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The "regional advantage" hypothesizes that inference uncertainty/prediction error of geotechnical or geological properties at a target site (a site contains a group of records measured from different locations/depths using a variety of tests) can be smaller if we use a quasi-regional cluster (includes two or more database sites with geotechnical or geological properties similar to the target site) instead of the entire database. A tailored clustering enabled regionalization (TCER) framework has been proposed to verify this "regional advantage" hypothesis. TCER requires the target site should not be an outlier site relative to the database. However, it remains a challenge on how to detect an outlier site (or data group) from a database. In this paper, we modify the original TCER by introducing a novel outlier site detection step called maximum site similarity (MSS) into the original TCER. The capability of MSS is verified using synthetic and real examples. Additionally, three inference methods [e.g., probabilistic multiple regression (PMR), classical Bayesian model (CBM), and hierarchical Bayesian model (HBM)] are studied for the purpose of determining the optimal inference method for the modified TCER in terms of achieving the minimum inference uncertainty/prediction error with reasonable computational time. It is shown that the modified TCER with CBM outperforms other inference methods for the examples shown in this paper. • An efficient method that addresses a non-traditional class of outlier site detection problems is proposed. • The proposed outlier detection method is added to the original TCER framework for screening appropriate database. • A Shenzhen clay database named SZ-CLAY/7/3426 is compiled to validate the modified TCER framework. • Three inference methods are applied in the modified TCER framework and their inference uncertainties are compared. [ABSTRACT FROM AUTHOR]

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

Grouting lifting is the effective approach for treating differential settlement of existing tunnels, but it easily deteriorates the tunnel structure again and seriously threatens the operational safety of urban rail transit. Aiming to solve the above problem, this paper considers the interaction between the tunnel and the foundation, and establishes a series of analytical solutions of the stress, strain, axial force, and bending moment of the tunnel under single point grouting. Based on the characteristics of shield tunnel correction engineering and the analytical solution under single point grouting, the equation of the internal force and deformation of tunnel structure under multi-point grouting considering different grouting angles and different strata is obtained. The grouting correction engineering case for Shenzhen Metro Line 1 shows that the analytical solution to the displacement of the tunnel structure is well matched with the measured displacement of the tunnel structure is well matched with the measured data of the case and the difference is less than 15 %. The parameter analysis results indicate that the main loaded area of the tunnel under single point grouting is related to the stratum, and the main loaded area of the soft soil stratum is - 42. 4 ° + 42. 4 ° around the grouting point. Convergence deformation of the tunnel under multi-point grouting reaches the maximum value at the diameter passing through the center of the grouting zone and the minimum value at the diameter which is perpendicular to the former. Under the condition of limiting horizontal convergence deformation, the reasonable grouting angle range of the multi-point grouting zone is about 120 ° 150 °. This paper provides a theoretical basis that can immediately evaluate the mechanical structure performance and to determine whether to impose greater pressure when the tunnel is continuously lifted. [ABSTRACT FROM AUTHOR]

Vector cellular automata (VCA) are effective models for cadastral-scale land use change modeling, leveraging fine spatial granularity information from cadastral plot data. The temporal dimension has the potential to improve the performance of VCA further. However, it is challenging to precisely capture long sequence information of cadastral plot temporal data for VCA while ensuring accurate capture of fine granularity information simultaneously. Our paper introduces the Temporal-VCA framework, which fully utilizes fine spatial and temporal granularity information of cadastral plot temporal data to enhance the accuracy of VCA. Applying Shenzhen's annual cadastral plot data from 2009 to 2014, this study shows how deep learning techniques can elucidate the temporal aspects of VCA models. Temporal-VCA notably improves precision by up to 22.12 %, outperforming the regular VCA models and traditional raster CA models. It reveals the complex nonlinear temporal patterns within cadastral-scale urban development processes. Designed simulations for 2030, including scenarios of disordered development and ecological protection, highlight the benefits of fully leveraging fine temporal granularity information of temporal data into urban planning, potentially reducing ecological damage by 70 %. Our findings offer a novel methodology for urban land use simulation, with significant implications for urban planning and the advancement of sustainable cities. • Temporal-VCA enhances cadastral land use modelling by improving VCA's long sequence data acquisition • Our model simultaneously utilizes the fine spatial granularity and time series data of cadastral data • Policy scenarios are designed to optimize future land resources use by evaluating policy impacts. [ABSTRACT FROM AUTHOR]

• A dual-camera computer vision monitoring system is proposed and applied in displacement monitoring of an underground prefabricated subway station for 5 months during construction and post-construction phases. • Field calibration experiment is carried out using movable targets equipped with spiral micrometers and the measurement errors are discussed. • The potential correlation between uneven displacement and some minor structural defects, as well as the temperature-induced deformation fluctuations are investigated. The increasing applications and structural vulnerability of prefabricated metro stations have resulted in a growing demand for monitoring their displacements during the construction and post-construction phases. This paper proposes a dual-camera computer vision monitoring system consisting of a dual-camera base station, a series of monitoring prisms and two reference prisms. The displacement and rotation of the camera station are considered to reduce measurement errors caused by camera motion. The system has been applied in the displacement monitoring of the 164 m assembly section of an underground station in Shenzhen during the construction and post-construction periods. A movable target equipped with a spiral micrometer is used for field calibration experiments, and the results show that the average measurement deviation is 0.01 mm and the standard deviation is 0.068 mm. During the 5-month monitoring, displacement caused by backfilling, rainfall, and other construction conditions are precisely reflected, which shows a potential correlation between uneven displacement and some minor structural defects. At the post-construction period, settlement variance is relatively small, and the temperature effect causes horizontal convergence to fluctuate. [ABSTRACT FROM AUTHOR]

In recent years, promoting regional synergistic development has become the shared objective in city-regions during the new stages of development. This paper aims to uncover the dynamics of synergistic development in urban agglomerations by constructing an index measurement system based on four dimensions: industrial synergy, innovation synergy, infrastructure synergy, and institutional synergy. By comprehensively employing theories and methods such as "space of flow" big data analysis, network analysis, and interlocking network models, this study examines patterns and capacities of synergistic development in the Guangdong-Hong Kong-Macao Greater Bay Area. The main instances of industrial cooperation between cities are observed in Hong Kong-Shenzhen and Guangzhou-Shenzhen; capital flows primarily concentrate in Guangzhou-Shenzhen and Hong Kong-Shenzhen. Research paper and patent collaborations between cities are predominantly concentrated in Hong Kong-Shenzhen; joint laboratories are mainly established in Hong Kong-Macao and Hong Kong-Guangzhou; inter-city co-construction of higher education institutions is mainly focused on Guangzhou-Foshan and Shenzhen-Hong Kong. Infrastructure synergistic development performs best in Guangzhou-Shenzhen, Guangzhou-Foshan, and Shenzhen-Dongguan. Cross-border collaboration benefits from institutional advantages held by Hong Kong and Macao. Urban interactions with outstanding performance include Guangzhou-Shenzhen, Hong Kong-Shenzhen, Guangzhou-Foshan, Shenzhen-Dongguan. Additionally, increasing interaction trends can be observed between Guangzhou-Dongguan as well as Shenzhen-Zhuhai/Shenzen-Foshan partnerships. Understanding the typical characteristics and potential trends of synergetic development within the GBA holds significant practical value for establishing a first-class bay area along with a world-class urban agglomeration. • Concept and connotation of synergistic development in urban agglomeration area was examined. • Indicators and methods for evaluation of synergistic development in the GBA were conducted. • Patterns and capacities of synergistic development at urban scale in the GBA were explained. • Urban interaction with highest synergistic development level is Guangzhou-Shenzhen. [ABSTRACT FROM AUTHOR]

The Yangtze River Major Conservation (YRMC) Action has been strategically crafted to catalyze industrial evolution within the Yangtze River basin, aligning with China's goal of achieving carbon neutrality by 2060, particularly from the corporate perspective. Despite its significance, the potential impact of this initiative on corporate environmental performance (CEP) remains understudied. To address this gap, we utilize panel data from Chinese firms listed on the Shanghai and Shenzhen A-shares (2012–2020) and employ the difference-in-differences (DID) method to investigate the causal relationship between the YRMC Action and the CEP. The main findings are threefold: (i) Firms located within a 100 km radius of the Yangtze River's main stream have exhibited an average increase of 0.1497 units in CEP due to the implementation of the YRMC Action, which are consistently validated across robustness tests; (ii) Mechanisms driving this enhancement include incentivizing firms to undertake green technological R&D programs, securing governmental green R&D subsidies, and credit incentives; and (iii) Heterogeneity analyses show that firms closer to the main stream of the Yangtze River, particularly those upstream and led by senior executives with awareness of green initiatives, have contributed to more pronounced improvements in CEP. This paper highlights policy implications for optimizing the incentive effects of the YRMC Action and strengthening the environmental regulatory governance to ensure enduring enhancements in CEP. [ABSTRACT FROM AUTHOR]

Airport buses play a crucial role in addressing the last-mile problem of air travel, especially in cities and countries lacking inner-city rail transit systems. Nevertheless, airport buses are currently witnessing a decline in ridership due to drawbacks such as long departure intervals, inflexible stops, and considerable distances between stops. Consequently, delivering high-quality airport bus services has become a pressing concern for public transport operators. Motivated by new flexible buses and ride-sharing services, this paper explores a flexible airport bus service that integrates ride-sharing services for passengers traveling from bus stops to their destinations. This problem entails integrated decisions involving bus stop selection, passenger assignment to drop-off bus stops, as well as bus and ride-sharing routing. Accordingly, this problem presents more challenges in decision-making than traditional flexible bus or ride-sharing routing problems. We first develop an arc-based mixed-integer linear programming model. Subsequently, we design a double decomposition math-heuristic algorithm that builds upon logic-based Benders decomposition and column generation algorithms to obtain a near-optimal solution within practical computation time limits for practical-scale instances. Additionally, we implement an adaptive large neighborhood search algorithm to evaluate the solution quality of this math-heuristic algorithm and to solve large-scale instances. To validate the effectiveness of both the model and the algorithms, we conduct numerical experiments using instances derived from Shenzhen airport bus lines. The experimental results demonstrate that the flexible service mode offers significant advantages in reducing both passenger ride time and vehicle mileage over traditional airport bus or taxi modes. • The flexible airport bus problem with last-mile ride-sharing services is introduced. • An arc-based mixed-integer programming model is proposed. • A logic-based Benders decomposition math-heuristic algorithm is designed. • An adaptive large neighborhood search algorithm is developed. • Numerical experiments using instances derived from Shenzhen airport are conducted. [ABSTRACT FROM AUTHOR]

In this paper, a CdTe photovoltaic double-skin ventilated window integrated with vacuum glazing (VPV-DSV) is proposed. The mathematical model of VPV-DSV was developed and validated through tests in Hefei, China. Annual energy performance and the economic analysis of the VPV-DSV was evaluated and compared with the other two PV-DSVs and an insulating glazed window (IG) under climate conditions of five selected cities, including Changchun, Hefei, Kunming, Shenzhen, and Shijiazhuang. Moreover, effects of window design factors, including the window-to-wall ratio, PV coverage, and pillar spacing of the VG, were explored. Results indicated that: (1) the VPV-DSV produced the best energy saving results and the least energy consumption. (2) NECs of the VPV-DSV in the five cities above was 955.05 kW•h, 464.31 kW•h, 110.66 kW•h, 53.71 kW•h, and 443.14 kW•h, respectively. (3) Due to high cost of the VG, the VPV-DSV only had the best economic performance in Shenzhen and the NPV was 3931.47 CNY. (4) The WWR and PV coverage had obvious impacts on the overall performance of the VPV-DSV, while the pillar spacing only had a more significant impact on the air conditioning energy consumption. [ABSTRACT FROM AUTHOR]

Signal control has been effective to alleviate urban traffic congestion. Massive related works about signal timing optimization have been proposed and led to many signal control methods and systems. In recent years, reinforcement learning (RL) algorithms have attracted the increasing attention of researchers in the area of signal control optimization, since they can learn the optimal timing policy themselves by analyzing changing patterns between the traffic condition and signal timing plans. Multi-intersection traffic signal control problems face more challenges than classical single intersection problems such as the dimensionality issue as the joint action space of multiple agents grows exponentially with the number of intersections. The existing state-of-art multi-agent reinforcement learning algorithms developed for other areas may not suit well with traffic signal control given the complex spatial–temporal nature of traffic flows. In this paper, we propose a novel multi-agent counterfactual actor–critic with scheduler (MACS) framework for multiple interactions. In this method, decentralized actors control the traffic signals and the centralized critic combines recurrent policies with feed-forward critics. Additionally, a scheduler module that exchanges information among agents helps the individual agent better understand the entire environment. The proposed method was evaluated using simulation experiments based on a real-world urban street network in Shenzhen, China. Results showed that the proposed method outperforms the classic model-based method and several existing RL-based methods according to different measures such as queue length, average travel time delay, and throughput. • Develop an AI-based signal control framework to collaborate multiple intersections. • The counterfactual baseline can well assign the credit among multiple intersections. • The scheduler module helps agents better understand the entire environment. [ABSTRACT FROM AUTHOR]

Understanding how municipal solid waste (MSW) classification influences the physiochemical properties and incineration emission characteristics of MSW is imperative for sustainable waste management. To investigate how long-term changes in the composition and physical properties of MSW resulting from MSW classification affect the emissions of polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) and multiple flue gas pollutants, a five-year experiment (2018–2022) was conducted in a full-scale MSW incinerator in Shenzhen City, China. After MSW classification, the food waste in MSW was substantially reduced while the paper, textile, and wood components were notably increased. MSW classification also remarkably decreased the water and chlorine contents of MSW while concomitantly increasing the wet-based calorific value, sulfur content, and nitrogen content. Consequently, the PCDD/Fs emissions declined from 106.7 ng I-TEQ/t MSW in 2018 to 26.0 ng I-TEQ/t MSW in 2022. Moreover, a process-tracing experiment demonstrated noticeable decreases in PCDD/F synthesis in the post-combustion area, with mass-reduction efficiencies of 23.6 %–95.4 %. In a correlation analysis, the PCDD/F emissions were significantly positively correlated with food waste, water content, and chlorine and negatively correlated with the wet-based calorific value and nitrogen content. Furthermore, MSW classification reduced the emissions of conventional flue gas pollutants (SO 2 , CO, and HCl) and multiple heavy metals (total sum of Sb, As, Pb, Cr, Co, Cu, Mn, and Ni). In summary, the long-term study demonstrated that MSW classification can feasibly separate incombustible waste, improve combustion quality, and reduce pollutant emissions from sources. These findings offer practical guidance for promoting MSW classification. [Display omitted] • MSW classification remarkably changes composition and physical properties of MSW. • MSW classification mitigates PCDD/F emission from 106.7 to 26.0 ng I-TEQ/t MSW. • MSW classification effectively reduces the emission of various flue gas pollutants. • The long-term experiment demonstrated the feasibility of MSW classification. [ABSTRACT FROM AUTHOR]

The utilization of Vehicle-to-Grid (V2G) technology, which facilitates the integration of electric vehicles (EVs) into the power grid, is promising for energy management systems. Among them, electric taxi (ET) is an important part of EVs. The relatively stable and predictable operating patterns of the ET fleets could have a scale effect, effectively managing load demand and supply with V2G technology. This paper investigates the potential and flexibility of V2G technology for urban power system based on real-world ET trajectory data. To evaluate the flexibility of V2G more precisely, a general framework is proposed to infer vehicle charging, energy consumption, and reconstruction of the State of Charge (SoC) for ETs. The V2G potential estimation model is established for each ET, followed by a bottom-up agent-based model to simulate flexibility changes in the system during various V2G events. The proposed methodology is tested on 19,900 ETs in Shenzhen over a month to explore the potential and flexibility of V2G technology. The results show that the V2G system could supply at least 50 MW for 1 h, 30 MW for 2 h, and 20 MW for 3 h during peak periods, while simultaneously recovering within 2–3 h, without disrupting the regular operations of ETs. The great potential of ETs to supply additional energy during peak periods and recover energy during valley periods is highlighted, which could lead to a more efficient and sustainable management of energy resources. • Real trajectory data with 19,900 ETs is used for V2G potential and resilience analysis. • A method infers charging patterns, energy use, and reconstructs State of Charge is proposed. • A general method framework for modeling ET-V2G system on supply side is proposed. • An agent-based model is introduced for ET-V2G system modeling and simulation. • ET-V2G system can supply up to 60 MW and recover within 2–3 h during peak periods. [ABSTRACT FROM AUTHOR]

Although studies have explored the travel behavior of residents in informal housing communities in recent years, few have been conducted in the context of a time series before and after the outbreak of COVID-19. This paper tracks individual-level daily mobility response for three months, using an interrupted time series (ITS) quasi-experimental design to infer daily causal effects of COVID-19 and community close-off management on individuals' daily travel distances. This study compares the mobility response to COVID-19 between people living in urban villages and commodity housing communities in Shenzhen, China. The results reveal that urban village residents reduced their travel less, with slower response and recovery rates after the COVID-19 outbreak. The community close-off management was less effective for urban village residents, with a smaller abrupt reduction in mobility and their recovery rate increasing faster during the post-lockdown period. Therefore, urban village residents may have experienced greater limitations in adjusting travel with higher exposure risks during the pandemic, compared with commodity housing residents. The variations in mobility change are significantly affected by some inherent differences between these two groups, even after controlling for their socioeconomic and built environment features. These results provide important insights for designing mobility restriction policies for different housing groups and planning more resilient neighborhoods in the post-COVID era. • Adopting an interrupted time series quasi-experimental design to infer daily causal effects of COVID-19 • Comparing mobility effects of COVID-19 in urban villages and commodity housing communities • Urban village residents reduced their travel less, with slower response and recovery rates. • The community close-off management was less effective for urban village residents. [ABSTRACT FROM AUTHOR]

Probabilistic site characterization is the cornerstone of data-centric geotechnics, which underpins the digitalization in geotechnical engineering. The essential aspect of probabilistic site characterization is to reasonably infer the probability density distributions of geotechnical parameters, based on field and laboratory data. In this paper, a clay property database, SZ-CLAY/11/5130, was established by collecting geotechnical investigation reports from 52 engineering projects in Shenzhen city. This SZ-CLAY/11/5130 database was first compared with the global clay database CLAY/10/7490 and the Shanghai clay database SH-CLAY/11/4051, in terms of their sample ranges, correlations, and scatter plots. Then, the multivariate probability distribution of eight clay parameters was established using the training records selected from SZ-CLAY/11/5130, to characterize the generic correlations between multiple clay parameters. Based on Bayesian theory, the posterior distributions of clay mechanical parameters from a construction site in Shenzhen city were updated using both the site-specific correlations and the generic correlations given by SZ-CLAY/11/5130. The constructed multivariate probability density distribution is necessary for this updating step. [ABSTRACT FROM AUTHOR]

• Establish dual-platform MDP framework for optimizing hybrid bike-sharing platforms in the duopoly market. • Consider dynamic operation strategies (i.e., pricing and rebalancing) and battery level variation of ebikes. • Adopt modified D3QN algorithm for solving the game in the real-world case. • Gain managerial insights into the introduction of ebikes. The advent of electric bikes, or ebikes, has significantly enhanced competitiveness of bike-sharing systems, providing benefits to both riders (comfort during uphill and long-distance rides), platforms (more profit), and the environment. Operating such a hybrid bike-sharing system, i.e., with both bikes and ebikes, in a competitive multi-platform market, can be challenging due to the complex and unpredictable interplay among heterogeneous market participants, which becomes more pronounced with the ebike varying battery, and dynamic demand. Most related research is predicated on the assumption of a monopoly market, which is not always the case: in worldwide capital-oriented markets, many firms will quickly imitate and join in rapidly developing fields for profits. Thus, this paper addresses platforms' hybrid bike-sharing system operation problem with time-varying ebike pricing and rebalancing strategy in consideration of competition. We consider two docked hybrid bike-sharing platforms with charging stations at the site. Platforms utilize trucks for their own rebalancing operation including bike, ebike and mixed bike/ebike relocation tasks. We combine the Markov decision process (MDP) model with game theory, and establish the dual-platform MDP framework in which one mainstream platform and one competing platform optimize their profits by dynamic pricing and bike/ebike rebalancing based on highly dynamic and stochastic demand. Users' choice is described by a modified nested logit model and the endogenous demand is generated. We develop the tailored double dueling deep Q-network for solving dynamic gaming. A series of experiments are conducted based on the real-world dataset in Shenzhen and several strategy combinations are compared. The results show the win–win situation where both platforms improve profits with a higher market ratio and demonstrate how to introduce and operate ebikes in the system by analyzing detailed strategies in different games. [ABSTRACT FROM AUTHOR]