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[Display omitted] • 2010-2018: Ecological-economic coordination was highest in Hebei and lowest in Tianjin. • The ecological-economic coordination degree of various regions has obvious spatial spillover effects. • Improved functional and development tactics and policy reforms can strengthen ecological-economic development relations. The Beijing-Tianjin-Hebei region is an integral part of the ecology and economy of northern China. Unique geographical factors closely interlink the regions; however, limited attention has been paid to urban clusters' ecological and economic spatial linkages. Therefore, this paper introduces Ecosystem service value and Ecological-economic coordination degree, considering extensive spatial spillover effects within the region under study. This paper adopts spatial autocorrelation and the spatial Durbin model to detect the coordination relationship. Moreover, examine the influence of population growth, sales of consumer goods, health facilities, green areas, and Tertiary Industry on Ecological-economic coordination. The relationship is tested economic growth, using the internal and external Thus, it determines that the change of coordination degree shows a "V"-shaped development in time and space and gradually realizes the overall coordinated development. It affirmed the judgment that the Ecological-economic coordination degree of each region has an obvious spatial spillover effect. Policy reforms, improved functions, and development mechanisms can enable the balanced and healthy development of the region's ecological and economic systems. [ABSTRACT FROM AUTHOR]

• Accurate risk path forecasting in rail transit systems is a significant and challenging task. • A probabilistic deep learning framework that can process multisource data for risk path forecasting. • The model can effectively handle safety issues such as cascade failures. Failure-related urban rail events can disrupt transit system operations and traffic flow and lead to serious safety problems worldwide. A domino effect can occur if potential cascading events of major failures are not effectively mitigated and controlled. Therefore, accurate risk path forecasting in rail transit systems is a significant and challenging task. Because of the limitations of traditional models in terms of computational power and feature extraction capabilities, this paper proposes a probabilistic deep learning framework that can process multisource data for risk path forecasting in urban rail transit. This paper first proposes a method for constructing a large-scale risk path ground truth dataset when fault events occur. Then, the framework uses a graph-based feature mapping method to model social media, passenger flow, and station failure information. Finally, we proposed a spatiotemporal feature extractor and a dynamic difference weighting loss function to extract features and optimize parameters. We apply real-world data from 2018 to 2019 from the Beijing urban rail transit system for experimental analysis. The analyzed results demonstrate that the proposed model exceeds the baseline models by at least 2.9% in terms of F 1 value, fusing multi-source data exceeds using single-source data by at least 14% in terms of F 1 value and the proposed attention mechanism and dynamic loss function weights can effectively improve the forecasting performance of the model. Furthermore, the results of the different steps ahead demonstrate that the above results are robust. The application of the model can effectively handle safety issues such as cascade failures. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty. • Proposed uncertainty quantification models for the source and load nodes of DHS. • Developed a novel credible interval calculation method for nodes' thermal power. • Quantified nodes' temperature dynamic response considering source-load uncertainty. [ABSTRACT FROM AUTHOR]

Road disruptions frequently occur in practice, resulting in a productivity loss of bus services and widespread passenger delays. These negative impacts are significant, especially when multiple routes are influenced at the same time. In order to mitigate these impacts, this paper proposes a multi-route coordination approach that collaboratively adjusts multiple bus routes and optimizes bus timetables to provide effective alternative bus services for passengers. Three adjusting strategies are adopted for bus routes with varying passenger demand: detouring, short-running, and cancellation. To address the multi-route coordination problem, a column-generation-based two-stage framework is developed. Concretely, a column generation technique is utilized in the first stage to iteratively generate candidate adaptive bus routes and passenger paths. Following that, an integrated integer programming model is built in the second stage to simultaneously determine bus timetables and the combinations of those adaptive bus routes. After realizing the low computational efficiency for large-scale problems, this paper designs a customized decomposition algorithm based on set partitioning to solve the presented model and obtain near-optimal solutions efficiently. Finally, the proposed methodology is applied to an illustrative Sioux Falls network and a real-world bus network in Beijing to verify its validity and effectiveness. Three comparative analyses are conducted to discuss the advantages of the three adjusting strategies, to investigate the benefits of coordinating bus timetables, and to explore the applicability of different adjusting strategies, respectively. • A multi-route coordination problem for bus systems in response to road disruptions. • A column-generation-based two-stage framework to address the problem. • A customized decomposition algorithm based on set partitioning for model solving. • The framework and algorithm are applied to a real-world bus network in Beijing. [ABSTRACT FROM AUTHOR]

• This paper contributes to the understanding of the taste heterogeneity of travelers to pre-peak discount pricing strategy. • Naturalistic data from smart card data users in Beijing before and after a policy intervention is used in this regard. • Four groups of travelers with heterogeneous characteristics are classified by constructing a latent class model. • Different targeted policy recommendations for each group of travelers are proposed in order to improve the policy effect. Studies of travelers' response behavior to transportation demand management is receiving substantial attention among researchers and transport operators in recent years. While previous studies in this area have generally assumed that the sensitivity of travelers to different factors is homogeneous and relies on survey responses, which may be prone to self-reporting errors and/or subject to behavioral incongruence. Relying on naturalistic data, this paper aims to investigate the behavioral response to pre-peak discount pricing strategy in the context of the Beijing subway with a special focus on the heterogeneity among the travelers. Anonymous smart card data from 5946 travelers before and after the introduction of a peak avoidance policy in Beijing are used to construct a latent class choice model to capture the sensitivity to different factors and the associated taste heterogeneity of travelers. Given the passive nature of the data, the model can offer more realistic outputs. The results indicate that there is substantial heterogeneity in travelers' responses to the peak avoidance policy, and that they can be probabilistically allocated to four latent classes. For all classes of travelers, the decision to shift their departure to off-peak is affected by the monetary saving, the required change in departure time and the frequency of travel, but in different magnitudes. In particular, only two classes of travelers (who exhibit lower standard-deviation in pre-intervention departure time) show significant sensitivity to price changes indicating that the discount policies are more likely to be effective for these groups. The rest of travelers are largely price insensitive – warranting the need for non-monetary incentives as opposed to fare discounts. To the best of our knowledge, this study is the first to innovatively apply the LCC framework to analyze travelers' heterogeneous behavior using large-scale smart card data without socio-demographic information. The findings can provide guidance to the subway authority in devising differential peak avoidance policies targeted for different groups of users, which are likely to be more effective than the current 'one size fits all' approach. [ABSTRACT FROM AUTHOR]

The heat transfer deterioration caused by frosting is the critical factor affecting the liquefied natural gas (LNG) vaporization efficiency of the ambient air vaporizer (AAV). In this paper, an LNG vaporization system using a solar-assisted heat pump (DX-SAHPNV) was proposed for AAV defrosting and LNG auxiliary heating, and a low-temperature LNG bypass flowing through the collector/evaporator was adopted for heat pump performance improvement and LNG preheating. Moreover, a dynamic model of the DX-SAHPNV system was established. Based on the meteorological data in Beijing, the system performance throughout the year and on typical days was analyzed, as well as the main meteorological factors effecting the system performance. Results showed the annual COP and the heat collection efficiency of collector/evporator with LNG bypass were 10.77% and 12.84% higher than that without LNG bypass, respectively. Comparing with the conventional AAV, the two-phase length of AAV in the DX-SAHPNV system was reduced by 12.7% and 20.2% on the typical winter and summer day, while the outlet LNG temperature was improved by 5.33 K and 11.56 K. The proposed system may provide a new perspective for the AAV defrosting and LNG vaporization optimization. [ABSTRACT FROM AUTHOR]

• CO 2 air conditioning systems driven by compressed air engines are designed innovatively. • CO 2 air conditioning system driven by compressed air engine can significantly reduce running costs. • Combination of air conditioning system and compressed air storage is promising. Traditional building air conditioning uses electricity or thermal energy to drive the refrigeration cycle. The energy released by the expansion of compressed air can also be used for driving the refrigeration cycle. Moreover, the expanded low-temperature air can be used in the air conditioning system too. In this paper, three forms of CO 2 air conditioning systems driven by novel reciprocating piston compressed air engines were designed, namely EAGC (Exhaust Air for Gas Cooler), EAS (Exhaust Air for Sub-cooler), and EASC (Exhaust Air for Space Cooling), to analyze the performance and economics of CO 2 air conditioning systems combined with compressed air energy storage technology. Twenty-seven typical cities were selected in different climate regions in China to evaluate the systems' performance and running cost under outdoor temperature changes and different electricity prices. The results show that compared with the electricity-driven CO 2 air conditioning system, the three systems can greatly improve the system COP (Coefficient of Performance) but still cannot reach the performance level of the conventional freon refrigeration systems. However, in cities like Beijing and Shanghai with the large peak-to-valley electricity price difference, EASC system compared to the electricity-driven R410A air conditioning system running costs are lower, the average running cost per hour can be saved 11.96% and 16.35%, respectively. This means that the CO 2 air conditioning system with compressed air energy storage technology has a certain economic application value and can be popularized on reasonable occasions. This innovative system design can help solve the contradiction between supply and demand of building energy, realize leisure energy consumption, and reduce the economic cost of energy consumption on the demand side. [ABSTRACT FROM AUTHOR]

• This paper is a large scale clinical studies between cytokines and prognosis of Kawasaki disease in China. • The prominent feature of this paper is the large amount of data. • The number of mediators detected in this study is large. Kawasaki disease (KD) is a type of pediatric vasculitis. Ten to twenty percent of children with KD do not respond to initial intravenous immunoglobulin (IVIG) treatment which called refractory Kawasaki disease. If untreated, approximately 15–25% of KD patients have complications. Therefore, it is important to predict whether KD is resistant to IVIG at an early stage. The aim of this study was to determine whether cytokines are predictors of refractory KD in children. We retrospectively reviewed the medical records of 265 children diagnosed with KD who received IVIG within 10 days of fever onset at Beijing Children's Hospital. Refractory Kawasaki disease was defined as persistent or recrudescent fever beyond 36 h after IVIG. Before IVIG and 3 days after temperature normalization following IVIG treatment, the concentrations of cytokines in the serum including interferon gamma (IFN-γ), tumor necrosis factor-alpha (TNF-α), interleukin-10 (IL-10), interleukin-6 (IL-6), interleukin-4 (IL-4), interleukin-2 (IL-2) and other conventional inflammatory mediators including white blood cells counts (WBC), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and albumin were detected. The patients were divided into 2 groups: IVIG-sensitive group and refractory group. Of the 265 patients, 47 (17.7%) were refractory. After treatment with IVIG, the serum levels of IFN-γ, TNF-α, IL-10 and IL-6 in both groups were significantly lower than those before treatment (P < 0.05). Before treatment, the serum levels of IFN-γ, TNF-α, IL-10 and IL-6 in refractory group were significantly higher than those in IVIG-sensitive group (P < 0.05). There were no significant differences in IL-4 or IL-2 levels between the 2 groups. The results of logistic regression analysis showed that IFN-γ, IL-6, fever duration and serum albumin were independent risk factors for refractory KD. The area under the receiver operating characteristic curve (ROC curve) for IFN-γ was 0.781, and the cut-off value for refractory was 7.37 pg/ml, while the area under the ROC curve for IL-6 was 0.837, and the cut-off value for refractory was 70.13 pg/ml. IFN-γ and IL-6 were independent risk factors for refractory Kawasaki disease in children. KD patients with serum levels of IFN-γ above 7.37 pg/ml and IL-6 above 70.13 pg/ml before treatment were prone to refractory. [ABSTRACT FROM AUTHOR]

Residential mobility has become an increasingly important issue in China, where urban families participate frenetically in the commodity housing market leading to soaring property prices. Applying place dependence approach and logistic regression models, this paper examines the association between residential mobility intentions and place dependence for employment and education in Beijing. The results reveal that people's intentions to move homes are driven by place dependence on the access to high-quality schools for their children, while the dependence for employment is rather weak. The findings indicate children's education has emerged as a new driving force that shapes urban space in contemporary China and therefore should be a focal point for planners and policy makers in urban resource distribution. This paper provides evidences to further our understanding of the high premium of properties that enable access to good schools. The needs for other spatial utilities and sentimental attachment to a place also encourage residential mobility intentions, which deserves further research. • Residential mobility intention was analyzed in Zhongguancun area in Beijing. • Influences of children's education and parents' employment were measured. • Children's education has emerged as a new driving force affecting residential choices. • Place dependence provides a useful lens to comprehend housing mobility in contemporary cities. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the key issues relating to sustainability and livability in many large cities. In particular, the situation in the growing megacities of developing countries has been worsening and is now attracting considerable attention from researchers and politicians. An understanding of the spatio-temporal patterns of this congestion is necessary in order to formulate effective policies to relieve it. Much of the research to date has focused on single districts for relatively short periods (days or weeks) using GPS, while long-term analysis of spatial and temporal patterns of traffic congestion at the city level has been rare. The aim of this paper is to help fill this gap in the literature by applying a big data analytic approach to a sample of 10.16 million records of traffic congestion indexes for 233 roads in the Beijing area over a six-month period. This analysis revealed four typical traffic congestion patterns in Beijing, which can be described as the weekend mode, holiday mode, weekday mode A, and weekday mode B. Each of these patterns possesses unique spatial and temporal characteristics. Compared with working days, on which congestion is regular and agglomerated, weekends and holidays are characterized by long-lasting congestion peaks throughout the day. Non-commuting travel on weekends and holidays, including trips for tourism, shopping, entertainment, and children's after-school activities, are major contributors to traffic congestion of the weekend and holiday mode. Owing to poor jobs-housing balance, the suburban new towns and job centres had relatively higher congestion than other areas. These findings shed significant light on geographical patterns of traffic congestion in growing megacities. • Traffic congestion is a key issue relating to urban sustainability. • This paper apples a big data analytic approach to a sample of 10.16 million records of traffic congestion. • There are four typical traffic congestion patterns in Beijing. • No-commuting travels on weekends and holidays are major contributors to traffic congestion. • The suburban new towns and job centres had relatively higher congestion. [ABSTRACT FROM AUTHOR]

Urban rail transit (URT) plays a pivotal role in facilitating human mobility within urban environments. It is significant to understand its vulnerability, i.e., the variation in capacity and demand when confronted with unexpected events, particularly operational disruptions. Although the network topology is generally fixed, the hourly-changing travel demand greatly impacts the actual vulnerability of a URT system. Unfortunately, few existing studies consider the combined influence of dynamic travel demand and network topology. To fill the gap, this paper proposes a network vulnerability assessment method with the joint consideration of static network topology and dynamic travel demand. This method includes a defined reasonable path, an accessibility-based identification of station importance with time-varying passenger demand, and a new dynamic vulnerability delay index considering affected travel demand. An empirical analysis was carried out by taking the URT system of Beijing, China as an example, and the impact of the more realistic multiple consecutive station failures in a URT system is also examined. Results show that the distribution of high-importance stations indeed varies with the time of day, affected by both static topology and hourly-changing passenger flow. When the disturbance of operation delay occurs, the impact of high-importance stations on the network vulnerability changes nonlinearly with the increase of delayed travel demand. Some stations that serve as bridges and are visited by large passenger flows have the greatest impact on network vulnerability. Network performance degradation is obviously segmented and stratified in the case of interval continuous failure. The disruption between different lines is the main cause of network performance degradation, and some high-importance stations within the lines act as catalysts to accelerate the performance degradation. The proposed method not only offers a valuable reference for quantifying network vulnerability arising from fluctuations in passenger mobility but also introduces a novel vulnerability evaluation index to the URT system. [ABSTRACT FROM AUTHOR]

• Long-term operational monitoring of a Zero Emission Building (ZEB) in Beijing. • An existing building being retrofitted into a Zero Emission Building (ZEB). • Key Building Mounted Photovoltaic (BMPV) technologies are practically utilized and their effects are estimated. • The ZEB realized its own net-zero carbon emission and net energy production. • The ZEB could be a comprehensive experimental platform. Building mounted photovoltaic (BMPV) technology is a promised way to achieve carbon peak in building sector. Focused on the influence of heat from PV modules on building envelope's heat transfer and energy saving performance, and the power generation, the coupling mechanism of power generation and heat transfer of integrated photovoltaic technology on building façades has been investigated. The synergy of solar energy generation and building energy consumption have been studied in this paper. The installation method of building PV system was proposed, with which can achieve the maximum energy saving. Furthermore, the achievements have been applied in Zero Emission Building (ZEB) of China Academy of Building Research (CABR), demonstrating way to realize zero carbon building by integration of photovoltaic technology. Total of 236.1 kWp PV systems were installed in ZEB. Monitoring data shows that the annual power generation of ZEB could reach 212,382 kWh, and the power generation per building area is 73.79 kWh/m2, which could meet all the energy demands. In addition, it can supply extra power to surrounding buildings. The ZEB can provide experience and practice data supporting carbon peak in building sector. [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]

• Experiments contrasted ten machine learning models in susceptibility prediction. • Hybrid ensemble models significantly outperformed widely-used individual models. • Study profiled the first urban waterlogging susceptibility map in Beijing, China. • Urban road network and densely populated areas face severer waterlogging risk. Urban waterlogging has emerged as a significant problem worldwide, particularly in densely populated cities. Accurate assessment of waterlogging susceptibility at the city scale is crucial for mitigating the risks associated with waterlogging and optimizing municipal design accordingly. However, existing studies on urban waterlogging susceptibility assessment have primarily relied on individual machine learning models. It is worthwhile to explore whether hybrid ensemble models have the potential to enhance the predictive performance. This research presents two hybrid ensemble machine learning models, namely Stacking and Blending, for assessing urban waterlogging susceptibility in the metropolitan area of Beijing, China. The performances of these models are compared with those of the widely used individual machine learning models. Evaluation of all the models is based on metrics such as Accuracy rate and Area Under Curve (AUC) score. The results demonstrate that the Stacking and Blending models consistently outperform the traditional machine learning models, such as Random Forest, Logistic Regression, etc. Through susceptibility analysis and model interpretation with SHAP method, this paper obtains several key findings that low lying areas may not necessarily be areas with severe waterlogging; urban roads and densely populated areas are highly susceptible to becoming high-risk areas for waterlogging in the study area. This study not only highlights the effectiveness of the Stacking and Blending models for urban waterlogging susceptibility assessment but also provides valuable insights for waterlogging mitigation strategies in urban planning. [ABSTRACT FROM AUTHOR]

In the traditional PV/T system, there is a certain contradiction in the collection process of heat and electric energy. This paper describes a spectral splitting PV/T system, which effectively alleviates the negative effects of high temperature on monocrystalline silicon cells, and separates the soalr radiation so as to improve the utilization efficiency of full spectrum energy. Then, through the all-day experimental study, the results show that the maximum temperature of the spectral splitting medium can reach 38.21 °C, the maximum thermal and electrical efficiency were 38.7 % and 11.77 %, respectively. The output power of the PV cell is more affected by solar radiation, and the filling factor is more affected by the temperature of the battery. In addition, the mathematical model is established and verified. The results show that the optimal nanofluid concentration of the system is 1*107 μg/m3 and 5*106 μg/m3 in Xining and Hefei, respectively. When applied in Beijing area, the optimal nanofluid concentration is 1*106 μg/m3 in summer and 1*107 μg/m3 in winter. Finally, we calculated the annual electric energy and heat output in the three regions using the best nanofluids, namely 126.6 MJ/m2 and 1186.68 MJ/m2 in Xining, 154.44 MJ/m2 and 948.96 MJ/m2 in Hefei, 164.46 MJ/m2 and 1062.43 MJ/m2 in Beijing. • Experimental and numerical investigation on spectral splitting PV/T system under natural conditions. • Excellent thermal and electrical performance, loss coefficient of heat energy. • The applicability of spectrally selective nanofluids in the system were evaluated. • Optimal nanofluid concentration for different regions based on annual performance analysis. [ABSTRACT FROM AUTHOR]

Green space accessibility affects housing prices, which is an issue that has been underappreciated. This paper takes Beijing's central urban area as an example. First, the Gaussian-based two-step floating catchment area (G2SFCA) method is used to measure four levels (community green space, subdistrict green space, district green space, and municipal green space) of green space accessibility by multiple travel modes (walking, cycling and driving). Second, the spatial error model (SEM) is used to examine the factors influencing housing prices. Finally, the geographically weighted regression (GWR) model is introduced to explore the spatial relationship between green space accessibility by walking, cycling and driving and housing prices. The results show the following: (1) The SEM shows that only driving accessibility has a positive spatial spillover effect on housing prices, indicating that the spatial inequity of local community green spaces by driving will exacerbate surrounding spatial inequity. (2) The GWR model shows that from the perspective of walking and driving accessibility, most areas within the Second Ring Road show spatial equity in green spaces, while cycling accessibility shows that the spatial inequity of green space exists only outside the Second Ring Road. (3) We find that municipal green space and subdistrict green space most affect the spatial equity of green spaces. Community green space, which is not open to the public, plays a minimal role. Regarding walking and driving accessibility, municipal green space increases the spatial equity of green spaces. In contrast, this type of green space can promote the spatial equity of green spaces regarding cycling accessibility. This study can significantly improve the quality of urban settlements in the Global South and has important implications for policy-makers, planners, and stakeholders in other large cities worldwide to create green and liveable cities. • We investigated green space accessibility impact on housing prices • G2SFCA was used to measure green space accessibility for multiple travel modes • Driving accessibility had positive spatial spillover effect on housing prices • Driving and cycling green space accessibility had opposite effect on housing prices • Our results provided guidance and example for other developing countries megacities. [ABSTRACT FROM AUTHOR]

The High Energy Photon Source (HEPS) is a 6 GeV diffraction-limited synchrotron light source under construction in Beijing. It will use five 166.6 MHz superconducting radio frequency (rf) cavities to accelerate the 200 mA electron beam in the storage ring. In the current lattice design, each straight section is 6 meters long to accommodate two 166.6 MHz cavities. The cavity adopts β =1 quarter-wave geometry, and the damping of the higher order modes is realized by using an enlarged cavity beam pipe to 505 mm and a ferrite absorber installed on the beam pipe. The low rf frequency, as well as the large beam aperture, added great challenges to the design of the 166.6 MHz cavity module to fit into a strictly limited longitudinal space. In addition, heat load optimization for the transition from room temperature to the cryogenic temperature, smooth tapering from the 505 mm beam aperture to the small diameter of the gate valves for impedance control, proper collimation of the synchrotron light to prevent thermal damage of delicate components, are also demanding requirements for a compact design. Accessibilities are also preserved for tuners and vacuum components to enable online maintenance. Finally, rf field leakage in the vacuum seal groove of the cavity is carefully examined to prevent overheating on the superconducting flange, which, in the worst scenario, may cause a thermal runaway, thus limiting the achievable accelerating gradient of the cavity. This paper presents the first compact design of the 166.6 MHz srf section for HEPS. [ABSTRACT FROM AUTHOR]

In the early morning on December 17, 2020 Beijing time, China's chang'E-5 probe successfully returned to the Earth with 1731 g of lunar samples after completing drilling, shoveling, packaging of lunar soil and scientific exploration on lunar surface. It is the successful completion of the third phase of China's lunar exploration project, namely "circling, landing and returning to the moon". The scientific objectives of CE-5 mission are to carry out in situ investigation and analysis of the lunar landing region, laboratory research and analysis of lunar return samples. This paper analyzes scientific exploration tasks of CE-5 mission conducted on the lunar surface, and carries out the scientific payload system architecture design and individual scientific payload design with the scientific exploration task requirements as the target, and proposes the working mode and main technical index requirements of the scientific payloads. Based on the preliminary geological background study of the Mons Ruemker region which is the landing region of CE-5, the lunar scientific exploration and the laboratory physicochemical characterization of the return samples are of great scientific significance for our in-depth understanding of the formation and evolution of the Earth-Moon system and the chemical evolution history of the lunar surface. [ABSTRACT FROM AUTHOR]

• Owning a car is not related to increased life satisfaction in Beijing. • However, owning more than one car weakly contributes to higher life satisfaction. • A more expensive car does not influence travel and life satisfaction. • Infrequent car use is related to higher travel and life satisfaction. • Attitudes towards cars confound the effect of car ownership and use on well-being among car owners. Promoting subjective well-being is an important goal in urban policy. Transportation scholars increasingly evaluate transportation systems based on people's subjective well-being. This study joins this strand of efforts to answer the following research question: how do constrained car ownership and car use influence travel and life satisfaction? Empirical data were collected in Beijing where various policies had been implemented to curb the rapid growth of car ownership and mitigate traffic congestion. We answer the question by examining the effects of car ownership and car usage variables on travel and life satisfaction. Our study shows that owning multiple cars increases vehicle users' life satisfaction, but owning a car is not related to increased life satisfaction. Purchasing a more expensive car and driving cars more frequently in daily life could not increase people's life satisfaction, but infrequent car use contributes to higher travel and life satisfaction. Among vehicle users, using cars to commute is related to higher travel satisfaction but not higher life satisfaction. In contrast, using cars for non-work trips is related to higher life satisfaction but not travel satisfaction. We also find that attitudes towards the instrumental and affective roles of cars are positively related to travel and life satisfaction among car owners. Results of this paper suggest the importance of promoting sustainable transportation and behavioral education in cities like Beijing. [ABSTRACT FROM AUTHOR]

With the rising urge to mitigate the green house effect, reducing the energy consumption of the freight train attracts much attention. Multiple constraints should be taken into account to solve the energy-efficient control problem, which can be reformulated as the multi-objective optimization. This paper proposes a Reinforcement Learning (RL) method for the multi-objective speed trajectory optimization with the goal of the energy-efficiency, punctuality and accurate parking simultaneously. Since the solution space for the optimization problem in this paper is large and discrete, a Gated Recurrent Unit (GRU)-based network is proposed to achieve the fast approximation of the optimal value function instead of the lookup Q-table. Meanwhile, a new architecture, including the embedding matrix, is used to model the control sequence that generates the speed trajectory. Besides, this paper constructs a Deep Q-Network (DQN) framework to train the GRU network without relying on the prior knowledge of the freight train model. Finally, the Intelligent Train Operation (ITO) algorithm is proposed and verified, using the data of Beijing–Guangzhou Railway Line and HXD1B electric locomotive. The case studies indicate that the reward function for the ITO algorithm converges rapidly and the energy consumption monotonically decreases with the trip time, which satisfies the multiple optimization objectives. In terms of saving the energy consumption, the ITO algorithm performs better than Fuzzy Predictive Control (FPC), Genetic Algorithm (GA) and the field test data. The computation time of different speed trajectories demonstrates that the ITO algorithm is applicable to generating the optimal speed trajectory off-line. • A novel multi-objective optimization model based on the reinforcement learning is proposed to search for the optimal control sequence. The embedding matrix is used to formulate the control sequence. • An intelligent train operation algorithm based on the gated recurrent unit network is proposed to compute the optimal speed trajectory without the precise train dynamics or the prior knowledge from the experienced drivers. • The energy consumption is reduced by 3.17%, 0.71% and 6.77% respectively comparing with fuzzy predictive control, genetic algorithm and field test data. [ABSTRACT FROM AUTHOR]

• This paper examines the integrated optimization of train stopping plan and seat allocation. • The integrated optimization problem is formulated as a mixed-integer mathematical model. • The integrated optimization approach improves efficiency against separate optimization of stopping plan and seat allocation. • Numerical studies were conducted on Zhengzhou-Xi'an and Shanghai-Beijing high-speed railway corridors. This paper examines the integrated optimization of train stopping plan and seat allocation scheme in railway systems, where equilibrium passenger travel choice under elastic demand is considered. The integrated optimization problem is formulated as a non-concave and non-linear mixed-integer mathematical model, where the objective is to maximize the system net benefit considering ticket revenue, consumer surplus, and cost associated with train stoppings. The integrated optimization model can be reformulated into a mixed-integer linear programming (MILP) model based on a series of linearization, relaxation, and outer-approximation techniques, which can then be solved by commercial MILP solvers (e.g., GUROBI). We also compare the integrated optimization approach with that when the train stopping plan and seat allocation are optimized separately and identify the potential benefits. Numerical studies have been conducted on a small-scale example, the Zhengzhou-Xi'an and Shanghai-Beijing high-speed railway corridors to illustrate the proposed model and solution approach. [ABSTRACT FROM AUTHOR]

For traditional air cooling/heating process in HVAC system, only single temperature of water generated by mechanical equipment is used to handle air cooling/heating load. However, there is the potential to simultaneously use waters at different temperatures to finish air cooling/heating process, which has shown a significant energy-saving effect in previous studies. Currently, there is no theoretical method for efficiently guiding the simultaneous use of multiple energies, especially natural/waste energies, for handling air cooling/heating load. Thus, this paper proposes a construction method for air cooling/heating process based on grade match between energy and load (GMEL). Considering the air cooling/heating process of typical electronic factory in Beijing as example, new air cooling/heating systems are constructed according to load grade and local natural/waste energies. Furthermore, the energy use is compared with that of traditional air cooling/heating system. The results reveal the following: (1) The GMEL method can construct air cooling/heating process that fully uses natural/waste energies to directly handle air load as much as possible, applies water at different temperatures generated by mechanical equipment to handle the corresponding grade of load, and realizes mutual utilization of air cooling and heating loads. (2) The energy-savings rate of the constructed air cooling/heating system of typical electronic factory in summer/winter is 26.7%/52.4%. The static payback period is 0.83 year. (3) The specialized air cooling/heating processes are different for different local natural/waste energies and economic condition. The method will promote the full usage of renewable energy worldwide, and significantly improve the energy efficiency of mechanical cooling/heating source. [ABSTRACT FROM AUTHOR]

• Propose the concepts of line strategy (LS) and node strategy (NS) and a novel methodology adopting the decision variables of LS and NS proportions. • Impose transfer constraints explicitly and mix all the passengers with the same maximal number of transfers together. • Establish the LNS (line and node strategy) -based model on the original transit networks and reduce the scope of the feasible transfer nodes by the maximal transfer constraints. • Adopt both the method of successive averages (MSA) and a projection algorithm to solve the LNS-based model. • The performance of the LNS-based model and algorithm on a transit subnetwork of Beijing showcases its huge advantage. Passengers traveling on transit networks involve two kinds of decision-making strategies: deciding which lines are attractive at an origin or transfer node (denoted line strategy, LS), or deciding which node to transfer at when riding a line (denoted node strategy, NS). Combining these two strategies, this paper proposes a novel variational inequality formulation for the user equilibrium passenger assignment problem. The inclusion of the NS eliminates the need for passenger assignment on a large augmented graph, reducing the modeling complexity and making it easier to track all passengers' travel routes. Moreover, constraints on the maximal number of transfers—which are crucial in practical decision-making on transit networks—are explicitly included, further drastically reducing the set of passengers' feasible strategies. Furthermore, some extant strategy-based transit assignment models are shown to be a special case of the proposed model when the transfer constraint is removed. Finally, the properties of the proposed model are illustrated on a small network, and the model and algorithm exhibit huge advantages on the chosen transit subnetwork of Beijing. [ABSTRACT FROM AUTHOR]

• A bus bridging approach to cooperate with the first train operations in a subway network. • An explicitly integrated MILP model for the first train and bus bridging timetabling problems. • A tailored algorithm for optimally solving the first train timetabling and bus service bridging problems. • The model and the approach are applied to the Beijing subway network. Subway system is the main mode of transportation for city dwellers and is a quite significant backbone to a city's operations. One of the challenges of subway network operation is the scheduling of the first trains each morning and its impact on transfers. To deal with this challenge, some cities (e.g. Beijing) use bus 'bridging' services, temporarily substituting segments of the subway network. The present paper optimally identifies when to start each train and bus bridging service in an intermodal transit network. Starting from a mixed integer nonlinear programming model for the first train timetabling problem, we linearize and reformulate the model using the auxiliary binary variables. Following that, the bus bridging model is developed to cooperate with the first train operation for reducing long transfer waiting times. After realizing the low computational efficiency of solving the integrated model, a tailored algorithm is designed to optimally solve the first train timetabling and bus service bridging problems. The exact models and algorithms are applied to the Beijing subway network to test their effectiveness and computational efficiency. Numerical results show that our approaches decrease the total passenger waiting time by 53.4% by a combined effect of adjusting the first train departure times and operating 27 bridging buses on 7 routes. [ABSTRACT FROM AUTHOR]

• Deal with the unfairness problem of passenger waiting times in an oversaturated URT line from the point of view of the individual time-dependent passenger demand through timetabling optimization • Develop an approximate general model with a min-max type objective function to formulate the travel schemes of passengers and train timetabling (including the stop patterns) by clustering passengers into groups, where the problem scale is controlled by the defined aggregation granularity parameter • Design a preprocessing method to reduce the space of feasible solutions for small-scale cases and a solving algorithm based on variable neighborhood search for large-scale cases • To justify the choice of our min-max type objective function, a computational comparison between our model and the efficiency-oriented model was made. The performance of our VNS and the impact of the aggregation granularity parameter were investigated for different scales in both small instances and a real case study In oversaturated urban rail transit systems, passengers departing from downstream stations often experience long waiting times due to unbalanced space-time demand and limited transit capacity. This is often prevalent during morning and evening peak periods in transit systems. This paper aims to mitigate the unfairness of waiting time among a time-varying number of passengers through train timetable's adjustment by optimizing the train skip-stopping pattern. We develop an approximate general model by clustering passengers into groups and introducing an aggregation granularity parameter. To characterize feasible passenger travel patterns, both rigid first-in-first-out rule and capacity constraints are incorporated in the proposed model. Preprocessing is proposed to reduce the space of solutions. Some small-scale case studies show that the proposed method outperforms the original timetable and the preprocessing is effective to reduce computation time. Case studies based on the Batong line of Beijing rail transit network are conducted, in which a variable neighborhood search algorithm is applied to obtain high-quality solutions in short computing times. The results show that the proposed approach not only mitigates the unfairness of waiting time among passengers but also improves other efficiency evaluation indexes, including the average waiting time and the maximum number of missed trains. We also investigate the impact of the aggregation granularity parameter on the computational effort and solution accuracy. [ABSTRACT FROM AUTHOR]

Evaluating the dispersion of SiC nanoparticles is significant for the development of ZrB 2 –SiC nanocomposite ceramics. This paper reports the 3D distribution characterization of the nanosized SiC additives in ZrB 2 –SiC ceramics with synchrotron X-ray nanoscale computed tomography (nano-CT) and Morisita indexes. Different from the investigation with electron microscopy, the presented work implements nondestructively 3D visualization and quantitative characterization of the SiC nanoparticles distribution in ZrB 2 –SiC ceramics. Firstly, a ZrB 2 –SiC ceramic sample with a size less than 60 μm was prepared. Then, a nano-CT experiment was designed and implemented for this sample at the 4W1A beamline of Beijing Synchrotron Radiation Facility (BSRF). Next, SiC nanoparticles were extracted by applying image segmentation to the generated 3D nano-CT images of this sample and visualized by volume rendering. Finally, Morisita index was extended from 2D to 3D and applied to the corresponding nano-CT images for the quantitative characterization of SiC nanoparticle 3D distribution. The results from the Morisita index are in good agreement with the visual observation. They reveal that SiC agglomerates exist in the ZrB 2 matrix and vary over the regions of interest in the investigated sample. [ABSTRACT FROM AUTHOR]

• This paper shed light on assessing the safety benefits of connected vehicles under heavy fog condition. • The results suggest that individual characteristics have not significant effects on speed adjustment due to risk perception. • The results reveal that better driving performance and greater safety benefits could be achieved by warning system. • The positive effects are more significant when the OBU is on. • In addition, the results could also facilitate the selection of a proper information release format. This paper studies the effectiveness of fog warning systems on driving performance and traffic safety in heavy fog condition. A comparison study was conducted for four scenarios in heavy fog condition. First, a series of indexes corresponding to driving speed adjustments and surrogate measures of safety was obtained to explore the impacts that fog warning systems have on driving behavior and traffic safety when approaching a fog area. This study divided the analyzed road into three different zones (clear zone, transition zone, and fog zone) according to visibility levels. Then, multivariate analysis of variance (MANOVA) was conducted, and the effects of drivers' individual characteristics on driving behavior were also investigated. Moreover, the linear mixed model with random effects was estimated to consider the contributing factors of the drivers' speed adjustment behaviors. In addition, the standard deviation of speed, TET (time exposed time-to-collision), and TIT (time integrated time-to-collision) were selected to evaluate the longitudinal safety. To obtain the driving data, an empirical driving simulator platform was established based on a real-world road in Beijing. Thirty-five drivers were recruited to participate in the driving experiment. The results showed that the cooperative vehicle-infrastructure warning systems could be beneficial to better driving behavior and safer traffic operations. The results revealed that the warning systems could be beneficial to speed reduction before entering a fog area. In addition, the On-Board Unit (OBU) had a significant impact on individual speed adjustment. Moreover, the results showed that scenarios with fog warning systems improve safety significantly over the no warning system scenario. The study results could also facilitate the selection of a proper information release format in the context of connected vehicles. [ABSTRACT FROM AUTHOR]

• Proposed bundle pricing model considers unit price of landfill and incineration. • Bundling waste disposal fees can motivate the public to reduce generation of MSW. • Increase in revenue from bundling pricing model can alleviate the capital shortage. • Separate prices for landfill and incineration can promote incineration technology. Considering the various ways of Municipal Solid Waste (MSW) disposal that are currently in practice, this paper proposes a bundle pricing model of MSW disposal in Beijing, China. The bundle pricing model is set according to per capita annual disposable income, per capita annual consumption expenditure and MSW disposal cost based on the cost reimbursement principle and complementarity principle. This paper first estimates the unit costs of landfill and incineration respectively, and then designs three bundling packages according to three different income levels of households in Beijing (low-income households, middle-income households and high-income households). Specifically, based on our bundle pricing model, we propose to charge low-income households (of the average size of 3 persons) MSW fees 5.64 RMB per month, middle-income households 10.16 RMB, and high-income households 20.70 RMB per month. We also discuss and explain how the proposed mechanism can be expected to not only mitigate the capital shortage problem, but also reduce MSW quantity and improve the MSW incineration disposal technology. [ABSTRACT FROM AUTHOR]

Accurate PM 2.5 forecasting provides a possibility for establishing an early warning system to notify the public and take precautionary measures to prevent negative effects on ambient air quality and public health. Considering strong seasonal variation in meteorological conditions, in this paper, a seasonal stacked autoencoder model combining seasonal analysis and deep feature learning is proposed for forecasting the hourly PM 2.5 concentration, named DL-SSAE model. The original data are firstly decomposed into four seasonal subseries according to the Chinese calendar, and then the Kendall correlation coefficient method is employed to search inherent relationships between PM 2.5 concentrations and meteorological parameters within 1-h ahead for each seasonal time series. The inherent relationships of each seasonal subseries are finally extracted, learned, and modeled by different deep neural networks (stacked autoencoders for regression), and the hourly PM 2.5 forecasts are yielded. The addressed model is tested by the dataset collected from three environmental monitoring stations in Beijing, China. The results demonstrate that the proposed model outperforms all other considered models with/without seasonality consideration in this paper. [ABSTRACT FROM AUTHOR]

Abstract Given the ongoing "mobility turn" in social science research, a more comprehensive understanding of segregation is needed. Activity-space-based segregation studies have aroused renewed interests in geography and urban planning research. Most of the existing studies utilized the differences in spatial characteristics of people's actual activity space to identify the segregation among social groups. However, few studies have examined activity-space-based segregation in terms of individuals' potential activity space and the temporal variations in their segregation experiences. This paper aims to help fill these two research gaps by implementing an empirical study in Beijing. We examine the activity-space-based segregation of the residents of different types of housing in a Beijing suburb in terms of both actual activity space and potential activity space. We further investigate the temporal variations of the residents' segregation experiences during a week. A 7-day individual GPS tracking dataset, combined with the activity diary data and the socioeconomic attributes data of 422 participants is used for the study. The major finding is that residents of different types of housing in Beijing do experience activity-space-based segregation, while the characteristics of segregation vary with respect to actual activity space and potential activity space. Also, participants' segregation experiences varies between different days of the week. This paper offers some empirical evidence on enriching the understanding of activity-space-based segregation research as well as improving the understanding about social segregation among the residents of different types of housing in Chinese cities. It also generates some nuanced knowledge for future policy recommendations in a broad context. Highlights • Residents of different housing types in Beijing do experience activity-space-based segregation, while the characters of segregation varies in AAS and PAS. • The residents of affordable housing have both the smallest AAS and PAS on weekdays, which makes them the most isolated group. • With respect to the residents of market housing and institutional housing, they have relatively large AAS on weekdays while having relatively moderate PAS. • As for the residents of resettlement housing, their PAS are relatively large, although their AAS are small. • Significant differences in the extensity of both the AAS and PAS of the residents in different housing types can only be identified on weekdays. In weekends, differences in the extensity of the activity spaces are not significant. [ABSTRACT FROM AUTHOR]

With the development of social economy and health care technology, people are expected to live longer, and the world's population tends to be ageing. As one of the services for elderly people, the concessionary bus travel programme provides a kind of social welfare for elderly people, while peak-hour traffic pressures (e.g., traffic congestion, traffic injuries, and travel discomfort) faced by elderly people have been ignored. This paper attempts to help society create a better travel environment for elderly people by analysing their travel behaviours. Specifically, using household survey data from Beijing, the statistical analysis is used to paint a more detailed picture of daily travel experience of elderly people with regard to departure times, travel purposes, travel mode choices, etc. Then, a nested logit model is employed to interpret travel patterns of elderly people. The results show that the bus plays the most important role in motorized travel of elderly people. Furthermore, the free bus programme has induced only 4.85% of bus travel among elderly people, indicating that the programme has little effect on elderly people's bus choices. Based on the results of the analysis, several options are proposed in this paper to help society adapt to the changing demographics. [ABSTRACT FROM AUTHOR]

Abstract Electrifying urban transportation through the adoption of Electric Vehicles (EVs) has great potential to mitigate two global challenges, namely climate change and energy scarcity, and also to improve local air quality and further benefit human health. This paper was focused on the six typical factors potentially influencing the purchase behaviour of EVs in Beijing, China, namely vehicle price, vehicle usage, social influence, environmental awareness, purchase-related policies and usage-related policies. Specifically, this study used the data collected in a paper-based questionnaire survey in Beijing from September 2015 to March 2016, covering all of the 16 administrative regions, and tried to quantify the relative importance of the six factors, based on their weights (scores) given by participants. Furthermore, Multinomial Logit (MNL) models and Moran's I (a measure of global spatial autocorrelation) were used to analyse the weights of each factor from statistical and spatial perspectives, respectively. The results suggest that 1) vehicle price and usage tend to be more influential among the six factors, accounting for 32.3% and 28.1% of the importance; 2) Apart from the weight of social influence, the weights of the other five factors are closely associated with socio-demographic characteristics, such as individual income and the level of education; 3) people having similar attitudes towards vehicle usage (Moran's I = 0.10) and purchase restriction (Moran's I = 0.14) tend to live close to each other. This paper concludes with a discussion on applying the empirical findings in policy making and modelling of EV purchase behaviour. [ABSTRACT FROM AUTHOR]

Highlights • The majority of carbon emissions derived from indirect fraction of energy consumption in Beijing. • Life expectancy is reduced in areas where a higher amount of coal is burnt for household consumption. • Socio-economic structure changes play a major role in the observed energy and emission trends. • Search of optimal solutions for energy-efficient and low carbon private will continue to be crucial. Abstract Household carbon emissions are major contributors to global emissions that is increasing recognized. In China, approximately 20 million rural populations transfer to the urban areas annually, which results in a lot of new infrastructure construction and consumer requirements. This paper quantifies the numbers and trends of urban and rural resident on energy use and carbon footprint in Beijing from 1996 to 2011 by analysis of Consumer Lifestyle Approach (CLA) and carbon footprint. It provides a better understanding of complete energy consumption and carbon emissions profile in Beijing. The results show that for the Beijing residents, when direct and indirect energy consumption and CO 2 emissions are taken into account, the total household energy consumption proportion compared to the city increases from 22.7% to 59.2%, and the total household CO 2 emissions proportion increases from 32.2% to 68.8% in the research period. In the urbanization process, total energy consumption and carbon emissions of Beijing urban residents are significantly higher than those of rural residents. However, the transformation of a rural Beijing resident into an urban Beijing resident would not create a large difference between urban and rural areas in the per-capita energy consumption and per-capita carbon footprint. Results are assessed with respect to the available scientific literature, as well as potential available technological and policy solutions. Since the implementation that different policy instruments can mitigate carbon emissions, governments are gradually combining policy measures for the same technology. The results in this paper may help promote alternative consumption strategies in household sector which could entail more reasonable resource distribution and effective reduction of both urban and rural environmental influences. [ABSTRACT FROM AUTHOR]

Abstract Urban gated communities have become increasingly common worldwide in both academic and policy discourse over recent decades. In contrast, gated communities in rural areas, a newer phenomenon, are rarely analyzed. This paper focuses on the unique phenomenon of gated villages that have emerged in the Beijing metropolitan area. We aim to explore the content, mechanism, heterogeneities etc. of gated villages, and how they differentiate from urban gated community. Five gated villages were selected for fieldwork and 27 semi-structured interviews were conducted with villagers, migrant tenants, heads of village committees, local government officers, and academics. Based on the framework of structuration theory, this paper analyzes the mechanism behind the formation of gated villages, which differ from urban gated communities. Based on the case studies, this paper concludes that: (1) the formation of the gated village is the culmination of interactions between complex dynamics of regional elements and the intellectual responses of various actors under the context of transitional rural China; (2) although the gated village appears similar to urban gated communities, it is in essence a new institutional tool of social management rather than a simple transfer of existing gated community structures into rural regions; (3) the consequences of the gated village are in general positive and can be enhanced by restricting physical size and by improvement of government-village co-operation. Highlights • Uncover the causes, mechanisms and consequences of gated village. • Distinguish rural gated village from urban gated community. • Clarify the co-evolution of institutions and actors in the formation and development of gated village. • Suggest that gated village can be an efficient institutional tool to improve rural wellbeing. [ABSTRACT FROM AUTHOR]

Abstract By virtue of the hukou reform issued by the State Council in 2014, a more people-oriented development path under a 'New Urbanization Model' is implemented during the 13th Five Year Plan. In a new grading regime, migrants' housing tenurial status plays a crucial role in differentiating who are qualified to enjoy the welfare entitlement (e.g. public education) in the host cities. Drawing upon the data from the 2011 and 2016 Migrant Dynamics Monitoring Survey, this paper investigates the rationale of the hybrid tenure continuum, its stratification mechanism among the whole migrant population in Beijing, and its dynamic change and impact on migrants given the undertaking of drastic measures by Beijing Municipality in the early 2010s to address its informality problems. A hybrid tenure structure from an insecure to de facto and to legal titles was proved critical to increase the resilience of housing supply for migrant groups. Policy implication of this paper is about how best to empower migrants as equal citizens. [ABSTRACT FROM AUTHOR]

Highlights • Explore the self-selection issue from the perspective of households. • Examine how two partners' attitudes affect residential and car ownership decisions. • Husbands' travel attitudes have more influence in these decisions than wives'. • The travel attitudes of two household heads can affect each other's travel behaviors. Abstract Residential self-selection has been acknowledged as a key issue underlining the link between the built environment and travel behavior. Existing studies often implicitly assume that residential choice is a decision of an individual and the influence of intra-household interactions is not considered. In reality, residential choice is a decision often made at the household-level involving more than one household members. Similarly, car ownership is usually a household-level decision incorporating views of different household members. In both cases, the travel related attitudes of more than one household members may play a role in the decision making. In other words, there is an issue concerning residential self-selection (similarly car ownership self-selection): whose self-selection? This paper makes an attempt to probe into this question by examining the self-selection issues concerning residential location choice and car ownership decision from the perspective of households, by incorporating the travel attitudes and travel behaviors of two household heads into an integrated self-selection framework. Data are derived from a household sample survey involving two-day activity-travel diaries conducted in 2016 in Beijing, China. Multivariate modeling results show that household members play different roles in household decisions with travel implications: in general husbands' travel attitudes have relatively more influence in residential choice and car ownership decision than wives'. Nevertheless, wives' travel attitudes have more power than husbands' in explaining the commuting distances of both husbands and wives. Some distinct features of the residential and car ownership self-selections in the Chinese context are also identified and discussed in this paper. [ABSTRACT FROM AUTHOR]

Abstract This paper studies the labour force in Beijing in relation to that in London. Through this comparative approach, the paper breaks the current isolation of Chinese studies, crosses the categorical divides in comparative research, and addresses local-global dialects in global cities. A very similar description of the labour force between these two cities is revealed. The labour forces in both cities had a young age profile with that in Beijing being only slightly younger, both shared a male-dominated characteristic with a stronger dominance in Beijing, and comprised primarily highly educated individuals who were slightly less represented in Beijing. Such a dominance of young, male and highly educated individuals in both cities is due to the two cities' similar attraction to these people as both now being a global city. The different extents of dominance in Beijing, however, are influenced by its various institutional practices. Yet with an aging population and a fast-developing urbanisation in China, these practices are very likely to be altered in the near future. It is therefore foreseeable that the labour force in Beijing will become yet more similar to that of London very soon. Based on these findings, it is untenable to claim Chinese cities as being greatly different. To largely exclude such cities from the comparative research is also no longer justifiable. [ABSTRACT FROM AUTHOR]

The "Reverse power flow" (RPF) behavior of distributed photovoltaics (DPV) promotes the new energy consumption and has a negative impact on the adaptability of the distribution network (DN). Researching and constructing a DN adaptability assessment model considering the DPV's RPF behavior is an important method to explore the weaknesses of DN adaptability from the perspective of management decision-making, and then propose improvement solutions. Firstly, this paper constructs a set of hierarchical DN adaptability assessment index systems from the three dimensions of security, economy and low carbon. Secondly, the combination of ordered weighted averaging improved the Analytic Hierarchy Process and the Entropy weight method is used to weigh the indicators. Then, an assessment method based on the combination of credibility theory and cloud model is proposed to solve the ambiguity and randomness in the evaluation process. Finally, the model was applied practically in the DN of Qiantuan Village in Beijing, and the adaptation of the DN is compared and analyzed under three scenarios: DPV not grid-connected, grid-connected and fully local consumption, grid-connected and RPF behavior. The results show that the adaptability levels of Qiantan Village DN are classified as "Medium", "Good", and "Good", with scores of 78.839, 86.312, and 88.287, respectively, under the three different scenarios. Furthermore, regarding the first-level indicators, the DPV's RPF behavior reduces the security of the DN but improves the adaptability in general. This paper's research can accurately locate the weak points based on reflecting the level of DN adaptability, thereby providing a reference for the formulation of a DN adaptability improvement plan and the construction of a clean and low-carbon DN. • Constructed a set of distribution network adaptability assessment index systems. • Propose a Combined weighting method by combining Ordered Weighted Averaging improved AHP and the Entropy weight method. • Established a distribution network adaptability assessment model based on the credibility theory and cloud model. • The adaptability evaluation model is applied to the distribution network of Qiantuan village in Beijing. [ABSTRACT FROM AUTHOR]

[Display omitted] • Evaluation framework for ULS external benefits is constructed. • Monetization calculation for ULS external benefits is proposed. • An empirical study is conducted based on the real data from Beijing ULS scheme. • External benefit can compensate for the ULS construction and operating costs. • Significant logistics benefits lead to multiple financing strategies for ULS. Underground logistics system (ULS) can effectively mitigate the negative effects of proliferating city logistics by integrating the management of urban freight flows through a fully automated and networked underground infrastructure. Although the considerable benefits of ULS have been well recognized at a macro level, the lack of quantitative research on ULS benefits makes it difficult to accurately measure the investment and return on ULS implementation. This paper utilized the service cost replacement method (SRCM) to construct a systematic evaluation index system for ULS external benefits, and developed a monetization calculation framework considering the attributes of ULS quasi-public infrastructure. Empirical data from the ULS scheme in Beijing was used to prove the reliability of the constructed ULS external benefits evaluation index system and the rationality of the benefit monetization calculation method. The results quantified the composition of the significant external benefits created by ULS, with the logistics benefits accounting for the largest proportion. The benefits evaluation framework of ULS constructed in this paper provides decision-makers with concrete knowledge of ULS benefits and multi-channel investment and financing strategies, as well as a method of monetizing the cost-benefit of quasi-public infrastructure. [ABSTRACT FROM AUTHOR]

Accurate assessment of SOE and SOH is a critical issue in the battery management system. This paper proposes an improved variable forgetting factor recursive least square-double extend Kalman filtering algorithm based on global mean particle swarm optimization to obtain a stable and accurate SOE and SOH at different aging levels and temperatures. Firstly, this paper establishes a framework for the parameter identification of variable forgetting factors recursive least squares algorithm based on the global mean particle swarm optimization. Then, proposing a global mean particle swarm optimization search mechanism centered on variable time double extended Kalman filtering. Finally, The proposed algorithm is validated on the hybrid pulse power characterization (HPPC) and Beijing bus dynamic stress test (BBDST) datasets. The experimental results show that the MAE and RMSE of the SOE results based on the HPPC condition are less than 0.0096 and 0.0153 at -5 °C and 15 °C. Similarly, the estimation results based on the BBDST condition are less than 0.0094 and 0.0102, respectively. The SOH estimation errors are less than 0.02. Therefore, the variable forgetting factor recursive least square-double extend Kalman filtering based on global mean particle swarm optimization algorithm can achieve accurate and stable SOE and SOH at different aging levels and temperatures. [ABSTRACT FROM AUTHOR]

• The Monte Carlo method is used to construct the charging load of EV in the planning area divided into four different vehicles type. • The Prim algorithm is adopted to search for the optimal network layout of energy transmission pipelines. • Proposed collaborative planning modeling promotes the time-of-use electricity price scheme to change the temporal and spatial characteristics of EV charging load. • The growth of EV charging load in the planning area has not increased the investment and operation-maintenance costs of DES equipment, and reduced carbon transaction costs as well as improved economic of system operation. • Simulations are conducted to verify the effeteness of proposed method. Under the dual-carbon goal, the number of electric vehicle (EV) has surged, and the uncertainty of the temporal and spatial characteristics of the load has become prominent, which brings the challenge for higher requirements to the equipment composition of distributed energy station (DES) and performance of station-network. This paper proposes a collaborative planning method for low-carbon DES with networks, which is focusing on the characteristics of EV charging behavior. First of all, based on the quantitative analysis of the temporal and spatial distribution of charging load of four typical types of EV, including private vehicle, public vehicle, taxi and bus, the Monte Carlo method is used to construct the charging load modeling of EV in the region area. In addition, the low-carbon DES is modeled equipped with photovoltaic (PV) and power to gas (P2G) devices, and the Prim algorithm is adopted to search for the optimal network layout of energy transmission pipelines in the region area. Under carbon trading mechanism, aiming at the lowest annualized total cost of the regional station-network, a station-network collaborative planning modeling strategy for the capacity allocation of low-carbon DES with networks is established. Finally, the case study for a project in Beijing China is implemented on MATLAB, to prove the effectiveness of the method proposed in the paper. The time-of-use electricity price scheme changes the temporal and spatial characteristics of EV load. For example, more than 2 MW the EV load can be brought into the valley price range, which reduces the carbon transaction cost and energy purchase cost of DES by 50 thousand yuan almost when peak-valley electricity price difference is close to 1.2 yuan. At the same time, the growth of EV charging load(the penetration rate of EV can reach up to 20%) in the planning area has not increased the investment and operation-maintenance costs of DES equipment. [ABSTRACT FROM AUTHOR]

As a newly-emerging travel mode in the era of mobile internet, ride-hailing that connects passengers with private-car drivers via an online platform has been very popular all over the world. Although it attracts much attention in both practice and theory, the understanding of ride-hailing is still very limited largely because of the lack of related data. For the first time, this paper introduces ride-hailing drivers' multi-day trip order data and portrays ride-hailing mobility in Beijing, China, from the regional and driver's perspectives. The analyses from the regional perspective help understand the spatiotemporal flowing of the ride-hailing demand, and those from the driver's perspective characterize the ride-hailing drivers' preferences in providing ride-hailing services. A series of findings are obtained, such as the observation of the spatiotemporal rhythm of a city in using ride-hailing services and two categories of ride-hailing drivers in terms of the correlation between the activity space and working time. Those findings contribute to the understanding of ride-hailing activities, the prediction of ride-hailing demand, the modeling of ride-hailing drivers' preferences, and the management of ride-hailing services. [ABSTRACT FROM AUTHOR]

• A multitask learning model for short-term inflow prediction of multi-traffic modes is proposed. • The adaptive matrix and attention mechanism are used to extract the spatiotemporal features of multi-traffic modes. • We analyze the interaction mechanism in the multi-traffic modes system. • The research highlights the significance of jointly predicting the inflow of multi-traffic modes. Network-wide short-term inflow prediction is important in efficiently managing the urban transportation system. Nowadays, all kinds of traffic modes gradually become interconnected and form a complex multi-traffic modes system, while extensive studies focus on the single-traffic mode and ignore the correlations among different traffic modes. There exist some challenges for short-term inflow prediction of multi-traffic modes: (1) the interaction mechanism among multi-traffic modes is difficult to learn and few studies explore the mechanism, (2) the data of multi-traffic modes are usually heterogenous due to the different spatial units of different traffic modes, and (3) it is challenging to extract the complex and dynamic features of the multi-traffic modes and most existing methods apply static spatiotemporal correlations among multi-traffic modes, while the genuine correlations among different traffic modes might be missing. To tackle these challenges, this study proposed a multitask-learning-based model called M ulti M ode-former (M2-former) with the encoder-decoder structure for network-wide short-term inflow prediction of the multi-traffic modes system. Specifically, the encoder is designed to learn and capture the complex and dynamic spatiotemporal correlations of multi-traffic modes, and the decoder is designed to extract the features of the target traffic mode and share knowledge among multi-traffic modes. Extensive experiments are conducted based on the real-world multi-traffic modes system data of Beijing, China. Results prove the superiority of the M2-former. In addition, the spatial and temporal information interaction mechanisms among multi-traffic modes are also explored. This paper can provide a reliable method and critical insights for the management and understanding of a multi-traffic modes system. [ABSTRACT FROM AUTHOR]

• A two-population 3-strategy evolutionary game model for subway network operation extension. • Theoretical analysis and numerical simulation of Evolutionary Equilibria for the replicator dynamics system. • The best long-term strategies of networks and key factors (motivations) analysis. • An extended game model for investigating the impact of adjustment minutes. With the growing demand for night travel and the development of the night-time economy, extending the operation time of metropolitan subway networks has become essential. This paper proposes a 2-population 3-strategy evolutionary game model for subway network operation extension decisions, which considers the interests of subway companies, passengers, and the government while addressing transfer failure between lines due to uncoordinated subway operation time adjustments. The study resolves the Evolutionary Equilibria (EEs) of the replicator dynamics system and identifies key factors influencing the evolutionary path using theoretical calculations and a ternary phase diagram. Additionally, an extended game model investigates the impact of adjustment minutes. To verify the models, a case study based on the Beijing subway network is conducted. We show that the rule of maximum total fitness ensures the priority of EE strategies for all lines, selecting Nash equilibrium strategies for the lines which cannot select EE in a vastly reduced network strategy profile space (from 729 to 2). The study concludes that combining subsidy measures can eliminate multiple equilibria produced by implementing only penalty measures, and combining penalty measures can have the effect of making small subsidies as effective as large subsidies. The variation of the satisfaction/loyalty affects the system equilibrium only when both game players value passenger satisfaction/loyalty. Finally, we find that the proper adjustment minutes not only can make operation extension a long-term best strategy but also increase the payoff (106,158 RMB/year) and enhance the occupancy rate (20 %) of the last train. The study provides a theoretical foundation for the government to regulate the operation extension of subway companies from multiple perspectives. [ABSTRACT FROM AUTHOR]

Uncertain demand may exacerbate the imbalance of the supply–demand for a one-way carsharing system and complicate vehicle relocation decisions. To consider the effect of the uncertainty, this study proposes a two-stage stochastic nonlinear programming model, integrating long-term and short-term decisions and maximizing the profit of the carsharing companies. Specifically, in the first stage, tactical decisions of fleet sizing and initial vehicle distribution are determined before the realization of the uncertain demand. Operational decisions of both operator-based and user-based relocation are optimized in the second stage. Moreover, this paper first studies the user-based relocation incentives, which affect the distribution of uncertain demand with an endogenous relationship. A learning-embedded optimization method is introduced to learn such a distribution, enabling the decision-making optimization model to achieve higher performance under the guidance of the demand uncertainty. Second, we envision an equitable relocation issue that considers an uneven distribution of the unsatisfied demand with two different equity criteria measured from the aspects of stations and OD pairs, respectively. Third, the large problem scale, the nonlinear objective function and constraints, and the endogenous demand uncertainty constitute the nontrivial challenges to the feasible solution. For solving the problem efficiently, we linearize the nonlinear terms and develop a dedicated two-phase solution algorithm with a learning-embedded trust-region method in phase I to solve the continuous relaxation problem and a mixed-integer linear programming guided iterative rounding in phase II to obtain the integer solutions of carsharing operations. The solution algorithm adaptively bridges the learning and optimization process via the trust-region method with flexible sample generation. Finally, we conduct numerical experiments based on a real-world one-way carsharing system in Beijing to demonstrate the effectiveness and applicability of the proposed method and reveal some insights for the carsharing service. • Integrate long- and short-term decisions to maximize the profit for carsharing companies. • Consider that the distribution of uncertain demand depends on user-based relocation. • Introduce a learning-embedded optimization method to learn the demand distribution. • The optimization model can achieve higher performance under the guidance of learning. [ABSTRACT FROM AUTHOR]

• A physics-informed data-driven model is presented for fuel consumption and battery degradation. • The commuting-oriented hybrid powertrain optimization with multiple objectives is carried out. • The commuting scenario is synthesized by a method combining Markov chain and neural network. • A novel hybrid powertrain with multiple drive modes is modeled based on vehicle bench test data. • The optimized solution can improve the vehicle performance under the commuting driving cycle. The physics-informed data-driven approach is instructive in powertrain modeling with high-fidelity, which can be adopted for powertrain optimization to further improve vehicle performances. In this paper, a plug-in hybrid electric vehicle (PHEV) equipped with a novel powertrain is considered as the research object, and a series of vehicle bench experiments are carried out for operative data acquisition. With the collected data, the physics-informed data-driven models for engine fuel consumption and battery state of health (SOH) loss are proposed. The accuracy and superiority of the proposed model are demonstrated compared with other approaches. In order to realize the commuting-oriented powertrain optimization, the driving cycle representing the commuting scenario is reconstructed based on the collected traffic data in Beijing. The synthesized driving cycle generated by the proposed method can describe the practical application scenario in Beijing within the allowable error range. In addition, the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to further optimize the parameters for the hybrid powertrain according to vehicle performances. The multi-objective optimization is completed with the engine fuel consumption and battery state of health (SOH) loss as targets. The results indicate that the optimized configuration parameters can reduce fuel consumption by 4.85% and battery capacity loss by 3.7% compared with the original vehicle. [ABSTRACT FROM AUTHOR]

In addition to its important economic value, carbon trading is also an important tool in international politics and diplomacy. Carbon price forecast accuracy has far-reaching implications for economic development and the environment. Few existing studies have been more accurate in predicting carbon prices over longer periods. In this paper, the incremental Gaussian nonlinear trend fuzzy granulation method and the Gaussian nonlinear trend fuzzy granulation method are innovatively proposed to predict carbon prices. This study first converts carbon prices into incremental granulation time series. A time-varying core line is added to produce nonlinear trend granulation on the foundation of linear trend granulation. The nonlinear trend and residual information are then predicted using the DeeAR network, respectively, and the final prediction result is obtained by adding the predicted values for each. Moreover, a new evaluation index, the comprehensive evaluation of RMSE, MAE, and MAPE as three indicators, is proposed to consider the accuracy of the evaluation index more comprehensively and reliably. The results show that the prediction method has the smallest error in long-term prediction compared with other models. The daily closing price datasets of carbon exchanges published in Shenzhen and Beijing are used to validate the efficacy of this methodology. • A modified Gaussian nonlinear trend that can better fit the actual data is proposed. • Incremental Gaussian nonlinear trend fuzzy granulation method with F test is proposed. • A new evaluation index is proposed to make the prediction result more interpretable. [ABSTRACT FROM AUTHOR]