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With large volume of passengers boarding and alighting through subway platforms, the stations are getting crowded, resulting in drops in the level of service and safety concerns, especially for subway systems operating at capacity during peak hours. Thus, it is crucial for subway agencies to sense changes in travel demand and adjust their management schemes accordingly. In this paper we propose a statistical approach to estimate dynamic passenger flows with automated data. First, we develop a dynamic logistic model for calculating passenger tap-out times, which can be employed to infer passenger flow characteristics at the aggregate level. In addition, a new passenger-to-train assignment model for any subway route is derived based on the dynamic model. Subsequently, we apply an expectation-maximization algorithm to estimate the model parameters with automated fare collection and automated vehicle location data. Finally, a cross-validation method is employed to validate our approach with data obtained from several routes in Beijing subway system in China. Results of 95% prediction intervals indicate the effectiveness of the models and the proposed estimation methods. • Propose a dynamic logistic model for inferring and predicting subway passenger tap-out times. • Apply an expectation-maximization algorithm for model parameters estimation. • Develop a novel passenger-to-train assignment strategy. • No prior distributions of parameters or additional information required in the proposed methods. • Results show good effectiveness of the models and acceptable accuracy of the prediction methods. [ABSTRACT FROM AUTHOR]

This paper conducts a comparative study of the tensile performance of plug-pin scaffold joints under different temperatures. The current research objects are mostly for the traditional low carbon steel scaffolding, and lack of research on the tensile performance of scaffolding joints in low temperature conditions. In this paper, based on the temporary seating facilities of the Beijing Winter Olympic Games complex, a new type of sorbite stainless steel was used. The tensile bearing capacity, damage mode and strain distribution patterns of the sorbite stainless steel joints and traditional Q355 joints under different temperature conditions were studied with finite element model and parametric analysis. The results indicated that for traditional Q355 joints, damage primarily occurs in the form of wedge bending deformation, and that decreasing temperature leads to increased tensile bearing and yield capacity. In contrast, for sorbite stainless steel joints, higher tensile bearing capacity was observed at +20 °C, but at −20 °C and − 40 °C conditions, the bend of the C-type card becomes more prone to crack. The accuracy of the ABAQUS finite element model is verified by comparing it with test results. Increasing the thickness of the U-type and C-type cards can slightly increase the tensile bearing capacity of the joint and a minimum wall thickness of 2.7 mm is recommended. Two suggestions are proposed for improving the joint design using sorbite stainless steel. The research provides a premise for further studies on the tensile performance of plug-pin scaffold joints. • A low-temperature loading test device and method for joints are proposed. It offers a test environment of −70 °C ∼ 150 °C. • The material properties of sorbite stainless steel and Q355 at −40 °C, −20 °C and room temperature are studied. • The mechanical properties of plug-pin scaffold joints using sorbite stainless steel and Q355 at low temperatures are studied. • The tensile properties of plug-pin scaffold joints at different low temperatures are investigated. • Theoretical load-displacement modeling of plug-pin scaffold joints is developed. [ABSTRACT FROM AUTHOR]

A good risk warning model is an effective way to guarantee the stable operation of the carbon market and exert its emission reduction function. This paper takes the China's pilot carbon market as the research object and measures the carbon market risk by ARMA-GARCH-VaR. Then, influential factors are selected as non-market characteristics from the perspective of causality and incorporated into the risk warning model, and SMOTE and SMOTEENN are introduced to deal with the unbalanced samples. Results show that (1) The risk fluctuations of Beijing and Shenzhen carbon market are large, and the risk fluctuations of Guangdong carbon market are relatively smooth in the early period and larger in the recent period. (2) Air quality, average daily temperature, CSI 300, power coal, natural gas, European carbon market, exchange rate, and Baidu index are all important factors in the risk volatility of carbon market. (3) The Classification Decision Tree (CDT) has the best performance. CDT has the best performance in Guangdong carbon market, SMOTE-CDT has the best performance in Hubei carbon market, and SMOTEENN-CDT has the best performance in Shanghai carbon market, in which SMOTEENN-CDT has a 76% Recall. The non-equilibrium treatment improves the overall Recall, and the risk warning model after non-equilibrium treatment can avoid missing important risk points. This paper can provide a certain degree of reference for the improvement of carbon trading market and risk management. [Display omitted] • A carbon market warning model is constructed. • The average VaR of the carbon trading markets in Shenzhen and Beijing is larger. • CDT performs better in market risk warning. • The model improves 34.8% in the avoidance of risk compared with other models. [ABSTRACT FROM AUTHOR]

Urban green spaces are increasingly recognized for their positive impact on well-being, quality of life, and public health. Researchers have explored how green space characteristics, such as size, theme, and convenience, influence urban movement. However, an important aspect that has received limited attention is the relative attractiveness of green spaces. When residents have access to similar green spaces nearby, their inclination to visit more distant ones decreases. This leads to the central research question: How does the relative attractiveness of urban green spaces affect urban movements? The existing literature lacks a comprehensive theory to address this question. To bridge this gap, this paper introduces the Potential to Kinetic Energy (PKE) framework, inspired by principles from physics. In this framework, potential energy represents the relative attractiveness of green spaces between the travel origin and destination, while kinetic energy signifies the urban movement resulting from the this attractiveness. To demonstrate the PKE framework's applicability, this paper analyzes green space data and smart card subway ridership records from Beijing. The results show that a unit increase in size, cultural, attraction, proximity, and location potential energy is associated with a 0.22%, 0.27%, 0.14%, 0.19%, and 0.02% increase in urban movement. The findings provide compelling evidence for the significant role of relative attractiveness in shaping urban movements and offer valuable insights for urban planning and policy development. The proposal of the PKE framework encourages interdisciplinary discussions among urban planners, environmental scientists, and transportation experts, fostering collaborative efforts to enhance the sustainability of cities. • A Potential to Kinetic Energy (PKE) framework is introduced to investigate the relationship between green space attractiveness and urban movements. • Potential energy contains five sub-concepts: size, culture, attraction, proximity and location. • Kinetic energy represents urban movement that characterizes the motion of objects resulting from potential energy conversion. • Empirical analysis of Beijing shows the significant role of relative green space attractiveness in shaping urban movement patterns. • The PKE framework supports urban planners to evaluate green space attractiveness, examine the impact of landscape change, and promote inclusive urban environments. [ABSTRACT FROM AUTHOR]

This study investigates the carbon compensation mechanism as a tool to promote coordinated economic and environmental development in China's Jing-Jin-Ji region (Beijing, Tianjin, and Hebei). A novel approach for calculating carbon compensation cost that integrate emission responsibility as well as carbon abatement cost through a bi-level programming model is proposed. Shared producer and consumer responsibility is established for carbon emissions under the multi-regional input-output model. Marginal abatement costs of Beijing, Tianjin, and Hebei are further calculated through the input-output and linear programming method. Results highlight that the emission responsibility allocation method significantly affects the inter-regional carbon compensation amount. The production-based approach leads to a 77% reduction of carbon compensation amount from Beijing to Hebei, and a 170% overestimation from Tianjin to Hebei compared with the shared responsibility approach in 2060. The optimal carbon compensation unit cost ranges from 1539 to 6438 yuan/ton when the carbon reduction ratio ranges from 10% to 20%. Beijing would compensate Hebei 3.56 and 29.74 billion yuan for achieving 10% and 20% emission reduction, respectively. Similarly, Tianjin's compensation for Hebei would be 0.23 and 1.87 billion yuan in the two scenarios. Results further show that implementing the proposed carbon compensation mechanism can reduce total abatement cost by 43% and 84% in Jing-Jin-Ji under the two scenarios. The methods and results of this paper could help inform future carbon compensation implementation in China and other countries. • Develop a bi-level programming model to determine optimal carbon compensation cost. • Consider emission responsibility and carbon abatement cost in Jing-Jin-Ji. • Resulting mechanism reduces abatement cost by 43% for 10% emission reduction. • Production-based allocation underestimates carbon compensation amount by 62%. • Optimal carbon compensation unit cost changes with marginal abatement cost. [ABSTRACT FROM AUTHOR]

While it is widely known that the greening landscape spatial quality plays a vital role in promoting the level of highway landscape design, there is still no consensus on how to measure it quantitatively on a large scale. Recent advancements in computer vision tasks under deep learning, along with esmerging datasets such as street view pictures, have shown the potential to overcome previous limitations, leading to a paradigm shift in research. This paper proposes a novel digital measurement technique that automatically identifies elements and greening landscape spatial features from a partial panoramic and spherical view field, considering the driver and passengers' environmental perspectives. To achieve this, we have developed a spatial quality parameter index system consisting of three dimensions and nine indicators, with two regions dedicated to plains and mountainous regions. We have also created a visualization atlas that illustrates the landscape of highway greening in both vertical and horizontal dimensions. We applied this method to the Beijing section of the Beijing-Tibet highway to guide targeted improvement strategies from different perspectives. Our findings provide strategies for enhancing scientific performance and tools for quantitative analysis in digitizing, engineering, and refining highway landscape design. • Propose a high-precision training model Panoptic-DeepLab. • Propose a digital measurement indicator system of highway greening landscape spatial quality. • Gives an atlas representation method of highway greening landscape spatial quality. [ABSTRACT FROM AUTHOR]

The provision of accurate travel time information holds paramount significance for optimizing traffic operations and management. However, existing approaches typically address multiple travel time learning tasks independently, neglecting the correlation of tasks and necessitating the construction of separate models for each task. As the scale of the road network expands, this practice becomes increasingly infeasible. To bridge this gap, this paper presents an adaptive deep multi-task learning model to solve multiple travel time estimation tasks collaboratively from a citywide perspective. The novel model seamlessly integrates geographical, environmental, and spatial-correlated features, capturing the complex non-linear characteristics of travel time in the urban road network through a deep network learning feature representations layer by layer from the input data. Through the paradigm of deep multi-task collaborative learning, the knowledge learned from one task serves as an inductive bias for other tasks, thereby facilitating the learning of related tasks. By modeling the uncertainty of travel time with a probabilistic model, the weights of jointly learned tasks are adaptively fine-tuned in a principled way. Additionally, a novel algorithm is devised to fully leverage the information embedded within the data with partial information. The proposed methodology is evaluated through a case study on the citywide road network in Beijing, China. Empirical results of extensive experiments demonstrate the adeptness of the proposed model in effectively harnessing information transferred from parallel tasks, adaptively fine-tuning the weights of joint learning tasks, and proficiently exploiting data characterized by incomplete labels, therefore leading to superior performance compared to competing methods. The average estimation error of the four jointly learned travel time estimation tasks is about 28.48%, yielding an improvement of over 4% compared to single-task learning models. The results verify the effectiveness and robustness of the proposed model for travel time estimation in the urban road network. [ABSTRACT FROM AUTHOR]

Currently, ozone pollution is one of the key environmental concerns, and to provide more effective strategies for ozone control, this paper proposes the use of the Geostationary Environmental Monitoring Spectrometer (GEMS) in conjunction with MAX-DOAS for observations and ozone sensitivity analysis. Compared to traditional polar-orbiting satellites, GEMS, launched by South Korea in 2019, offers superior spatiotemporal resolution. However, there is a lack of ground validation for GEMS data related to ozone and its precursors. In this study, we conducted validation in the Jing-Jin-Ji (Beijing-Tianjin-Hebei) region using two typical MAX-DOAS sites and the China National Environmental Monitoring Center (CNEMC) sites, confirming the accuracy of GEMS data. Analysis of the seasonal horizontal distribution results from GEMS and vertical distribution results from MAX-DOAS in 2022 reveals that the concentrations of NO 2 , HCHO, O 3 , and ozone sensitivity not only display seasonality and regional variations but also exhibit distinct characteristics at different altitudes and times, influenced by local climatic conditions, human activities, and topography. When formulating ozone control policies, it becomes imperative to consider factors such as time, regional characteristics, and seasonality. Comparisons between daily results from TROPOMI and GEMS indicate that polar-orbiting satellites may overlook high-pollution periods within a single day, and their measurements may not provide a comprehensive representation of an entire day's data. Consequently, relying solely on polar-orbiting satellite data for shaping ozone prevention and control policies may inadvertently encourage ozone formation during specific time periods, rendering these policies ineffective. Therefore, leveraging GEMS is essential for the development of comprehensive, around-the-clock ozone control policies. Finally, we used monitoring data from Beijing on October 6, 2022, to illustrate how the combination of GEMS and MAX-DOAS enables three-dimensional monitoring of pollutant transport processes on an hourly scale. [Display omitted] • Validation of the accuracy of ozone and its precursor data from GEMS. • Analysis of hourly ozone sensitivity level distribution using GEMS. • Limitations of polar orbit satellites in O 3 sensitivity analysis compared to GEMS. • Observation of the transport of pollutants combining GEMS and MAX-DOAS. [ABSTRACT FROM AUTHOR]

The solar furnace is key equipment for the thermochemical reaction research which uses focused solar energy to drive the reactor, and the solar flux distribution on the reactor surface plays a decisive role in the regulation of the thermochemical reaction process and formation of target products. In this paper, a reflective solar furnace with a thermal power of 30 kWth in Yanqing, Beijing, is taken as the research objective. Firstly, the Monte Carlo Ray Tracing (MCRT) method is used to establish the mathematical model for the optical calculation, and the distribution characteristics of solar flux are deeply analyzed. The accuracy of the model is verified through the comparison with the SolTrace software and experimental measurements respectively. Secondly, an improved four-quadrant and adjustable focusing solar furnace experimental equipment is developed, and a new solar flux distribution regulation strategy is proposed by using the inward crossover and outward expansion offset of the four-quadrant focusing points coupled with the heat-absorbing platform movement. The results show that the non-uniform factor and the peak value of the solar flux distribution in the set target area can be optimized by selecting an appropriate inward crossover offset and movement of the heat-absorbing platform, respectively. Moreover, when the DNI varies between 480 W/m2 and 800 W/m2, through the optimized control strategy, a non-uniformity factor with the value of less than 0.10 can be achieved in the target area of a square reactor surface with a side length of 10 cm, and the peak flux can also be controlled in the range from 3000 kW/m2 to 3300 kW/m2 with a fluctuation range of less than 10.0% as set by the thermochemical reaction. The optimized control strategy can effectively meet the uniformity and stability requirements of solar flux distribution in solar thermochemical reaction and finally achieve the goal of regulating the target products. • An improved four-quadrant adjustable focusing solar furnace is developed. • An innovative solar flux distribution regulation strategy is proposed. • A non-uniformity factor with the value of less than 0.10 can be achieved. • The peak flux can be controlled in the range from 3000 kW/m2 to 3300 kW/m2. [ABSTRACT FROM AUTHOR]

Accurate traffic congestion estimation and prediction are critical building blocks for smart trip planning and rerouting decisions in transportation systems. Over the decades, there have been many studies focusing on traffic congestion estimation and prediction with different statistical approaches (e.g., Markov chain) and machine learning models (e.g., clustering, Bayesian networks, and artificial neural networks). However, there is a lack of a unified framework to address the mechanisms of different models and integrate the advantages of different methods through combinations. This paper introduces the FD-Markov-LSTM model, a hybrid interpretable approach that combines the fundamental diagram (FD), Markov chain, and long short-term memory (LSTM). The aim is to estimate and predict traffic states by integrating statistical data in both congested and uncongested scenarios. The FD-Markov-LSTM model leverages the FD to identify hierarchical traffic states and utilizes the Markov process to capture the probabilistic transitions between these states. We employ the LSTM model to further capture the residual time series produced by the Markov chain model (assuming a memoryless property) to enhance the estimation and prediction performance. The proposed model's accuracy in estimating and predicting traffic flow is evaluated using empirical data from three case studies conducted in Beijing and Los Angeles. The results highlight a significant improvement in accuracy compared to classical benchmark models such as the Markov model, ARIMA model, k-Nearest Neighbor model, Random Forest model, and LSTM. Specifically, the FD-Markov-LSTM model achieves reductions of over 39% in mean absolute error, 35% in root mean squared error, and 7.4% in mean absolute percentage error. These results clearly demonstrate that the FD-Markov-LSTM model outperforms the benchmark models, enabling more precise predictions of traffic flow. [ABSTRACT FROM AUTHOR]

Persistent PM 2.5 pollution poses a serious threat to human health. Developing an accurate urban regional PM 2.5 forecasting is of practical significance for environmental protection. However, previous studies have mostly focused on individual monitoring stations, neglecting the influence of neighboring stations, which limits forecasting accuracy. Additionally, the PM 2.5 of a single monitoring station cannot reflect the overall situation of a region. Therefore, this paper develops a novel PM 2.5 spatiotemporal forecasting framework that combines graph convolutional module, temporal convolutional module, linear module. It enables the forecasting of PM 2.5 concentrations at multiple stations and multiple time steps in the future. Concretely, we utilize a mixed graph convolutional network to extract the spatial features of PM 2.5. Then, an improved temporal convolutional network, the second-order residual temporal convolutional network, is developed to capture complex temporal features. Following the classical "linear and non-linear" modeling strategy, a linear module is added to the forecasting framework. Experiments on the real air pollution dataset from Beijing demonstrate that our framework outperforms the state-of-the-art baselines. • Extracting and fusing spatiotemporal features of PM2.5 is crucial for prediction. • A novel spatiotemporal forecasting framework is proposed. • A second-order residual TCN is developed to extract complex temporal features. • Experiments on real air pollution dataset show our framework's superiority. [ABSTRACT FROM AUTHOR]

Sewer pipes health and failure consequences evaluation are vital to ensure the safety of urban sewer networks. Sewer pipes with low health and severe failure consequences should be the primary focus of daily maintenance and management for drainage authorities. Conversely, sewer pipes with high health and low failure consequences are temporarily not a primary concern, in order to reduce resource wastage within the constraints of limited economic and time resources. Thus, the scientific analysis of their relationships is deemed essential. This paper applies supply-demand balance theory to analyze the relationship. On the supply side, a health Fuzzy Comprehensive Evaluation model (HFCE) is proposed with weighted hierarchies established. On the demand side, a failure consequences FCE model (FFCE) is established. Each sewer pipe's supply-demand balance score is analyzed using matrix multiplication, highlighting two types of imbalanced pipes and sub-districts. Taking the Liangshui River Basin in Beijing as an example, the results show that the health evaluation can identify the spatial distribution of the health of sewer pipes, with low health levels V and IV accounting for 10.23 % and 4.43 %, respectively. The failure consequences evaluation can evaluate the spatial distribution of the severity of sewer pipe failure consequences, with high failure consequences levels V and IV respectively accounting for 10.97 % and 16.77 %. The supply-demand balance analysis identifies the proportion of the first and second types of imbalanced pipes, respectively 14.24 % and 6.80 %. This method, by analyzing the relationship of health and failure consequences, provides a decision-making basis for drainage authorities to prioritize sewer pipe operation management, repair, and maintenance. • Supply-demand balance to identify the relationship of health and failure consequences. • DEMATEL optimized AHP calculated weights by considering inter-indicator relationships. • Improved health evaluation via hydraulic metrics in drainage models, enhancing accuracy. • Identifying low(high) health/high(low) failure consequences areas for drainage authorities. [ABSTRACT FROM AUTHOR]