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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]

Disruptions occurring at an urban rail transit (URT) system can severely affect its normal operations, and an effective bus bridging service (BBS) is able to help to reduce the negative effects. Transit operators usually arrange BBS to depart from the disrupted stations to evacuate the stranded passengers. However, the overload of passengers at the disrupted stations, especially at turnover and transfer stations, may incur the secondary operation disruptions such as stampede accidents. To mitigate the negative effects of disruptions and reduce the number of passengers stranded at the disrupted stations, the strategy based on the passenger guidance and extended BBS (E-BBS) is introduced in this paper. Different from the widely applied standard BBS running within the disrupted links, E-BBS runs among the normal operating stations or between the normal operating stations and the disrupted stations. A mixed integer linear programming model is developed in this paper to guide the stranded passengers and design an optimal E-BBS solution to transport them. Considering the bi-directional running trains along the disrupted links, a dynamic decision framework is developed to manage the disruptions. Given the impacts of the high uncertainty on bus running time which could affect the performance of E-BBS, a robust model is proposed to obtain more reliable travel guidance and E-BBS schemes. Numerical experiments based on Chengdu subway system in China are conducted. The results indicate that the proposed model can obtain optimal travel guidance and E-BBS solutions in a timely manner. When the uncertainty on bus running time is ignored, the total travel cost for affected passengers is reduced 14.20 % on average with the aid of the optimal travel guidance and E-BBS solutions. Moreover, the number of passengers gathering at the disrupted stations decreases by 36.80 %. The robust model can obtain more reliable travel guidance and E-BBS schemes in consideration of uncertain bus running time. The proposed model shows great potential to effectively mitigate the negative effects of disruptions and help to enhance the capability of a URT system to respond to disruptions. [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]

Tourism has become an engine for economic growth, but its rapid expansion has also triggered global ecological issues. Tourism ecological security has become a crucial constraint on urban sustainable development. In this context, "all-for-one" tourism leverages its advantage in driving regional resource integration, promoting coordinated urban-rural development, and advancing the implementation of China's rural revitalization strategy. Therefore, this paper aims to analyze the tourism ecological security of the Chengdu-Chongqing urban agglomeration from an "all-for-one" tourism perspective. It further dissects this framework into production and tourism ecological protection stages, evaluate the tourism ecological security level of this urban agglomeration from 2011 to 2020 through the dynamic super-efficiency network SBM (Slack-Based Measure) model, and explore the driving factors of the two stages by using geographical detectors. And the Dagum Gini coefficient is used to identify regional disparities. The results indicate that Chongqing has the highest tourism ecological security efficiency at 1.41, while Chengdu scores only 1.0, suggesting that Chengdu needs to prioritize ecological concerns compared to Chongqing. Furthermore, five cities have efficiency values below 1, highlighting the need for secondary cities to tap into their potential and strive for balanced development within the urban agglomeration. The paper proposes improvement frameworks tailored to each region's unique circumstances and offers relevant recommendations for governments, businesses, and the tourism industry, aiming to provide guidance for the sustainable development of urban ecology and tourism. [ABSTRACT FROM AUTHOR]

• The measurement model is reliable to unveil the spatial pattern of mixed land-use. • Public awareness is a novel and reliable indicator for correcting POI data. • The layout of mixed land-use in Chengdu is unevenly distributed in space. • There is a significant spatial correlation between mixed plots at the same level. • Optimizing the layout of mixed land can improve land use efficiency. In the context of urban quality enhancement, resilience focus, and the promotion of human-centered urbanization, a well-structured mixed land-use layout plays a pivotal role in intensifying conservation land use, enhancing land use efficiency, and improving residents' living environments. The existing research on measuring mixed land parcels is hampered by a lack of area and category data for POI point data, and some methods neglect the variations in function quantities. This paper establishes an optimal calculation model by incorporating the weighted optimization of POI data, introduces the concept of land gravity to supplement the entropy method, and computes the distribution of mixed land-use patterns. This paper analyzes both the spatial patterns of mixed land-use in urban areas and the potential laws governing the combination of internal functions. The results demonstrate that mixed-use parcels exhibit a spatial pattern resembling a "1 + n" pole-axis structure and a "T-type" development trend. Regarding the internal function combination, mixed-use parcels are generally found to include commercial and transportation functions. The paper provides suggestions and discussions on optimizing the layout and function structure of mixed land-use, offering a reference for current urban mixed land-use planning. [ABSTRACT FROM AUTHOR]

• The study area is typical, and has instructive and significant. • Compared with RSEI, the useful data of IRSEI for analysis increased by 3.26%. • We use GDP sub-industry spatialization model to represent the urbanization process. • Our study is important to know how urbanization affects the environment. As the fourth pole of China's economic growth, the Chengdu-Chongqing urban agglomeration plays a significant role in reinforcing the ecological barrier in the upper reaches of the Yangtze River, and is crucial for ecological and environmental protection strategies. In this paper, Google Earth Engine (GEE) and the MODIS images from 2000, 2005, 2010, 2015, and 2022 were utilized to construct the Improved Remote Sensing Ecological Index (IRSEI) to characterize the ecological quality more accurately than RSEI. Additionally, combined with nighttime light remote sensing data, land use data and socio-economic data, the GDP sub-industry spatialization model was utilized to analyze the urbanization process in depth. To dynamically monitor and evaluate the interaction between urbanization and ecological environment quality, a coupling coordination model incorporating the above methods was developed. The results show that (1) The effective information of the IRSEI in ecological quality analysis increased by 3.26% compared to RSEI, and the correlation with each ecological index was higher; (2) The ecological environment quality of the Chengdu-Chongqing urban agglomeration peaked in 2005 and has been declining since then, with the rate of decline gradually slowing down from 2005 to 2022; (3) The GDP sub-industry spatialization model is more suitable for characterizing the scattered villages, and can effectively exhibit the urbanization process. From 2000 to 2022, urbanization was rapidly developed, and the level of core cities such as Chengdu and Chongqing far exceeded those of neighboring cities; (4) The coupling coordination level between IRSEI and GDP spatialization model generally increased, indicating ongoing improvements in the synergy between ecological environment and urbanization in the Chengdu-Chongqing urban agglomeration. This study developed a method for quickly monitoring and assessing the relationship between ecological environment quality and urbanization by using IRSEI and GDP sub-industry spatialization model, and provides scientific analytical methods and data support for monitoring and governing the ecological environment in emerging urban agglomerations. [ABSTRACT FROM AUTHOR]

• ·The three-stage super efficiency SBM-DEA model and Malmquist index was used to measure carbon emission efficiency, index and decomposition terms of land use. • ·LUCE and its spatial and temporal pattern of urban agglomerations in the YREB were discussed from static and dynamic perspectives respectively. • ·Environmental variables such as level of economic development, urbanization rate, and industrial structure all have significant effects on input slack value. • ·The PTE of the MRYR was relatively low due to insufficient resources in low-carbon technology, while the low TE of the CC is due to its insufficient SE. • ·TC was the main factor promoting the improvement of LUCE in the whole basin, and the trend changes of LUCE in the three urban agglomerations was closely related to their management level, technological level, and adjustment of element structure. Exploring regional differences in land use carbon emission efficiency (LUCE) and the path of collaborative emission reduction in the Yangtze River Economic Belt (YREB) are of immense importance to achieve the sustainable regional development. Firstly, based on the estimation method of land use carbon emissions, this paper scientifically calculates the carbon emission of land use of the Yangtze River Delta urban agglomeration (YRD), the middle reaches of the Yangtze River urban agglomeration (MRYR), and the Chengdu-Chongqing urban agglomeration (CC) from 2010 to 2021 in the YREB. And the carbon emission of land use of each city in urban agglomerations was obtained, which was regarded as the unexpected output in the efficiency evaluation index system. Secondly, the three-stage SBM-DEA model was used to reveal the LUCE and its spatiotemporal characteristics in different regions from a static perspective. The empirical results of the second stage SFA showed that the level of economic development, urbanization rate, industrial structure, non-agricultural conversion rate of agricultural land, the level of scientific and technological innovation and government intervention had significant effects on LUCE. By comparing the efficiency values and their decompositions in the first and third stage of various urban agglomerations, it was found that after eliminating environmental and random factors, the average ranking of comprehensive technical efficiency (TE) of land use carbon emission changed from YRD > MRYR > CC to YRD > CC > MRYR due to the cross influence of pure technical efficiency (PTE) and scale efficiency (SE). Thirdly, Malmquist index model was used to analyze the change of total factor productivity index (TFPch) and its spatiotemporal pattern from a dynamic perspective. It was found that the TFPch, technological progress index (TC), pure technical efficiency index (PEC) and scale efficiency index (SEC) of land use carbon emission of the three major urban agglomerations all showed varying degrees of increase after adjustment. Finally, to further analyze the impact of technological progress, PTE and SE on LUCE, a regression model was constructed with LUCE of each city in urban agglomeration as the dependent variable, and TC, PEC and SEC as independent variables. It was concluded that technological progress was the main factor to promote the improvement of LUCE in the whole YREB. The trend of changes of LUCE in various urban agglomerations was closely related to its management level, technology level and factor structure adjustment. The above conclusions provide support for the coordinated emission reduction path of urban agglomerations in the YREB from the perspective of land use, which is conducive to actively and steadily promoting the realization of carbon peak and carbon neutrality goals. [ABSTRACT FROM AUTHOR]

the construction of high-speed railway improves the accessibility of cities along the route and plays a certain role in promoting the economic development of cities. The construction of Chengdu-Chongqing high-speed railway improves the timeliness and accessibility of Chengdu to Chongqing, and plays a certain role in promoting the development of cities along the Neijiang River.This paper mainly analyzes the influence of Chengdu-Chongqing high-speed railway on the economic development of Neijiang city after its completion, and puts forward some targeted suggestions for the development of Neijiang. [ABSTRACT FROM AUTHOR]

• Five species of human interference are coupled to produce a cumulative human interference. • The spatial distribution of the hot spots of ecosystem services is visualized after caculation of three types of ESV. • This study explores the clustering patterns of HDI and the clustering patterns of ESV, respectively. • The spatial correlation and clustering patterns between HDI and ESV are analyzed by using the Bivariate Moran's index. The spatial correlation between human disturbances and ecosystem services carries profound implications, serving as a pivotal tool for quantifying the magnitude of human impacts on ecosystems and offering invaluable insights for the development of effective management and conservation strategies. This research endeavors to conduct an exhaustive analysis of the spatial patterns between human disturbance intensity and ecosystem services in the Chengdu-Chongqing urban agglomeration, as well as to explore the spatial associations between these two factors. In the paper, we conducted a comprehensive analysis of five key indicators of human pressure, namely population density, gross domestic product (GDP), land use type, transportation network and energy consumption. These indicators were carefully examined and utilized to create a spatial map depicting the integrated human disturbance intensity index in the Chengdu-Chongqing urban agglomeration area in the year 2020. Concurrently, we utilized the ESV equivalent factor method to estimate the ecosystem service value (ESV). Bivariate Local Moran Index and spatial autocorrelation analysis were introduced to quantitatively depict and visualize the spatial relationship between human disturbance and ecosystem services. This research provides valuable insights into the intricate patterns of human pressures and ecosystem services in urban agglomeration scale. The key findings are as follows. First, the integrated human disturbance intensity is relatively high in the hub parts of Chengdu, Chongqing, and Deyang, as well as the middle zone of the cities between Chongqing and Chengdu. Second, the high value of supply services is distributed in the periphery of the cities, while the value of supply services in the middle of the cities tends to be medium and low. The majority area in the middle of the entire study area has low and medium–low values of regulation and maintenance service. At the fringes, there are only a few cities with higher values, including Ya'an, Leshan, Deyang and the eastern part of Chongqing. The distribution of cultural service values is quite similar to the distribution of regulation and maintenance service values, with the low value areas being very large and located in the middle of the study area, while the high value areas are significantly smaller and scattered around the perimeter of the study area. Third, the global Moran's index of cumulative human disturbance is 0.798, indicating that human disturbance exhibits a significant spatial autocorrelation. The global Moran's index of ecosystem services is 0.410, indicating that ecosystem services present aggregation in space as well. Fourth, the Bivariate Moran′s I between human disturbance and supply services is −0.221. The Bivariate Moran′s I between human disturbance and regulation and maintenance service is −0.212. And the Bivariate Moran′s I between human disturbance and cultural services is −0.462, which shows a stronger negative correlation. [ABSTRACT FROM AUTHOR]

This paper aimed to investigate the prevalence of diabetes mellitus (DM) and explore the associated risk factors in a very elderly southwest Chinese population. From September 2015 to June 2016, a cross-sectional survey was conducted to obtain a representative sample of 1,326 participants over 80 years old living in Chengdu. The presence of DM was based on fasting plasma glucose (FPG) and 2-h plasma glucose (2-hPG) levels during an oral glucose tolerance test (OGTT). A logistic regression model was used to calculate the odds ratios (OR s) and 95% confidence intervals (CI s) of the potential associated factors. The participants' mean age was 83.5 ± 3.1 years. The overall prevalence of DM was 27.4%. The prevalence was higher in males (30.2%) than females (24.7%) (P = 0.02). The prevalence of DM increased with body mass index (BMI) and decreased with aging. The multivariate analysis suggested that male sex (OR = 1.433; 95% CI , 1.116–1.843), hypertension (OR = 1.439; 95% CI , 1.079–1.936), overweight or obesity (OR = 1.371; 95% CI , 1.023–1.834), high heart rate (≥ 75 beats/min; OR = 1.362; 95% CI , 1.063–1.746), and abdominal obesity (OR = 1.615; 95% CI , 1.216–2.149) were all significantly positively correlated with DM. However, age was negatively correlated with DM (OR = 0.952; 95% CI , 0.916–0.989). The prevalence of DM and newly diagnosed DM in a very elderly southwest Chinese population was high. OGTT screening should be performed regularly in people aged ≥ 80 years to ensure timely diagnosis of DM. [ABSTRACT FROM AUTHOR]

This paper investigates a location problem of public charging stations for electric vehicles with the objective of CO 2 emissions minimization through massive GPS-enabled trajectory data. The problem considers two distinct features, including CO 2 emissions generated in round trips to charging stations and remaining electricity restrictions on charging decisions. A data-driven and particle swarm optimization-based intelligent optimization approach is developed to handle this problem. We then present how to implement this approach by using taxi trip data in Chengdu, China as case data and explore how much data could reflect effectively the travel patterns of an area. The results of case study show that one-week taxi trip data are sufficient to handle the investigated problem. The results also validate the necessity of considering two realistic features, including CO 2 emissions in round trips to charging stations and remaining electricity restrictions on charging decisions, in charging station location problems. It can lead to (1) the reduction of daily CO 2 emissions captured by about 0.14–0.37 ha of forests in one year, and (2) 0.85%–2.64% more charging demands being satisfied per day. Flowchart of the proposed intelligent approach. Image 1010 • A location problem of public charging stations based on GPS-enabled trajectory data is studied. • A data-driven PSO-based intelligent optimization approach is proposed. • One-week data could reflect the travel patterns of a region well. • The impacts of two important realistic features are considered. [ABSTRACT FROM AUTHOR]

• The spatial competitions contribute to the evolution of rural residential land (RRL). • RRL in study area is characterized with "dense in the west and sparse in the east". • Evolution of RRL is impacted by macro factors and micro housing selection. • Spatial distribution pattern of RRL will remain nearly unchanged in the long run. Spatial systematic competition definitely contributes to rural residential evolution. Rural residential land of mountainous economic circle is generally hard to govern due to the disturbances from natural factors and economic activities. This study established a framework and adopted Kernel Density Estimation (KDE), Exploratory Spatial Data Analysis (ESDA), Geographical detector (Geo-detector) and Patch-generating Land Use Simulation (PLUS) to examine the evolution characteristics, drivers and trends of rural residential land of Chengdu-Chongqing Economic Circle(CCEC). Our research uncovered that: 1) The competitions between rural living space (RLS), rural production space (RPS) and rural ecological space(RES) substantially contribute to the evolution of rural residential land according to the conceptual framework, and the evolution in mountainous economic zone is greatly impacted by the close coupling interaction of macro driving factors and micro farmers' housing selection. 2)The characteristics of rural residential evolution of Chengdu-Chongqing area presents "dense in the west and sparse in the east", and the majority of which are concentrated in Sichuan Basin and Chengdu plain area. 3) Although the size of rural residential land in Chengdu-Chongqing area presents a fluctuant downward trend according to the simulation results, the spatial distribution pattern will remain nearly unchanged in the long run. 4) Rural residential evolution reflects the interactional process of muti-factors, as well as the matching result between man-land relationship and regional development goals under the land use policies. Overall, The theoretical and empirical research form the perspective of spatial competition in this paper provides a new idea for future studies on the evolution of rural residential land. [ABSTRACT FROM AUTHOR]

• ESRM unsupervised learning. • Two pre-trained target tasks: relationship consistency prediction and replace language model. • Downstream attribute alignment task fine-tuning. Multisource point of interest (POI) attribute alignment is the consistent processing of heterogeneous attribute values from different data sources pointing to the same POI data, which is one of the key technologies to achieve geospatial data fusion. However, semantic heterogeneity problems of synonyms and homographs among different POI data sources are encountered, which makes multisource POI data fusion challenging. This paper proposes a multisource POI attribute alignment method based on the Enhanced Semantic Representation Model (ESRM). First, the unlabeled corpus is preprocessed by Chinese word segmentation and attribute expression sequence construction. Then, the ESRM is pre-trained using the relational consistency prediction and replacement language model tasks. Finally, the model is fine-tuned through supervised learning to perform the attribute alignment task for multisource POI data, as per the specific downstream tasks. We used the POI attributes of Baidu Map, Tencent Map, and Gaode Map in Chengdu, China as the experimental data. The findings demonstrate that the proposed model outperforms existing methods for attribute alignment. Specifically, the category attribute consistency achieves a Macro-F1 value of over 90%, and the address attribute standardization achieves a BLEU-4 score of over 95%. [ABSTRACT FROM AUTHOR]

• The prototype experiment was carried out based on a new-type shield tunnel. • The mechanical behavior of segmental joint was studied using optical fiber sensor. • The failure characteristics of the joint with the assembled dowel were revealed. The dislocation of circumferential joints in shield tunnel has serious safety impact on subway operation. A new-type shield tunnel with dowel was used to limit the dislocation of circumferential joints of the tunnel in Chengdu Metro. In this paper, the prototype segmental experiment is carried out to analyze the shear performance of circumferential joint of tunnel with the dowel. The optical fiber sensing technology is used to investigate the deformation characteristics and crack propagation law of concrete. And the change laws of the dowel, bolt and rebars in segment are obtained in detail. The research shows that segments with the assembled dowel have its particular failure mode in the shear process. The dowel significantly changes the stress of concrete, bolt and rebars. The research results provide a theoretical basis for the application and promotion of the new segment structure. [ABSTRACT FROM AUTHOR]

Occupant behavior is a major contributing factor to building energy consumption. Large discrepancies might occur when occupants with diverse types in terms of behavior patterns reside in an identical building. Moreover, it is vital to derive several kinds of typical occupant behavior that represent the assemblage of occupants in a simplified way as the reference for building designers and energy policy makers. This paper presents a preliminary research on occupant behavior categorization through the building energy simulation, where some typical occupant air-conditioning behavior patterns use in Chengdu, China have been derived from a large-scale questionnaire survey based on the energy consumption levels. These typical occupant behavior patterns could be used as the context for an overview of building energy in a community, as well as the evaluation of building energy-saving technology where occupants are involved. Further efforts are required in the algorithm of dividing occupant behavior into typical patterns, the validation of the classification results, and the analysis of other behaviors. [ABSTRACT FROM AUTHOR]

• A spatial-temporal density-based clustering algorithm is proposed for trip end detection. • Three scenario optimization models are applied to improve the method capability. • A smartphone application is developed for GNSS data collection. • The potential of the method is demonstrated by comparing results with travel log. As a widespread portable probe, smartphone equipped with Global Navigation Satellite System (GNSS) can continuously track individual's travel trajectory, it is potential for trip end information identification. Existing studies mostly use rule-based methods with specific dwelling time and distance thresholds for trip end detection, these methods are highly dependent on expert experience and lack universality across different traffic environments. Some density-based spatial clustering methods also have issues in the case of GNSS signal missing, traffic congestion, short-time stays and repeat visits to the same place etc. Therefore, this paper proposes a two-step method for trip end identification by using smartphone GNSS positioning data. First, a spatial-temporal density-based clustering algorithm (ST-DBCA) is proposed for trip end identification, the method considers both spatial and temporal travel trajectory point density at the same time, and performs much better than traditional clustering methods. Second, three optimization models are further proposed to optimize the identification results, including 1) a short time stay optimization model, 2) a redundant stay optimization model, and 3) a traffic congestion stay optimization model. Field tests in Chengdu China are conducted to verify the feasibility and effectiveness of the proposed methods. Results show that the average trip end identification accuracy under different trip purposes reaches 92.8%, and the average errors of arrival and departure time are smaller than 150 s. [ABSTRACT FROM AUTHOR]

An increasing number of infrastructure mega-projects (IMPs) have been built worldwide over the past decades, that comprehensively assess the performance of operation and maintenance (O&M) are rare. This paper attempts to identify indicators to assess the O&M performance, literature review, document analysis, and Delphi method are used to determine 23 indicators, and an evaluation model combining entropy weight and improved D-S evidence theory was proposed. The indicator system and evaluation model can be validated by taking the Chengdu-Chongqing high-speed railway as an example. The results revealed the comprehensive O&M performance of project is good, the evaluation grades of social relations, economic benefits and operational capability are all good, while the environmental benefits is fair. It is importance to reduce the negative impact on the surrounding environment, improve the utilization rate and enhance the sustainable development of the IMP. Furthermore, the improvement strategies of O&M performance were formulated. [ABSTRACT FROM AUTHOR]

Nowadays, traffic situation prediction is a major concern. Travel time index prediction is one of the representative tasks in traffic situation prediction. Most research of the current prediction focuses on road-level speed, but the travel time index prediction of complex road network at the regional level has not been well solved. In this paper, we proposed a novel model with convolutional neural network called Seq2Img-DRNET. The sequence data with 10 min interval are converted into image data as the input of our model. The model is constructed by fusing dense connection network and residual network to solve the travel time index prediction of complex road network at the regional level. Taking two actual traffic networks of commercial district and scenic district in Chengdu as the example, we compared our model with common sequential model and convolution neural network model, and the results show that our model can effectively capture the spatial–temporal relationship characteristics of complex road network at the regional level and make accurate prediction. • Our method transforms time series data into image data for spatiotemporal information fusion. • A convolutional neural network model is constructed to accurately predict the travel time index. • Aiming at urban business districts and scenic spots prone to traffic congestion. • The prediction accuracy is significantly higher than the existing baseline model. [ABSTRACT FROM AUTHOR]

Abstract: The defects of traditional GM(1,1) model for forecasting logistics volume are analyzed in the paper. A new residual GM(1,1) model is proposed, which uses Markov state transition matrix to judge the sign of the residual predictive value. The new model could improve forecast accuracy. The model is used in forecasting the logistics volume of Xindu city in Chengdu, Sichuan province, PR. China. The prediction results proved that the model is reliable and with high forecast accuracy. [Copyright &y& Elsevier]

This study presents an event-driven discrete-event simulation (EDDES) approach based on an O2DES framework for a one-way electric carsharing system (OECS). The developed model mainly consists of three modules: station, electric vehicle (EV), and link. The system carefully considers the impact of road congestion on travel speed and designs a detailed charging process for EVs to approximate the real world. Based on the high speed of EDDES, a simulation-optimization framework that jointly determines station capacities, fleet size, and trip pricing to maximize the net revenue of operators is proposed. A simultaneous perturbation stochastic approximation (SPSA) algorithm is adopted to solve this problem. A case of EVCard in Chengdu is conducted to demonstrate the efficiency of the proposed framework. Several control experiments focusing on various pricing schemes are introduced to verify the advantage of optimization results further. Moreover, a one-way gasoline carsharing system (OGCS) is optimized to contrast with optimal OECS. These comparisons reveal some interesting findings: (1) from the perspective of operators, employing dynamic pricing achieves at least a 57.03% increase in net revenue compared to fixed pricing strategies; (2) from service, the optimal configurations and trip pricing of OECS effectively avoids vehicle overstock at stations; and (3) in terms of greener production, the optimal OECS could sustain a larger fleet size and a higher degree of cleaner production, and its profit generated by per CO 2 emission is over six times higher than that of the optimal OGCS. • This paper proposes an event-driven simulation model for electric carsharing systems. • The charging speed, range anxiety, and state of charge (SOC) are considered. • A simulation-optimization method is proposed for seeking the optimal system designs. • A simultaneous perturbation stochastic approximation is implied to solve the issue. • The greenhouse gas emission is captured to analyze the degree of green production. [ABSTRACT FROM AUTHOR]

• Multifractal method is introduced to determine the interaction between PM 2.5 and O 3. • Multi-temporal scaling of the interaction between PM 2.5 and O 3 is studied. • A mulitfractal index is set up to evaluate PM 2.5 -O 3 coordinated control capability. • Time dependence of the mulitfractal index in different cities verifies its validity. • The mulitfractal index can provide guidance for two-pollutant control strategies. Under combined air pollution conditions, the coordinated control of PM 2.5 and O 3 has significant implications for environmental management. To better understand the coordinated control capability of different metropolises, a prime consideration is how to develop a scientific and objective index for quantifying it. In this paper, based on the multifractal detrended cross-correlation analysis (MF-DCCA) method, a novel dynamical index η is developed to quantitatively determine the capability of metropolis to control PM 2.5 and O 3 pollution cooperatively. Furthermore, combing with the sliding window method, the high temporal scale resolution of η values of four metropolises (Beijing, Shanghai, Guangzhou, and Chengdu) in China is conducted in order to determine the temporal evolution of η. Then, the monthly variation of η values in the four metropolises is investigated. The U-shaped curves in the monthly mean η values indicate that PM 2.5 -O 3 coordinated control capability of the four metropolises is best in summer. The further discussions about the relations between index η and meteorological conditions have been made. Next, long-term trends of η series of four metropolises are studied by Mann–Kendall statistical test. Result shows that the trends of the η series of four metropolises are similar from 2015 to 2017. After 2017, the η series of Beijing, Shanghai and Chengdu show an increasing trend, while that of Guangzhou show a decreasing trend. This suggests that PM 2.5 -O 3 coordinated control capability of Guangzhou is gradually strengthened after 2017. This may be caused by the combined effect of targeted strategies and special climatic conditions in Guangzhou. The η index could directly quantify the two-pollutant coordinated control capability of different metropolises based on field observation, rather than rely on any subjective factor. It can provide guidance for policy-making of pollution emission reduction, so as to control the combined air pollution. [ABSTRACT FROM AUTHOR]

Bike-sharing is being widely used in many cities in China and gradually penetrated people's daily habits. However, because there are so many bicycle providers in the market, companies need to develop differentiated service strategies to compete in this homogenized market. Therefore, this paper examined the current bike-sharing enterprise service level in Chengdu, constructed a evaluation index system with 17 indicators under four main dimensions: perceptibility, availability, reliability, and sustainability; Developed a multi-stage hybrid fuzzy best and worst method (BWM) and Visekriterijumska Optimizacija i Kompromisno Resenje (VIKOR) method to evaluate bike-sharing service levels, understand user perceptions, and enable decision-makers to make more accurate and reliable judgments under uncertain conditions. This research provided a new comprehensive decision-making tool that the evaluation results could help managers improve their performances targeted, provide users with higher quality products and services, and contribute to the long-term development of companies. • A hybrid fuzzy BWM-VIKOR MCDM method to evaluate bike-sharing service in Chengdu. • An index system with 17 criteria and 4 dimensions to assess bike-sharing service. • It provides bike-sharing managers with valuable insights about their ranking. • We found that Meituan has the highest service level, followed by Hello and Qingju. • The evaluation results could help managers improve their performances targeted. [ABSTRACT FROM AUTHOR]

Cities are faced with booming populations, limited resources, and intensified pollution. To remain its sustainability and resilience against the varying environment, a city has to tackle the challenges from both the nature and human ourselves. This paper aims at evaluating the sustainability and resilience of cities in terms of their capabilities for maintaining, in face of potential threats in the future. Five subsystems, i.e. economy, environment, population, resource, science & technology are incorporated in the conceptual framework of linked urban sustainability and resilience. Through scenario simulations, we measure the performance of system elements and evaluate the resilience process with water resources trial when disturbance occurs. With Chengdu, China as the case, we have found that: (1) Limited water resources could not meet the basic demand well, and the quality could not be significantly improved till 2035 under the current path, and the modifications could be done with ongoing strategies. (2) Impacts of disturbances could be largely expanded in resource-dependent period, for which improving resource use efficiency and conducting industry structural adjustment could make cities more resilient. (3) The greater the presence of resilience system elements, the more developed the sustainability system. • A dynamic hybrid framework system of urban sustainability and resilience is constructed. • The performance of sustainability system elements is measured through scenario simulations. • Disturbance is introduced in for evaluating urban resilience. • The greater the presence of resilience system elements, the more developed the sustainability system. [ABSTRACT FROM AUTHOR]

The Sichuan Basin (SCB) has suffered from serious ozone (O 3) pollution in recent years, especially in summer. However, the formation and evolution mechanisms of O 3 pollution in the SCB remain poorly understood. In this paper, we comprehensively investigated the causes of elevated ozone concentrations in Chengdu in July 2017 with a combination of field measurements and the WRF (Weather Research and Forecasting)-CMAQ (Community Multi-scale Air Quality) model simulations. The ozone formation characteristics of continuous O 3 episodes and non-O 3 episodes were compared. Higher temperature (T), stronger solar radiation (SR), lower relative humidity (RH) and weaker winds were conducive to O 3 formation during ozone episodes. The O 3 concentration during O 3 episodes (202.6 μg m−3) was significantly higher than that during non-O 3 episodes (122.7 μg m−3), attributable to the higher volatile organic compounds (VOCs) concentration (34.0 ppbv compared with 27.5 ppbv) despite comparable NO x concentrations during O 3 episodes. The results of positive matrix factorization (PMF) for ambient VOCs showed that gasoline vehicle exhaust and solvent usage were the dominant sources during the month. However, the increasing source attributions from the petrochemical industry and biogenic emissions were responsible for the elevated VOCs concentrations during O 3 episodes. The relative contributions of local emission and regional transport to ozone were quantified during two O 3 episodes by conducting numerical sensitivity experiments. In the first episode, namely, the "O 3 -regionally transported" pattern, high ozone concentration in Chengdu was primarily due to the regional transport of ozone and ozone precursors through vertical mixing and horizontal advection from upwind areas (central and eastern SCB) when easterly or southeasterly winds prevailed over the SCB. In contrast, in the second episode, namely, the "O 3 -locally dominated" pattern, high local emissions under favorable meteorological conditions are the major reason for elevated ozone concentration. The process analysis results from the CMAQ model suggested that the vertical transport of O 3 that formed at the upper levels from intense gas-phase chemistry reactions was a dominant contributor to diurnal O 3 enhancement in O 3 episodes by 45 μg m−3, while horizontal transport contributed more than 80% of the nocturnal O 3 concentration. This study provides a better understanding of ozone photochemical formation and regional transport mechanisms in the SCB under complex terrain conditions, and contributes to ozone pollution prevention in other basins around the world. • Two high O 3 episode in Chengdu was simulated using the WRF-CMAQ modeling system. • Ozone and precursors from upwind-polluted areas are crucial to high ozone episodes in Chengdu. • Vertical mixing plays an important positive role to surface elevated ozone in Chengdu. [ABSTRACT FROM AUTHOR]

A new mass-time primary standard for high pressure natural gas, which is based on electromagnetic balance and hydraulic fast-acting valves, was set up at the beginning of 2017 in Chengdu, China. The full load of the electromagnetic balance is 3 tons and the measurement uncertainty of mass is better than 1.0g(k = 2). The opening and closing time of the hydraulic fast-acting valves can achieve 33 ms±3 ms.The operation pressure and flowrate range of the facility is (4–60)bar.a and (5–410)m3/h respectively. In accordance with the preliminary tests, the estimate uncertainty of sonic nozzles calibration is between 0.10% and 0.12%(k = 2). The operation principle, testing results and the uncertainty evaluation are presented in the paper as well as some improving ideas. • A new mass-time primary standard for natural gas based on 3t electromagnetic balance and hydraulic fast-acting valves. • Gas collecting tank, balancing tank and weights are operated automatically by PLC. • Two hydraulically-actuated valves equipped with 13 optical gratings are operated in about 33ms and "zero overlap" are achieved. • Uncertainty evaluation and one year time stability test results show that the CMC is about 0.10%-0.12%(k =2). • Inter-lab comparisons with NMI and CEESI also verify the CMC. [ABSTRACT FROM AUTHOR]