, notably within the construction industry, which accounts for a significant proportion of the nation's emissions. This study presents a comprehensive examination of the allocation of building carbon emission rights, underpinned by an index system specifically designed for the construction sector, to adhere to the overarching goals of carbon neutrality. Ten refined indicators were developed, encapsulating principles of fairness, efficiency, and sustainability, including metrics such as construction stock and the value added by the construction industry. Employing a methodological framework that integrates a centralized Data Envelopment Analysis (DEA) approach, the entropy method, and k-means clustering, this research delineates an effective strategy for the allocation of carbon emission quotas. The initial allocation for Henan Province in 2023 revealed a geographical variance, characterized by higher quotas in the west compared to the east, with Zhengzhou City allocated 16.53 Mt of carbon emissions—3.59 times greater than that allocated to Zhoukou City, the municipality receiving the lowest quota. Subsequent optimization and adjustment led to the identification that, out of eighteen cities and municipalities, ten require no immediate modification to their carbon emission rights. Meanwhile, four cities were found to have a surplus, and four faced a deficit. The findings not only offer actionable insights for the implementation of urban-level carbon reduction strategies but also enhance the discourse on the allocation of building carbon emission rights, thereby contributing to the broader aim of achieving carbon neutrality. The refined approach and empirical demonstration within Henan Province serve as a pivotal reference for similar endeavors in other regions, emphasizing the necessity for tailored, data-driven allocation strategies that account for local economic activities and construction practices.

In the rapid urbanization experienced globally, traffic congestion emerges as a critical challenge, detrimentally affecting economic performance and the quality of urban life. This study delves into the deployment of machine learning (ML) and deep learning (DL) methodologies for mitigating traffic congestion within smart city frameworks. An extensive literature review coupled with empirical analysis is conducted to scrutinize the application of these advanced technologies in various transportation domains, including but not limited to traffic flow prediction, optimization of routing, adaptive control of traffic signals, dynamic management of traffic systems, implementation of smart parking solutions, enhancement of public transportation systems, anomaly detection, and seamless integration with the Internet of Things (IoT) and sensor networks. The research methodology encompasses a detailed outline of data sources, the selection of ML and DL models, along with the processes of training and evaluation. Findings from the experiments underscore the efficacy of these technological interventions in real-world settings, highlighting notable advancements in the precision of traffic predictions, the efficiency of route optimization, and the responsiveness of adaptive traffic signal controls. Moreover, the study elucidates the pivotal role of ML and DL in facilitating dynamic traffic management, anomaly detection, smart parking, and the optimization of public transportation. Through illustrative case studies and examples from cities that have embraced these technologies, practical insights into their applicability and the consequential impact on urban mobility are provided. The research also addresses challenges encountered, offering a discourse on potential avenues for future research to further refine traffic congestion management strategies in smart cities. This contribution significantly enriches the existing corpus of knowledge, presenting pragmatic solutions for urban planners and policy makers to foster more efficient and sustainable transportation infrastructures.

To investigate the high-quality and efficient development of the manufacturing industry in the Central Plains Urban Agglomeration (CPUA), this paper uses the three-stage Data Envelopment Analysis (DEA) and Malmquist index model to evaluate and analyze the manufacturing development efficiency of 30 prefecture-level cities in five provinces of China in the CPUA from 2017 to 2022. First, the DEA model is applied to evaluate the comprehensive efficiency of the manufacturing industry in 30 regions of the CPUA; second, the Stochastic Frontier Analysis (SFA) regression model is used in conjunction with the technical efficiency and scale efficiency of the manufacturing industry to deeply explore and adjust the causes of the current situation in various regions; finally, after re-analyzing the efficiency with the corrected input-output data, the Malmquist index model is used to analyze the total factor productivity index and its decomposed efficiency of the manufacturing industry in the CPUA from 2017 to 2022. The study shows that the pure technical efficiency (PTE) of the 30 prefecture-level cities in the CPUA from 2017 to 2022 is stable and relatively good, and the main reason for the overall low comprehensive efficiency is the poor scale efficiency; after excluding the interference of environmental factors, the average comprehensive efficiency of each city is lower than before the adjustment, with environmental factors and random errors having a significant impact on the manufacturing industry, especially in Bengbu City; the main factor in the decline of the total factor productivity of the manufacturing industry in the CPUA is the hindrance of technological progress; the spatial distribution of the comprehensive efficiency of the manufacturing industry in the CPUA generally shows a pattern of “higher efficiency in the middle, lower efficiency at the edges”, and there is a situation of regional development imbalance in the high-quality development level of the manufacturing industry in the 30 regions.

This study uses BIM software and fluid simulation finite element analysis software to investigate the impact of the water film effect on the surfaces where highways connect with urban roads. The analysis indicates that the drainage length (L), road surface slope (i), rainfall intensity (I), and road surface construction depth (T) are significant factors affecting the thickness of the water film (H) generated by the said effect. The thickness of the water film grows with an increase in drainage length and rainfall intensity; however, it decreases with an increase in road surface slope and road surface construction depth. The approximate relationship between the water film thickness and these four influencing factors is $\mathrm{H} \propto \mathrm{L}^{4.02655} \mathrm{i}^{-1.65562}(0.87 \mathrm{I}+1.26)\left(4.07-0.17 \mathrm{~T}-0.13 \mathrm{~T}^2\right)$. When the water film is thin, the area occupied by the water film in front of the car tires is small; as the thickness of the water film increases, the area it occupies gradually increases, and the tires are gradually lifted by the water film. When lifted to a certain height, the "hydroplaning" phenomenon occurs, which can lead to traffic safety issues. The results of this study are expected to provide a reference for related research on connection lines.

In the realm of sustainable urban development, a paramount focus is placed on the amalgamation of environmental conservation, the integration of smart technology, and the promotion of social inclusivity. This approach advocates for transit-oriented development, the establishment of resilient infrastructure, and the active engagement of communities. A critical balance is sought between economic viability and adaptive governance, aiming to cultivate cities that are simultaneously environmentally conscious, economically vibrant, and socially equitable. Within this context, Multiple Attribute Decision Making (MADM) emerges as a pivotal tool, streamlining decision processes through the quantitative evaluation of alternatives against criteria such as environmental impact and social inclusivity. MADM plays an instrumental role in ensuring effective resource allocation, thereby fostering resilient infrastructure and optimizing the equilibrium between economic growth and sustainability in urban planning. This study delves into an advanced methodology for addressing uncertainties in decision-making, employing Picture Fuzzy Sets (PFSs), articulated through the meticulous application of the Measurement Alternatives and Ranking according to Compromise Solution (MARCOS). The utilization of the MARCOS strategy in decision-making is underscored by its proven robustness as a tool for pinpointing the optimal objective. This method integrates diverse aggregation strategies to adeptly navigate complex decision scenarios characterized by multiple criteria. To illustrate the adaptability and efficacy of the proposed methodology, a numerical case study is presented, offering a vivid demonstration of its practical application in the field of urban development.