Search

The inconsistency between the whitening differential equation and the grey basic form of the non-homogeneous continuous grey model CFNGM(1,1,k,c) will result in internal errors. Thus this paper proposes a CFNGMA(1,1,k,c) model with adjustable parameters, which improves the accuracy of the CFNGM(1,1,k,c). This paper first elucidates reasons for the internal errors generated by the continuous grey model CFNGM(1,1,k,c), and explains the classic method, the discrete grey forecasting model, of eliminating internal errors. On the basis of an in-depth analysis of the modeling mechanism of CFNGM(1,1,k,c) model, a new parameter adjustable grey forecasting model is proposed by introducing parameter adjustment factors to modify model's parameters. Finally, the new model is applied to explore the gross regional product of Chengdu and Deyang in the Chengdu metropolitan area. The calculation results indicate that the newly proposed model can obtain more accurate results. [ABSTRACT FROM AUTHOR]

This paper utilizes an improved undesirable output DEA model to measure the eco-efficiency of cities in five major urban agglomerations in China during 2006–2020. It employs the Theil Index and Geodetector to investigate the spatial-temporal distribution differentiation characteristics and driving factors of urban eco-efficiency. The main findings are as follows. Firstly, the eco-efficiency of all urban agglomerations showed a fluctuating upward trend, but the eco-efficiency performance of different urban agglomerations in China shows a stratification characteristic. Specifically, the Pearl River Delta urban agglomeration consistently ranks first in China, while the mean values of the Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, and Chengdu-Chongqing urban agglomeration are lower than the national average. Secondly, the overall differences in the urban eco-efficiency of all sample cities show a consistently fluctuating downward trend. The factor that affects the level differences of eco-efficiency in different cities is the intra-regional differences. Last but not least, the top three factors affecting the spatial distribution difference of urban eco-efficiency in the whole sample are environmental pollution control investments, innovation level, and environmental infrastructure investments. In the end, this paper proposes that reducing the intra-regional differences is the primary task to achieve the coordinated improvement of urban eco-efficiency in urban agglomerations, and then puts forward some policy suggestions to improve eco-efficiency for the five major urban agglomerations. [ABSTRACT FROM AUTHOR]

The recommended bearing capacity of medium weathering mudstone foundation is less than the capacity of the rock structure to withstand loads in Southwest China. A comprehensive failure characterization of medium weathering mudstone in Chengdu has been performed including bearing plate test (BPT), binocular vision measurement (BVM) test, uniaxial compressive strength test, trial trench test of shallow rock surface and 3D imaging in this paper. Failure behavior of rock has been modeled with 3D imaging algorithm that utilizes Zhang's calibration method in BVM system combination with trial trench test of shallow rock surface. The bearing capacity of medium weathering mudstone foundation were extracted from uniaxial experiments and BPT-BVM test by fitting relevant material properties to the data. The results revealed that: Bearing capacity of medium weathering mudstone of layered isotropic in Chengdu is undervalued. Specifically, the characteristic load carrying value is in the range 1500–2500 kP, that is 50% higher than in the local standard system. Failure process is different from Hoek–Brown Failure Criterion, presenting a wave peak transfer phenomenon of the increment displacement into the distance. Thus, it can be reduced to that of punching failures for thin bedded structures of Moudstone foundations. Compressive strength of soft rock proves to be main factor limiting the bearing capacity, a clear correlation between the uniaxial compressive strength reduction coefficient and the bearing capacity has been used to establish, leading to the proposal of a load bearing capacity prediction model. [ABSTRACT FROM AUTHOR]

We investigate a deep foundation pit for urban substation construction in Chengdu, China. The pit was excavated in a sandy gravel layer and has a depth of 24.6 m. Detailed field surveys and monitoring were carried out to investigate the deformation characteristics of the foundation pit and its supporting structure related to excavation. Flac3D software was used to analyze the influence of pile anchor design parameters, including pile diameters, pile spacing, anchor cable prestress, anchor angle, and the number of rows of anchor cables, on deformation of the deep foundation pit. The results showed the pile diameter was the most critical influencing factor for pile deformation. The influences of anchor cable prestress and anchor angle on the horizontal displacement of the pile were inferior to the influence of the pile diameter and the number of anchor cable rows. The results reported in this paper provide guidance for the optimal design of foundation pits in settings with similar geological conditions. [ABSTRACT FROM AUTHOR]

Scientific and technological innovation and the sustainable development of new urbanization are the key factors to promote national economic growth, and have a very important role in promoting regional development and international competitiveness. Based on the panel data of 16 cities in Chengdu-Chongqing dual-city economic circle from 2015 to 2022, this paper uses entropy weight method to determine the weight of each index and constructs a coupling coordination model to explore the sustainable development of scientific and technological innovation and new urbanization, the Panel Vector Autoregression (PVAR) model was used to further verify the interaction between the two. The empirical results show that the coupling coordination degree of scientific and technological innovation and new urbanization in Chengdu-Chongqing economic circle is increasing year by year. There is a balanced relationship between scientific and technological innovation and new urbanization, and the two are two-way Granger causality. Therefore, the conclusion puts forward the practical enlightenment of increasing investment in scientific and technological innovation, guiding enterprises to carry out scientific and technological innovation to feedback new urbanization and accelerate the construction of new urbanization, and actively integrating with urban development. [ABSTRACT FROM AUTHOR]

With the advent of the era of Industry 4.0, big data and the Internet of Things came into being. The rise of big data and the Internet of Things has had an impact on all walks of life. The development of big data and the Internet of Things provides an opportunity for the evolution of street form. With the rapid development of smart cities, the street shape is also changing more and more towards the direction of intelligence. Taking Chunxi Road in Chengdu as an example, this paper studies the generation mechanism of street morphology. The factors influencing the generation of street form are divided into two categories: (1) legal factors. (2) Architectural design factors. By building a model, the article quantitatively analyzes the impact of these two types of factors on street form. At the same time, the article conducts an in-depth study on the evolution of ancient Chinese street morphology. The ancient Chinese street form is divided into four stages, namely the Tang and Song Dynasties, the Yuan, Ming and Qing Dynasties, the Late Qing Dynasty, and the founding of New China. The influencing factors of street shape are divided into four categories, namely, the influence of commodity economy, the influence of foreign culture, the influence of building materials, and the influence of traffic. This paper elaborates the influence mechanism of these four factors on the street form in China. The article finally puts forward the development trend of my country's future street form, and points out that the evolution of my country's street form will develop in the direction of intelligence, and intelligent transportation, intelligent garbage classification system, etc. will be applied. The number of brick-and-mortar stores in my country's street layout may decrease. [ABSTRACT FROM AUTHOR]

As high-speed rail (HSR) and air transportation developed rapidly in the last decade, their coopetition and interactional relationship has reshaped China's urban networks. Based on HSR and flight schedule data from 2009 to 2019, this paper constructs a weighted network to compare China's urban networks and their evolution, and employs machine learning to investigate the potential determinants. The results indicated that both networks tend toward polarization in the overall distribution and that the structure of urban networks under HSR networks is more hierarchical. That of HSR networks gradually forms a corridor structure along the trunk lines, while that of airline networks mainly shows a diamond spatial structure with Beijing, Shanghai, Guangzhou, and Chengdu as the cores. As for the evolution of urban networks, geographical factors, per capita GDP, and the tourism function of cities have more important impacts on that of HSR networks, while the network's topological structure and education resources have a greater impact on that of airline networks. Some socioeconomic attributes, such as urban administrative level, population, and the proportion of tertiary industry, have similar and limited influences on the two networks. • The paper constructed a comparative analysis on the evolution of China's urban networks during the last decade. • Employing machine learning to investigate the potential determinants. • The study examined the distinct attributes of the evolution and structure of high-speed rail and airline networks. • The study contrasts the determinants influencing the evolution of high-speed rail and airline networks at the city level. [ABSTRACT FROM AUTHOR]

[Display omitted] • Latrine odor release followed "rapid increase, continuous decrease, stable" pattern. • Temperature changes mainly drove the variation of fecal microbes and odor emission. • The composited microbial deodorant significantly suppressed odor emission. • Abundance of odor-producing bacteria were obviously decreased after deodorization. As an important source of malodor, the odor gases emitted from public toilet significantly interfered the air quality of living surroundings, resulting in environmental problem which received little attention before. Thus, this paper explored the odor release pattern of latrine feces and deodorization effect with composited microbial agent in Chengdu, China. The odor release rules were investigated in sealed installations with a working volume of 9 L for 20 days. The odor units (OU), ammonia (NH 3), hydrogen sulfide (H 2 S) and total volatile organic compounds (TVOC) were selected to assess the release of malodorous gases under different temperature and humidity, while the highest malodor release was observed under 45℃, with OU and TVOC concentration was 643.91 ± 2.49 and 7767.33 ± 33.50 mg/m3, respectively. Microbes with deodorization ability were screened and mixed into an agent, which composited of Bacillus amyloliquefaciens , Lactobacillus plantarum , Enterococcus faecalis and Pichia fermentans. The addition of microbial deodorant could significantly suppress the release of malodor gas during a 20-day trial, and the removal efficiency of NH 3 , H 2 S, TVOC and OU was 81.50 %, 38.31 %, 64.38 %, and 76.86 %, respectively. The analysis of microbial community structure showed that temperature was the main environmental factor driving the microbial variations in latrine feces, while Firmicutes, Actinobacteria, Proteobacteria and Bacteroidetes were the main bacteria phyla involved in the formation and emission of malodorous gases. However, after adding the deodorant, the abundance of Bacteroidetes, Proteobacteria and Actinobacteria were decreased, while the abundance of Firmicutes was increased. Furthermore, P. fermentans successfully colonized in fecal substrates and became the dominant fungus after deodorization. These results expanded the understanding of the odor release from latrine feces, and the composited microbial deodorant provided a valuable basis to the management of odor pollution. [ABSTRACT FROM AUTHOR]

In the Chengdu-Chongqing economic circle in China, the recommended bearing capacity of a medium-weathering mudstone foundation is less than the capacity of the rock structure to withstand loads. The aim of this paper was to perform plate loading tests (PLTs) with binocular vision measurement (BVM) to obtain the accurate deformation of the foundation under vertical loading. The results revealed that vertical load–bearing foundations have a failure mode that can be reduced to that of thrust failures for thin bedded structures of mudstone foundations. By analyzing the differences in results between the PLTs and the uniaxial compressive strength tests under the same conditions, a new formalism for calculating foundation-bearing capacity was proposed; there is only a 5% difference compared to in situ tests. The results of this research provide theoretical support and a practical benchmark for the full development of the load-bearing capability of soft red rocks. As the Chengdu-Chongqing economic circle progresses toward the international urban cluster, inevitably, soft rock is used as a foundation retaining layer in the construction of superhigh and super-rise buildings in the region. However, owing to antiquated testing methods and poor intelligibility of the testing equipment, the bearing capacity of such a mass of rock has yet to be fully developed and cannot meet the dual-carbon economic goals and sustainable development of architectural design. Based on this, the study introduces a noncontact method to measure the bearing capacity of soft rock within the traditional method of in situ testing and combines the concept of big data analysis to advance an empirical method for calculating the bearing capacity accurately. Based on the results of this study, the concept of natural foundations can be adopted and the engineering dilemma that the treatment of building foundations cannot be economical and practical can be solved. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy, the impact of light pollution on human life has become increasingly serious. For a long time, people have only paid attention to the positive effects of light, while ignoring the harmful consequences of light pollution. In order to evaluate the degree of light pollution in the region, this research paper has presented the multilevel evaluation model of light pollution (MELP) model. First, the MELP model selected 14 generally applicable indicators, and these indicators are easy to obtain. Second, the MELP model considered the correlation between indicators and utilizes analytic hierarchy process-entropy weight method (AHP-EWM) to determine the weight of indicators. Third, the normal cumulative distribution-Monte Carlo method was used to simulate regional light pollution data. Finally, based on the TOPSIS scoring results, the severity of light pollution was categorized into three levels: slight pollution (I–III), moderate pollution (IV–VI), and severe pollution (VII–IX). A study case is Chengdu City, Southwest China. According to the results of the MELP model, the light pollution level of Chengdu is VIII, which belongs to severe pollution. This study aimed to investigate and reduce the impact of light pollution on humans and the environment, and it can also provide a reference for similar studies around the world. [ABSTRACT FROM AUTHOR]

Online ride-splitting has rapidly evolved worldwide, providing new means to enhance the efficiency of urban travel and reduce the number of vehicles and traffic congestion. While ride-splitting is currently believed to improve vehicle occupancy rates and decrease total travel distances, passengers may perceive it as resulting in additional travel due to ride sharing. To gain a more intuitive understanding of the current operations of ride-splitting, this study delves into carpooling behavior and analyzes its relative impact using GPS trajectory data from DiDi Chuxing in Chengdu, China. The investigation focuses on the behavior of ride-splitting and examines its relative impact based on disparities in total vehicle miles traveled (VMT) and travel duration between ride-splitting traveling (RST) and corresponding separated traveling (ST), aiming to estimate the effect of RST on urban mobility. Following are the three main findings of this study: (a) The existing operation of RST induces 7%–10% extra VMT and 20%–35% extra duration for RST users; (b) The RST achieves 30%–35% VMT and 10%–20% duration saving for drivers who are willing to provide RST service; (c) The RST saves up to 10% VMT and duration varying by time in a day. This paper offers some insights to quantify the current impact of RST and evaluate the orientation of mobility service quality improvement. • Introducing a novel approach to analyzing ride-splitting behaviors. • By leveraging rich GPS data, the research provides a comprehensive analysis of ride-splitting behaviors. • Identifying key limitations, including low ride-splitting rates, prompting future research directions to address this issue. • Providing valuable suggestions and emphasizing the integration of psychological perception benefits. [ABSTRACT FROM AUTHOR]

The high-quality development of service industry has become an important engine for promoting sustainable economic development. This paper first constructed the evaluation index system of high-quality development of service industry, based on panel data from 2005 to 2020. Second, Kernel density, Markov chain and Dagum Gini coefficient were used to represent the regional differences and dynamic evolution of service industry, and the Koo method was used to explore the characteristics of spatial agglomeration. Finally, social network analysis was used to identify core indicators. The study found that: (1) From 2005 to 2020, the overall level of service industry first decreases and then increases, with Chengdu and Chongqing leading other cities. (2) The development of service industry in the CCEC has large spatial differences, mainly due to inter-regional differences. (3) The level of spatial agglomeration is less variable, with high agglomeration mainly in Chengdu. (4) Indicators such as the level of human capital are the core factors of its high-quality development. This study is of great theoretical and practical significance for the optimization and upgrading of service industry in the CCEC and the synergetic development of the region. [ABSTRACT FROM AUTHOR]

With the popularity of shared bicycles in urban areas, more and more residents choose this fast and convenient mode of transportation for short-distance travel. By optimizing the layout of shared bicycle parking areas and delivery quantity, the investment cost of shared bicycle enterprises can be effectively reduced, and the convenience of residents' travel can be improved at the same time. In this paper, we develop a collaborative optimization model for the layout of the shared bicycle parking area and delivery quantity, aiming at minimizing the walking distance of residents and the investment cost of enterprises, while considering the constraints of the parking area's attractive range and the number of bicycles placed. Aiming at the characteristics of this mixed integer nonlinear problem, an improved genetic algorithm incorporating symmetric individual precision control mechanism is designed. Finally, taking the planned area between the Second Ring Road and the Third Ring Road in the northern part of Jin-niu District, Chengdu as the background, the proposed collaborative optimization model for the layout of shared bicycle parking areas and delivery quantity is applied to a real scene. The results show that after optimization, the number of parking areas is reduced by 2, and the total investment cost is reduced by about 12.2%. [ABSTRACT FROM AUTHOR]

The Chengdu Plain serves as the central hub for economic development in Sichuan Province. The study of its surface deformation plays a pivotal role in disaster prevention and ensuring the safety of lives and property. Based on SBAS-Interferometric Synthetic Aperture Radar technology, this paper derived the land surface deformation in the Chengdu Plain from January 2020 to October 2022, and analyzed it in relation to urban development and other natural environmental factors. Results are as follows: (1) The overall surface deformation in the study area remains stable, with a noticeable trend of westward uplift and eastward decline. Significant uplift is identified in Dujiangyan and Pengzhou, located near the Longmen Mountain, with a maximum uplift rate of +61 mm/year. Conversely, subsidence is evident from Qingbaijiang District in Chengdu to Deyang, with subsidence rates reaching a maximum of −87.8 mm/year. In the areas of Dayi and Qionglai, subsidence is relatively minor. The surface subsidence phenomenon in the study area is primarily associated with urban development. (2) The mean grey correlation coefficient between the time-series cumulative surface deformation and precipitation is 0.71, which suggests a strong relationship between the surface deformation and precipitation in the Chengdu Plain. However, a time delay is observed between the changes in surface deformation and precipitation. (3) The Longmen Mountain tectonic belt extrudes and lifts the premountain plain, playing a crucial role in shaping its topography. Additionally, the concealed fracture zone of Xinjin-Chengdu-Deyang may have contributed to surface subsidence. (4) After eliminating the effect of human factors, the cultivated land and grass land cover types exhibit a subsided state, whilst the artificial surface deformation is relatively stable, and the forest land exhibits an uplifted state. Surface deformation on different land cover types is correlated with water permeability, but it is also influenced by other factors. [ABSTRACT FROM AUTHOR]

Probabilistic seismic hazard analysis is an important scientific basis for building seismic fortification. Unlike most seismic hazard analyses starting from the perspective of magnitude, this paper presents a seismic hazard analysis method based on the monthly maximum peak ground acceleration (PGA) distribution. In this paper, cities of Chongqing, Chengdu, and Kunming in southwest China are taken as examples where historical earthquake events from 1920 to 2020 with surface wave magnitude (Ms) greater than or equal to 3 and epicentral distances within 400 km are selected for analysis. PGA data are back-calculated from Ms data recorded in the earthquake catalog using the attenuation relation of seismic parameters. By considering the monthly maximum PGA values of the three cities as statistical samples, regression analysis is performed to obtain the monthly maximum PGA distribution and the frequency-PGA relation respectively. Based on the Poisson model and the parameters obtained from the regression analysis, then seismic hazard analysis is conducted to derive year exceedance probability vs. PGA curves and recurrence interval vs. PGA curves. Finally, earthquake events with the epicentral distance within 200 km, 300 km, and 400 km are respectively analyzed to discuss the influence of the epicentral distance on results of seismic hazard analysis. [ABSTRACT FROM AUTHOR]

Logistical risks are now significantly more acute due to epidemics, economic recession and local political turmoil. Cross-border logistics risks are particularly prevalent given the large number of participants and the long transport distances involved. This paper develops a system dynamics model to analyse the key factors of cross-border logistics risk. In previous studies, people usually consider the risk factors as many independent individuals. But actually, there is a causal relationship as well as conduction effects between risk factors. Therefore, this paper analyses the relationship between risk factors through qualitative analysis and establishes a system dynamics model to indicate the direction of risk transmission. The rate of risk transmission was analysed using quantitative methods. Finally, this paper conducts simulations using survey data of cross-border e-commerce enterprises in Chengdu, indicating that the industry currently has more prominent customs risks, process risks, and cooperation risks. For these more serious risks, this paper proposes corresponding prevention and control measures. [ABSTRACT FROM AUTHOR]

Using a parsimonious model, this paper analyzes a dockless bike-sharing (DLB) service that competes with walking and a generic motorized mode. The DLB operator chooses a fleet size and a fare schedule that dictate the level of service (LOS) as measured by the access time or the walking time taken to reach the nearest bike location. The market equilibrium is formulated as a solution to a nonlinear equation system over which three counterfactual design problems are defined to maximize (i) profit, (ii) ridership, or (iii) social welfare. The model is calibrated with empirical data collected in Chengdu, China, and all three counterfactual designs are tested against the status quo. We show the LOS of a DLB system is subject to rapidly diminishing returns to the investment on the fleet. Thus, under the monopoly setting considered herein, the current fleet cap set by Chengdu can be cut by up to three quarters even when the DLB operator aims to maximize ridership. This indicates the city's fleet cap decision might have been misguided by the prevailing conditions of a competitive yet highly inefficient market. For a regulator seeking to influence the DLB operator for social good, the choice of policy instruments depends on the operator's objective. When the operator focuses on profit, limiting fare is much more effective than limiting fleet size. If, instead, it aims to grow market share, then setting a limit on fleet size becomes a dominant strategy. We also show, both analytically and numerically, that the ability to achieve a stable LOS with a low rebalancing frequency is critical to profitability. A lower rebalancing frequency always rewards users with cheaper fares and better LOS even for a profit-maximizing operator. Funding: This research was partially supported by the U.S. National Science Foundation [Grant CMMI 1922665] and the National Natural Science Foundation of China [Grant 71971044]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0304. [ABSTRACT FROM AUTHOR]

With the deepening of urbanization in China, the coordinated development of cities in different regions is an important part of the sustainable development of the country, and the reasonable quantification of the unbalanced development of cities in different regions is an important issue facing the society nowadays. Previous studies usually use population data to analyze the power-law distribution law to quantify the imbalance of urban development in different regions, but China's population data span a large number of years and numerous division criteria, and the results obtained from different population data are widely disparate and have obvious limitations. The paper starts from a fractal perspective and utilizes OpenStreetMap (OSM) data to extract national road intersections from 2015 to 2022, calculates critical distance thresholds for eight years using urban expansion curves, generates urban agglomerations in China, and quantifies the imbalance of urban development in different regions by calculating the urban agglomeration power-law index. The results indicate that (1) the critical distance threshold of urban expansion curves exhibits a slight overall increase and stabilizes within the range of 120–130 m, (2) the number of urban agglomerations in China has been increasing significantly year by year, but the power-law index has been decreasing from 1.49 in 2015 to 1.36 in 2022, and (3) the number of urban agglomerations and the power–law index of the Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing regions, which is consistent with the national scale trend, indicates that the scale distribution of urban agglomerations in China at this stage does not conform to Zipf's law, and there is a certain Matthew effect among cities in different geographic areas with a large unevenness. The results of the study can provide new ideas for assessing the coordinated development of cities in different regions. It compensates for the instability of population and economic data in traditional studies. [ABSTRACT FROM AUTHOR]

Detection of subsurface hydrodynamic anomalies plays a significant role in groundwater resource management and environmental monitoring. In this paper, based on data from the groundwater level, atmospheric pressure, and precipitation in the Chengdu area of China, a method for detecting outliers considering the factors affecting groundwater levels is proposed. By analyzing the factors affecting groundwater levels in the monitoring site and eliminating them, simplified groundwater data is obtained. Applying sl-Pauta (self-learning-based Pauta), iForest (Isolated Forest), OCSVM (One-Class SVM), and KNN to synthetic data with known outliers, testing and evaluating the effectiveness of 4 technologies. Finally, the four methods are applied to the detection of outliers in simplified groundwater levels. The results show that in the detection of outliers in synthesized data, the OCSVM method has the best detection performance, with a precision rate of 88.89%, a recall rate of 91.43%, an F1 score of 90.14%, and an AUC value of 95.66%. In the detection of outliers in simplified groundwater levels, a qualitative analysis of the displacement data within the field of view indicates that the outlier detection performance of iForest and OCSVM is better than that of KNN. The proposed method for considering the factors affecting groundwater levels can improve the efficiency and accuracy of detecting outliers in groundwater level data. [ABSTRACT FROM AUTHOR]

The development of the automatic fare collection (AFC) systems provides significant support for predicting passenger flow on urban rail transit. This paper extracts passenger travel patterns using AFC data on urban rail transit in Chengdu, China, over a one-month period. Passengers are divided into two categories based on their travel habits and data mining models, and multinomial logit (MNL) models are separately used to predict their destinations. Furthermore, a two-way search algorithm is developed to search the optimal paths between origin-destination (OD) pairs by considering interchange constraints. Start a path search through the origin point and destination point, respectively, until the shortest path is found. The maximum effectiveness of a path is measured by travel time, interchange time, and the number of interchanges between the OD pairs. Finally, the validity of the proposed passenger flow path prediction method is verified by using the AFC data of Chengdu metropolitan rail transit from April 2018. [ABSTRACT FROM AUTHOR]

In daily operation, urban rail transit (URT) systems often experience disturbances that result in a partial reduction in transport capacity [partial disturbances (PDs)] rather than disturbances leading to a complete reduction in transport capacity. However, research that assesses the resilience of URT networks under PDs remains limited. This paper addresses this gap by proposing a passenger-oriented resilience assessment model for URT networks under PDs, considering the travel behaviors of passengers and different relations (i.e., linear, concave, and convex) between the velocity coefficient and failure severity. A simulation-based assessment approach was developed to solve the resilience assessment model. A numerical experiment was conducted on the Chengdu subway network in China. The results demonstrate that the performance indicator employed herein reflects the impact of passenger travel time distribution on network performance. Deliberate PDs cause more significant performance losses than random PDs. Moreover, the network is the least resilient under PDs considering the convex relation between the velocity coefficient and failure severity. The resilience-based critical link of each line is not fixed and varies with the failure severity and disturbance occurrence time. Increasing the failure severity of PDs results in more severe performance losses than increasing the number of PDs. Additionally, the passenger-oriented resilience of a URT network can be enhanced by improving the passengers’ tolerable delay time and disturbance duration. [ABSTRACT FROM AUTHOR]

Early work on China's Two-Child Policy identified cost as a major disincentive to having a second child, but financial constraints may work in tandem with cultural factors to shape childbearing desire. In addition, most research has excluded men from the sample. This paper develops Yan's (2003) concepts of 'new' and 'traditional' fertility cultures to childbearing desire, a key component of fertility decision-making that is poorly understood (Hayford & Agadjanian, 2017). In particular, we focus on how differing conceptions of children's needs relate to perceptions of cost. This paper reports on semi-structured interviews with 29 men and women in Chengdu, the capital of Sichuan province. We found that interviewees who espoused views consistent with a traditional fertility framework were more interested in having a second child, and were less concerned about cost. In addition, men were more likely to espouse views consistent with a traditional fertility cultural framework. [ABSTRACT FROM AUTHOR]

Several possible lithosphere–atmosphere–ionosphere coupling mechanisms before earthquake occurrence are presented in the literature. They are described by several models with different interaction channels (e.g., electromagnetic, mechanics, chemical, thermal), sometimes in conflict with each other. In this paper, we search for anomalies six months before the Lushan (China) 2013 earthquake in the three geo-layers looking for a possible view of the couplings and testing if one or another is more reliable to describe the observations. The Lushan earthquake occurred in China's Sichuan province on 20 April 2013, with a magnitude of Mw = 6.7. Despite the moderate magnitude of the event, it caused concern because its source was localized on the southwest side of the same fault that produced the catastrophic Wenchuan event in 2008. This paper applies a geophysical multi-layer approach to search for possible pre-earthquake anomalies in the lithosphere, atmosphere, and ionosphere. In detail, six main increases in the accumulated seismic stress were depicted. Anomalous geomagnetic pulsations were recorded in the Chengdu observatory, sometimes following the increased stress. Atmosphere status and composition were found to be anomalous in several periods before the earthquake, and, spatially, the anomalies seem to appear firstly far from the upcoming earthquakes and later approaching the Longmenshan fault where the Lushan earthquakes nucleated. The Formosat-3 data identified interesting anomalies in the altitude or electron content of the ionospheric F2 peak in correspondence with seismic and atmospheric anomalies 130 days before the earthquake. In addition, the total electron content showed high anomalous values from 12 to 6 days before the earthquake. We compared the anomalies and tried to explain their correspondences in different geo-layers by the lithosphere–atmosphere–ionosphere coupling models. In particular, we identified three possible couplings with different mechanisms: a first, about 130 days before the earthquake, with a fast (order of one day) propagation delay; a second, about 40 days before the earthquake occurrence, with a propagation delay of few days and a third from 2.5 weeks until one week before the event. Such evidence suggests that the geo-layers could interact with different channels (pure electromagnetic or a chain of physical-chemical processes) with specific propagation delays. Such results support the understanding of the preparation for medium and large earthquakes globally, which is necessary (although not sufficient) knowledge in order to mitigate their impact on human life. [ABSTRACT FROM AUTHOR]

For the location-routing problem of medical waste treatment centers, this paper proposed a multi-objective optimization model considering the public risk. Firstly, from the perspective of the government, the public and the processing center contractor, this paper established the mathematical model which simultaneously considered the operation cost, public risk and transportation cost.Secondly, according to the characteristics of the constructed model, this paper designed an improved multi-objective salp swarm algorithm to solve the problem. Finally, taking the planning projects of the medical waste treatment center in Chengdu, Sichuan province as an example, this paper verified the feasibility of the model and the effectiveness of the algorithm through the method of comparative analysis. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is an important and challenging task in building smart city. Accurate forecasting requires consideration of spatio-temporal characteristics consisting of multiple influences such as holiday, similar node, and weather. In order to accurately capture the spatio-temporal characteristics of road network intersections, this paper proposed a prediction model based on graph convolutional neural network, temporal algorithm Prophet and Pearson correlation coefficient to achieve accurate prediction of intersection traffic considering spatial structure, similar nodes, holidays and other influencing factors. Firstly, it introduced pearson correlation coefficient to reduce the influence of similar nodes to improve the temporal algorithm for capturing temporal features. Secondly, it used the graph convolution neural network to capture spatial features; Finally, it determined the fusion weights of the graph convolutional network and the temporal algorithm by linear regression to obtain the results of spatio-temporal fusion prediction. This paper finally extracted the intersection traffic data based on the analysis of Chengdu taxi trajectory data and conducted traffic prediction experiments. The results show that the accuracy of the model proposed in this paper is better than that of most existing baseline methods, compared with the T-GCN, ASTGCN, and AGCRN models, the MAE is reduced by 1.623, 0.724, 0.161, respectively, and the accuracy is improved by 0. 144, 0. 068, and 0. 021, respectively, which verifies the effectiveness of the model in traffic intersection flow prediction. [ABSTRACT FROM AUTHOR]

[Objective] The paper aimed to conduct quantitative calculation and localization research on ecosystem services in Chengdu, to understand the strength, spatial differentiation and impact mechanism of ecosystem services, and to provide policy ideas for the evaluation and supply of ecosystem services in rapidly urbanizing regions. [Method] Based on the gradient space theory, a spatial gradient ring and a spatial gradient transect in Chengdu were established to evaluate the value of ecosystem services, spatial differences and coordination were studied by the evaluation model of ecosystem service value, and coordination degree model in 1990, 2000, 2010 and 2020. [Result] (i) During the study period, the value of ecosystem services in Chengdu decreased by 4.737 billion yuan. (ii) The overall ecosystem service value in Chengdu showed a degrading trend, and the ‘center-periphery’ spatial circle structure radiating from the center to the periphery was obvious, mainly expanding in the southeast (ES) direction, and the northwest (WN) direction was the most stable. (iii) Chengdu has an obvious trend of expanding from the center to the two wings in the north-south direction (S) In the east-west direction (H）, the ecosystem service value gradually declined in the central urban area, rapidly declined in some areas adjacent to the city, and the ecological function areas in the outer suburbs were relatively stable. (iv) From 1990 to 2020, the comprehensive coordination degree (P) on the gradient spatial transect in Chengdu decreased by 2.43. [Conclusion] (i)The value of ecosystem services and comprehensive coordination degree in Chengdu have obvious spatial and temporal differences and significant influence from the expansion of construction land. (ii）The spatial change direction and location of ecosystem services are consistent with the characteristics of urban expansion. (iii) The overall trend of comprehensive coordination (P) is downward. The construction of spatial ecosystem services has an increasingly significant impact on comprehensive coordination (P). [ABSTRACT FROM AUTHOR]

Under the background that electric vehicles cause enormous supply pressure on the grid, the application of vehicle-to-grid (V2G) technology can effectively alleviate supply pressure and also bring more economic benefits. Considering conditions for participating in V2G, pure electric logistics fleets have the advantages of stable work end time and large remaining power. In this paper, the economic benefits of the pure electric logistics fleet participating in V2G and the influence factors of the V2G profit are studied. Firstly, the travel data of a pure electric logistics fleet in Chengdu, China is collected. Secondly, the V2G profit model is established to quantify the economic benefits. Then, 4 V2G scenario modes are proposed based on window duration, window start time, SOC lower limit and discharge price. Finally, the change law and influence factors of the V2G profit in each V2G scenario mode are studied from the perspective of the discharge revenue, charge cost and battery loss cost. The results show that the V2G profit increases with the extension of window duration, the delay of window start time, the decrease of SOC lower limit and the increase of discharge price. The average profit of logistics fleets ranges from 3.94 RMB/(vehicle·day) to 65.54 RMB/(vehicle·day). [ABSTRACT FROM AUTHOR]

Water justice can be a delicate balancing act. Conservation regulations ensure urban areas' water demands are met, but these may conflict with the water rights of local people living in catchment areas. No metropolis wants to face water shortages, yet prioritising municipalities' rights over those of local people can result in water injustice. This paper explores water rights and water justice, defining both to show how the latter is achieved by balancing the former. It examines Chengdu's water catchment area using field observation, interviews, and document analysis and investigates the causes of water conflict through the lens of local culture. The study then outlines a water justice framework involving domestic habits and lifestyle, production of water, and management principles with the aim of providing parity of rights that highlight local cultural factors. The justice criteria outlined here could also be used in other places where power structures disregard local people's water rights. [ABSTRACT FROM AUTHOR]

In recent years, the tourism industry has developed rapidly. However, traditional tourism information has the disadvantages of slow response speed and limited information content, which cannot reflect the evolution trend of spatial and temporal patterns of tourism information in time. Here, based on the Baidu Index, we construct an evaluation framework to analyse the spatial and temporal flow of tourism information in the Chengdu–Chongqing urban cluster from 2011 to 2021. Then, we analyse the urban links between different network levels from the evolution pattern. Finally, we use the geodetector model to analyse its driving mechanism. The results show that Chengdu and Chongqing are the most active cities in the study area in terms of tourism information. The unbalanced development of tourism information between Chengdu and Chongqing and other cities in the region gradually deepens during the period 2011–2019 (polarization effect), but the unbalanced development moderates after 2019. On the other hand, cities in the middle of the Chengdu–Chongqing cluster always have weak agglomeration effects of tourism information. Cities with high tourism information outflow rates in the Chengdu–Chongqing city cluster are mainly concentrated around Chengdu. The average outflow rate of Deyang is the highest, at 27.8%. Cities with low tourist information outflow rates are primarily located in the west, central and south. Ya'an is the city with the lowest outflow rate, with an average of −62.2%. Specifically, Chengdu is the dominant and most radiantly influential city. The tourism information of the Chengdu–Chongqing urban cluster shows a radial network with Chengdu and Chongqing as the core. The driving force analysis shows that the push factor of tourist source, such as the number of people buying pension insurance, is the core driving mechanism, while the pull factor of destination, such as the park green area, and resistance factors such as psychological distance, are in the secondary position. In general, this paper uses Internet tourism data to expand the traditional tourism information research of the Chengdu–Chongqing urban cluster, which can better respond to the changes and needs of the tourism market and provide reference for the spatial optimization of tourism destinations. [ABSTRACT FROM AUTHOR]

The development and functional perfection of urban areas have led to increasingly severe fire risks in recent decades. Previous urban fire risk assessment methods relied on subjective judgment, rough data collection, simple linear statistical methods, etc. These drawbacks can lead to low robustness of evaluation and inadequate generalization ability. To resolve these problems, this paper selects the indicator and regression models based on the high-resolution data of the spatial distribution characteristics of Longquanyi distinct in Chengdu, China. and proposes an integrated machine learning algorithm for fire risk assessment. Firstly, the kernel density analysis is used to map the fourteen urban characteristics related to fire risks. The contributions of these indicators (characteristics) to fire risk and its corresponding index are determined by Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost). Then, the spatial correlation of fire risks is determined through Moran's I, and the spatial distribution pattern of indicator weights is clarified through the raster coefficient space analysis. Finally, with these selected indicators, we test the regression performance with a backpropagation neural network (BPNN) algorithm and a geographically weighted regression (GWR) model. The results indicate that numerical variables are more suitable than dummy variables for estimating micro-scale fire risks. The main factors with a high contribution are all numerical variables, including roads, gas pipelines, GDP, hazardous chemical enterprises, petrol and charging stations, cultural heritage protection units, assembly occupancies, and high-rise buildings. The machine learning algorithm integrating RF and BPNN shows the best performance (R2 = 0.97), followed by the RF-GWR integrated algorithm (R2 = 0.87). Compared with previous methods, this algorithm reduces the subjectivity of the traditional assessment models and shows the ability to automatically obtain the key indicators of urban fire risks. Hence, this new approach provides us with a more robust tool for assessing the future fire safety level in urban areas. [ABSTRACT FROM AUTHOR]

Smart cities can effectively improve urban life quality. But, with the rise in population size, there is an increase in the use of vehicles for transportation. In smart city development, traffic control and route planning are the primary tasks to address the problem of travel time estimation. It has become complex due to concerns of wrapped spatio-temporal data on dynamic real-time traffic conditions. The existing methods failed to estimate travel time efficiently due to not considering the spatio-temporal features and lack of computing resources. There is a need for a system like intelligent transportation system to monitor and accurately predict traffic flow to avoid traffic congestion and reduce the impact on the ecological system. This paper developed a novel hybrid deep learning model to estimate optimized travel time and possible trajectories. In this work, U-Net used to reduce the number of feature points for each temporal data and GNN is used to build the graph with connectivity between vehicular nodes. This hybrid model helps us predict traffic flow and aims to estimate accurate travel time from one location (node) to another. The model's performance is evaluated on the standard benchmark datasets Q-Traffic, TaxiBJ, and Chengdu. The experimental results show that the proposed framework significantly improves performance in extracting travel patterns and provides an optimal route with an estimated travel time than existing methods with RMSE 4%, MAE 20.49%, and MAPE 18%. The proposed model has accurately predicted the estimation of travel time of the vehicle for the given urban traffic data. [ABSTRACT FROM AUTHOR]

Detection of subsurface hydrodynamic anomalies plays a significant role in groundwater resource management and environmental monitoring. In this paper, based on data from the groundwater level, atmospheric pressure, and precipitation in the Chengdu area of China, a method for detecting outliers considering the factors affecting groundwater levels is proposed. By analyzing the factors affecting groundwater levels in the monitoring site and eliminating them, simplified groundwater data is obtained. Applying sl-Pauta (self-learning-based Pauta), iForest (Isolated Forest), OCSVM (One-Class SVM), and KNN to synthetic data with known outliers, testing and evaluating the effectiveness of 4 technologies. Finally, the four methods are applied to the detection of outliers in simplified groundwater levels. The results show that in the detection of outliers in synthesized data, the OCSVM method has the best detection performance, with a precision rate of 88.89%, a recall rate of 91.43%, an F1 score of 90.14%, and an AUC value of 95.66%. In the detection of outliers in simplified groundwater levels, a qualitative analysis of the displacement data within the field of view indicates that the outlier detection performance of iForest and OCSVM is better than that of KNN. The proposed method for considering the factors affecting groundwater levels can improve the efficiency and accuracy of detecting outliers in groundwater level data. [ABSTRACT FROM AUTHOR]

Using the CRITIC method, comprehensive index evaluation method, VAR model, coupling coordination model and other methods, this paper evaluates the comprehensive development of the population economy and eco-geological environment composite system in the twin-city economic circle of Chengdu–Chongqing region from 2000 to 2020, verifies the dynamic coupling relationship between subsystems and measures the coupling coordination degree of complex system. Meanwhile, the differences in the development process of each subsystem are discussed and the spatial–temporal evolution characteristics and laws of coupling coordination of the composite system during the research period are analyzed. The results reveal the following: (1) There is a long-term coordinated relationship between population, economy and eco-geological environment in Chengdu–Chongqing region, which have the conditions for emergence and generation. (2) The subsystems of population, economy and eco-geological environment in Chengdu–Chongqing region show an overall upward trend, among which the Sichuan part obviously outperforms the Chongqing part. Besides, the growth rate of the economy subsystem is significantly higher than that of the population, eco-geological environment subsystem. (3) The coupling coordinated development of the composite system has shown a benign upward development trend, gradually changing from "composite spiral structure" to "two cores outstanding, peripheral collapse, the west superior to the east", while the main coordination state has developed from "Basic synergy—Economic lag" in 2000 to "Basic synergy—Population lag" in 2022. In addition, Cheng-De-Mian and Yuzhong districts with the better level of economic development have shown "Advanced synergy—Ecological lag". [ABSTRACT FROM AUTHOR]

One of the most important archaeological finds excavated from the Tianhui Laoguanshan Han tombs in 2012–13 was a manuscript on horse medicine, dated around the third century BCE. Prior to the discovery of this highly specialized veterinary text, only a handful of horse recipes from the Qin and Han periods had been found, and a sixth-century CE agricultural treatise, Essential Techniques for the Common People , was generally regarded as the earliest surviving source of extensive veterinary material, including various medical treatments for horses. Although the Laoguanshan manuscript – given the modern title Book of Treating Horses by the Team for Collating the Medical Bamboo Slips Excavated from the Han Tombs in Tianhui Town, Chengdu – has suffered significant damage, it nevertheless gives us an insight into the knowledge and treatments for horses during the Qin and early Han periods. A variety of ways of treating horses are recorded in Treating Horses , including herbal remedies, piercing, cauterization, hot packs, bandages, massage, and bathing. The use of gold needles is also mentioned in this text, echoing the gold and silver sewing needles excavated from Liu Sheng's (d. 113 BCE) tomb in Mancheng, Hebei Province. This paper offers a short introduction to this valuable text on horse medicine by examining the content of its fragments, including names of ailments, symptoms of certain diseases, etiologies, and treatment methods. The discovery of Treating Horses challenges the established view that horse treatment methods in ancient China were predominantly herbal and that techniques of bleeding and cauterization recorded in Essential Techniques were brought to China from elsewhere, together with the introduction of Buddhism. The paper argues that the "foreign influence" had already occurred at a much earlier date, in the form of interactions with nomadic tribes such as the Scythians, the horsemen par excellence of classical antiquity. [ABSTRACT FROM AUTHOR]

The name Bian Que, like that of the Yellow Emperor (Huangdi), has reverberated through the development of Chinese medicine since the time of the Warring States. The discovery of a human figurine showing channels and strategic points, together with a number of medical texts, during the excavation of the Laoguanshan Han tomb in Chengdu, Sichuan, in 2012–13 has reignited controversies about whether it is correct to speak of a specific Bian Que school, or whether, as this paper argues, these texts were written by the Han physicians who used Bian Que as a mouthpiece to record their own medical expositions. The paper begins by examining the main characteristics of the Laoguanshan human figurine and discusses what this excavated artifact reveals about the early history of Chinese medicine. It questions the existence of the so-called Bian Que school and, obliquely, the suggested relationships between the school and the figurine and between the school and medical texts found in the same tomb. The paper shows how diverse the disjointed knowledge of medicine was and that the idea of a "school" does not accurately reflect what was happening in the transmission of medical knowledge during the Warring States, Qin, and Han periods (475 BCE–220 CE). [ABSTRACT FROM AUTHOR]

The study takes 10 urban agglomerations in China as the research object, focusing on the Chengdu-Chongqing urban agglomeration, and applies Data Envelopment Analysis (DEA) to measure and compare their scientific input and output efficiency of universities. First, this paper analyzes the input and output of scientific research in universities in major provinces in China in detail. Second, according to the construction principles of the indicator system, using qualitative interview to construct evaluation indicators of university research efficiency. Third, using DEA method, first analyze the input and output profile of some urban agglomeration universities such as Chengdu-Chongqing economic circle, measure and compare their research input and output efficiency, then compare and analyze the research efficiency of research-type sample universities within Chengdu-Chongqing economic circle, and conduct a projection study of non-DEA effective sample universities. The main conclusions are as follows: first, the average efficiency of scientific research in universities in Chengdu-Chongqing and other urban agglomerations in 2020 has slightly increased compared with that in 2016, but the gap between urban agglomerations is prominent, and the innovation level of scientific research in higher education institutions in urban agglomerations needs to be improved. Second, there is a mismatch between the themes of research, funding and human resources in research-oriented universities in the Chengdu-Chongqing economic circle. Third, there is considerable room for improvement in research efficiency, and the influence of scale on overall efficiency is weak. We found that excessive investment in scientific research in universities is the main reason for the non-effect. [ABSTRACT FROM AUTHOR]

• A non-linear programming model to design full-day demand-adapted timetables is proposed. • The time-dependent maximum sectional passenger flow is used as input data. • A new algorithm is proposed to match demand and services' supply. • The algorithm uses a rolling-horizon mechanism to iteratively compute departure times. • The effectiveness of the proposed algorithm is illustrated using real data of Chengdu Metro line 1. This paper focuses on the problem of designing the schedule of urban rail transit systems under dynamic passenger demand considering limited train capacity and congestion. From the point of view of operators, the goal of timetable optimization consists of using as few as possible service trains to transport the arriving passengers to their destinations securely and quickly. On the other hand, passengers want to spend the minimum time travelling to their destinations, including the time they wait on platforms until they board the first and successive trains in case of transferring between lines. Due to the variability of passenger flows and the difficulty in exactly solving full-day timetabling problems in short computation times, it is difficult in practice to use the approaches proposed in the literature. Moreover, when the capacity of trains is considered and oversaturation conditions emerge, the problem becomes even more complex, converting into a challenge the design of schedules from an operational point of view. By considering fixed train dwell times at stations and train running times between stations, we first propose a non-linear programming formulation to model the problem of determining the most convenient interdeparture times of services at the first station of a line, aiming at minimizing the waiting time of passengers. Since the dynamic behaviour of passenger demand and the maximum sectional passenger flow are approximately synchronous, an innovative method, the spatiotemporal synchronization coupling algorithm, based on working with the time-varying maximum sectional passenger flow instead of the demand patterns is proposed to quickly solve in an approximate way the demand-driven timetabling problem. The proposed approach is applied to a real case of Chengdu Metro Line 1 in China. The results show the effectiveness of the algorithm, both in terms of computational effort and in terms of reducing the waiting time currently experienced by passengers. [ABSTRACT FROM AUTHOR]

Economic development and fast growing urbanization in China have caused severe air pollution, with frequent pollution episodes endangering the health of inhabitants and disturbing social activities, and as an expanding metropolis, Chengdu has suffered ever since. The concentration variations of main air pollutants, such as PM10, PM2.5 and NO2, often show periodicity because of meteorological impact and anthropic activities, and display orientation discrepancies due to influences of wind speed (WS), frequency and pollutant sources. These features have complicated the mechanisms of pollution episodes and deepened the difficulty in pollution control evaluation. The WS has significant influences on the periodicity and orientation variations in pollutant concentrations, and quantifying the influence of which is of high significance and provides sustainable foundations for pollution alleviation strategies. Different time-scale cycles (i.e., Diurnal, weekly, seasonal and annual), along with the WS, wind frequency, wind and spatial orientations in urban areas, were analyzed in this paper. Results show that the periodicity of diurnal, seasonal and annual cycles is remarkable, and weekly cycle is obvious by adding the influence of the WS in 16 orientations. The WS has direct impacts on pollutants varying in the range of 1.5–2.5 m/s, and has a remarkable diffusion effect on pollutants once above 2.5 m/s. Over heavy pollution hours in diurnal, weekly, annual cycles and transitional seasons, the WS had more significant influences on pollutants, and whereas the wind frequency is not the main impact factor for orientation variations. For Chengdu, the northeast orientation is suitable to construct a wind panel with a remarkable diffusion effect on pollutants, while air pollutions in the northwest and southwest orientations were severe with the WS below 1.5 m/s, and pollution diffusion in the north-northwest orientation was the worst. This work can provide guidance and reference for urban planning optimization and air environment protection in cities with air quality control considerations impacted by city wind. [ABSTRACT FROM AUTHOR]

The Local Climate Zone (LCZ) classification scheme is a vital method of building a category dataset for high-resolution urban land. For the development of urban meteorology, air pollution and related disciplines, the high-resolution classification data of urban buildings are very important. This study aims to create LCZ datasets with detailed architectural characteristics for major cities and urban agglomerations in China, and obtain more accurate results. We constructed 120 m resolution land use datasets for 63 cities (mainly provincial capitals, municipalities directly under the Central Government, important prefecture-level cities and special administrative regions) and 4 urban agglomerations in China based on the local climate zone (LCZ) classification scheme using the World Urban Database and Access Portal Tools method (WUDAPT). Nearly 100,000 samples were used, of which 76,000 training samples were used to provide spectral signatures and 23,000 validation samples were used to ensure accuracy assessments. Compared with similar studies, the LCZ datasets in this paper were generally of good quality, with an overall accuracy of 71–93% (mean 82%), an accuracy for built classifications of 57–83% (mean 72%), and an accuracy for natural classifications of 70–99% (mean 90%). In addition, 35% of 63 Chinese cities have construction areas of more than 5%, and the plateaus northwest of Chengdu and Chongqing are covered with snow all year round. Therefore, based on the original LCZ classification system, the construction area (LZC H) and the snow cover (LCZ I) were newly added as the basic classifications of urban LCZ classification in China. Detailed architectural features of cities and urban agglomerations in China are provided by the LCZ datasets in this study. It can be applied to fine numerical models of the meteorological and atmospheric environment and improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a network coupling mechanism is studied to couple the air sector network, airport network, and air route network into a multilayer air network model. Then, the altitude layers are divided into three: high, medium, and low, and the arrival and departure flight procedures are also considered. By defining the association between the altitude layers and the waypoints, a multilayer air network model considering the altitude layer is constructed. Then, the line graph theory is used to redefine the nodes and edges, and the network is reconstructed to obtain a new single-layer one which is easy to calculate. Finally, a case study is carried out in Chengdu control area. The results show that the proposed model is closer to the reality, and the invulnerability performance is more referential. Besides, it also reflects the impact of altitude layers on the efficiency of airspace. The results are helpful for air traffic controllers to manage airspace better and have significance for promoting air traffic safety and stability. [ABSTRACT FROM AUTHOR]

The classical multi-period inventory routing problem (MIRP) combines inventory management and vehicle routing problems, attempting to maintain customer inventory levels via autonomous deliveries, with the assumption that stock-out never occurs at the supply side. While this is no more the truth when a common third-party logistics company operates a central warehouse for a group of small and medium sized retailing companies. Procurements have to be made to refill inventory at the central warehouse. This study thus extends the traditional MIRP by incorporating procurement decisions (MIRP-PD) and tries to minimize the total cost of procurement, inventory holding and transportation. To our knowledge, this paper is among the first to formally describe MIRP-PD and model it as a mixed integer linear program. A hybrid two-level heuristic is proposed to address large-scale instances. The upper level of the algorithm determines the visiting schedule while the lower level works out procurement and routing decisions accordingly. Components from tabu search and adaptive threshold acceptance are also embedded to help escape from local optima. Extensive numerical instances are generated from a real case in Chengdu (China), based on which the computational results reveal the effectiveness and efficiency of the proposed algorithm compared with the commercial solver CPLEX. The comparison between MIRP and MIRP-PD has demonstrated the cost advantage of incorporating procurement decisions into inventory and routing plans. Impacts on costs of key factors including delivery frequency and buffer size of stores have also been examined to provide managerial implications to practitioners. [ABSTRACT FROM AUTHOR]

Shield tunneling method is widely used in urban metro construction. The construction stability is closely related to the engineering geological conditions. Sandy pebble strata have a loose structure and low cohesion, resulting in great engineering-induced stratigraphic disturbance. Meanwhile, the high water-abundance and strong permeability are extremely detrimental to construction safety. It is of great significance to evaluate the dangerousness of shield tunneling in water-rich pebble strata with large particle size. In this paper, risk assessment of engineering practice is carried through with Chengdu metro project in China as a case study. Referring to the special engineering situations and assessment workload, seven evaluation indices, including compressive strength of pebble layer, boulder volume content, permeability coefficient, groundwater depth, grouting pressure, tunneling speed and tunnel buried depth are selected to establish an evaluation system. A complete risk assessment framework is established based on the cloud model, AHP and entropy weight method. Further, the measured surface settlement is taken as the risk degree characterization to verify the results. This study can provide reference for method selection and evaluation system establishment in the risk assessment of shield tunnel construction in water-rich sandy pebble strata, and contribute to proposing safety management in similar engineering projects. [ABSTRACT FROM AUTHOR]

Travel time forecasting has become a core component of smart transportation systems, which assists both travelers and traffic organizers with route planning, travel schedule adjustments, ride-sharing, navigation applications, and efficient traffic management. However, timely and accurate travel time forecasting still remains a critical challenge owing to the complex nonlinear and dynamic fluctuations of spatial–temporal dependencies. Also, spatial sparseness is a big issue in traffic forecasting, since adopting the implicit interactions between the close traffic regions leads to superficial characterization of spatio-temporal dependences. In this paper, we propose a new deep learning-based framework (STTG-TTE) that addresses these drawbacks and improves the travel time estimation. First, we build a geo-hashing algorithm for the data sparsity issue that incorporates fluctuations of nearby and distant traffic situations in terms of spatio-temporal dependencies. Second, a new spatio-temporal correlation modeling method is proposed to fully leverage large-scale spatial and temporal traffic patterns using temporal convolutional networks integrated with a gated multi-modality mechanism. Then, for external factors' representation, a new dual-gated Res-Net multi-modality-based module is proposed. Finally, we fuse these representations of multi-components dynamically and utilize the transformer model, which is conducive to learning intersections among these multiple factors for obtaining accurate prediction results. Experiments on two large-scale real-world traffic datasets from two different urban regions (Chengdu taxi-datsets and NYC-Bike datasets) demonstrate that the proposed model is superior to state-of-the-art baseline models. [ABSTRACT FROM AUTHOR]

Increases in the area of impervious surfaces have occurred with urbanization. Such surfaces are an important indicator of urban expansion and the natural environment. The automatic extraction of impervious surface data can provide useful information for urban and regional management and planning and can contribute to the realization of the United Nations Sustainable Development Goal 11—Sustainable Cities and Communities. This paper uses Google Earth Engine (GEE) high-resolution remote sensing images and OpenStreetMap (OSM) data for Chengdu, a typical city in China, to establish an impervious surface dataset for deep learning. To improve the extraction accuracy, the Small Attention Hybrid Unet (SAH-Unet) model is proposed. It is based on the Unet architecture but with attention modules and a multi-scale feature fusion mechanism. Finally, depthwise-separable convolutions are used to reduce the number of model parameters. The results show that, compared with other classical semantic segmentation networks, the SAH-Unet network has superior precision and accuracy. The final scores on the test set were as follows: Accuracy = 0.9159, MIOU = 0.8467, F-score = 0.9117, Recall = 0.9199, Precision = 0.9042. This study provides support for urban sustainable development by improving the extraction of impervious surface information from remote sensing images. [ABSTRACT FROM AUTHOR]

The popularity of ride-hailing platforms has significantly improved travel efficiency by providing convenient and personalized transportation services. Designing an effective ride-hailing service generally needs to address two tasks: order matching that assigns orders to available vehicles and proactive vehicle repositioning that deploys idle vehicles to potentially high-demand regions. Recent studies have intensively utilized deep reinforcement learning to solve the two tasks by learning an optimal dispatching strategy. However, most of them generate actions for the two tasks independently, neglecting the interactions between the two tasks and the communications among multiple drivers. To this end, this paper provides an approach based on multi-agent deep reinforcement learning where the two tasks are modeled as a unified Markov decision process, and the colossal state space and competition among drivers are addressed. Additionally, a modifiable agent-specific state representation is proposed to facilitate knowledge transferring and improve computing efficiency. We evaluate our approach on a public taxi order dataset collected in Chengdu, China, where a variable number of simulated vehicles are tested. Experimental results show that our approach outperforms seven existing baselines, reducing passenger rejection rate, driver idle time and improving total driver income. [ABSTRACT FROM AUTHOR]

Natural environmental factors and human activity intensity factors, the two main factors that affect the spatial and temporal distribution of PM2.5 concentration near the surface, have different mechanisms of action on PM2.5 concentration. In this paper, a GTWR-XGBoost two-stage sequential hybrid model is proposed aiming at detecting the expression of spatiotemporal heterogeneity in the traditional machine learning retrieval model of PM2.5 concentration and the difficulty of expressing the complex nonlinear relationship in the statistical regression model. In the first stage, the natural environmental factors are used to predict PM2.5 concentration with spatiotemporal characteristics by collinearity diagnosis method and Geographically and Temporally Weighted Regression method (GTWR). In the second stage, the simulation results in the first stage and the natural factors eliminated through LUR stepwise regression in the first stage are into the XGBoost model together with the human activity intensity factors in the buffer zone with the best correlation coefficient of PM2.5, and finally the temporal and spatial distribution of PM2.5 concentration. Taking the Chengdu Chongqing Economic Circle as an example, the proposed model is used to retrieve PM2.5 concentration and compared with the single GTWR, XGBoost, and coupling model published recently. The experimental results show that the R2, RMSE, and MAE of the GTWR-XGBoost two-stage model cross-validation are 0.92, 5.44 ug·m−3, and 4.12 ug·m−3, respectively. Compared with the above single models, R2 increased by 0.01 and 0.12, and MAE decreased by more than 0.11 and 3.1, respectively. Compared with the coupling model published recently, R2 is increased by 0.02, and MAE is reduced by more than 0.4. In addition, the PM2.5 concentration in Chengdu Chongqing showed obvious seasonal temporal and spatial changes, and the influence ratios of natural environmental factors and human activity intensity activities factors on PM2.5 were 0.66 and 0.34. The results show that the GTWR-XGBoost two-stage Model can not only describe the heterogeneity and objectively reflect the complex nonlinear relationship between the phenomenon and the influencing factors, but also enhance the interpretability of the phenomenon when simulating the spatiotemporal distribution characteristics of PM2.5 concentration. [ABSTRACT FROM AUTHOR]

Architectural heritage is an important cultural resource in the city, and its macroscopic spatial pattern and accessibility are important for the conservation of architectural heritage and the development of heritage tourism. This paper explores the spatial pattern and accessibility of architectural heritage in Chengdu by using spatial analysis of 308 declared national, provincial, and municipal cultural relic protection units in Chengdu as the object of architectural heritage research. The results showed that: (1) The distribution of architectural heritage was generally clustered, and the clustering areas of various types of architectural heritage differ significantly, with architectural heritage mainly concentrated in the central urban areas; (2) The integration value of various road networks connecting architectural heritage in the central urban area were significantly better than those in other areas; (3) The transportation accessibility index of architectural heritage in the central urban areas was higher, while the transportation accessibility index of architectural heritage in the surrounding areas was lower. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that e-shopping has considerable effects on e-shoppers' travel behavior. Therefore, it is valuable to investigate the built environment effects on online shopping, which can help clarify whether land use policy is effective to manage online shopping and further moderate travel demand. However, this issue has not been fully investigated in prior research. In particular, some existing studies fail to identify a significant link between the built environment and online shopping. One of the possible reasons is that the indirect effects of the built environment on e-shopping through e-shopping attitudes are rarely considered. Against this backdrop, considering the mediating role of e-shopping attitudes, this paper aims to explore the influence of the built environment on the frequency of e-shopping for clothes and shoes, food and drinks, cosmetics, and electronics. Data used in this study are acquired from 675 face-to-face interviews with online buyers in Chengdu, China, and the Structural Equation Modeling method is employed. The outcomes show that higher residential density has a positive impact on online shopping frequency. Higher accessibility to metro stations has an indirect and negative influence on e-shopping frequency through pro-e-shopping attitudes. In contrast, mediated by e-shopping attitudes, higher accessibility to bus stations has an indirect and positive impact on online shopping frequency. The mediating role of attitudes provides a possible explanation for the influences of transportation accessibility on e-shopping frequency. Land use policies seem influential in online shopping attitudes and frequency, and thus moderate e-shoppers' travel demand. [ABSTRACT FROM AUTHOR]

The text discussed in this paper is part of the collection of bamboo slip medical manuscripts excavated from the Laoguanshan Han tomb outside of Chengdu, Sichuan Province, China, in 2012. We have given it the title Piercing Methods based on its contents. This text provides invaluable new material for understanding the historical development of Chinese medicine and acupuncture over the course of the Han dynasty (202 BCE–220 CE). We combine analyses of excavated manuscripts, received texts, and excavated artifacts to reconstruct the piercing methods from the early Han presented in this text. Three primary sets of techniques and associated piercing tools have been identified: mai piercing, division piercing, and piercing water (swelling), although only the first technique that concerns treating the mai is dealt with comprehensively in this article. [ABSTRACT FROM AUTHOR]

A variety of Chinese textual operational text data has been recorded during the operation and maintenance of the high-speed railway catenary system. Such defect text records can facilitate defect detection and defect severity analysis if mined efficiently and accurately. Therefore, in this context, this paper focuses on a specific problem in defect text mining, which is to efficiently extract defect-relevant information from catenary defect text records and automatically identify catenary defect severity. The specific task is transformed into a machine learning problem for defect text classification. First, we summarize the characteristics of catenary defect texts and construct a text dataset. Second, we use BERT to learn defect texts and generate word embedding vectors with contextual features, fed into the classification model. Third, we developed a deep text categorization network (DTCN) to distinguish the catenary defect level, considering the contextualized semantic features. Finally, the effectiveness of our proposed method (BERT-DTCN) is validated using a catenary defect textual dataset collected from 2016 to 2018 in the China Railway Administration in Chengdu, Lanzhou, and Hengshui. Moreover, BERT-DTCN outperforms several competitive methods in terms of accuracy, precision, recall, and F 1 -score value. [ABSTRACT FROM AUTHOR]