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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]

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 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]

• 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]

Urban traffic involves two stochastic factors, complicating accurate modeling: (1) Driver stochasticity, arising from driver heterogeneity and random environment perturbations, introduces unpredictable fluctuations in urban traffic volume. (2) Traffic correlation, referring to the interaction in traffic states across different regions. This paper proposes a waveguide-based quantum walk urban traffic volume model (UTVDC-QW), incorporating driver stochasticity and traffic correlation. The model uses waveguide arrays to map urban traffic networks, incorporates random environment perturbations via noise, and traffic correlation via waveguide coupling. UTVDC-QW develops measures of environment perturbations and traffic correlations. Experiments with Chengdu traffic datasets demonstrate that UTVDC-QW improves the simulation accuracy compared to reference models. In addition, the model reveals the characteristics of environment perturbations and traffic correlation in the study area. Overall, this study effectively incorporates driver stochasticity and traffic correlation by waveguide-based quantum walk, is a pioneering attempt to apply quantum computing to urban traffic modeling. [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 business environment is becoming a yardstick for assessment of local governments in China, and thus the question of how to evaluate the business environment of urban agglomerations has become an important new issue. This paper proposes a quantitative method for the assessment of business environments in urban agglomerations for purposes of city ranking. First, a business environment evaluation index system for urban agglomerations, including the four dimensions of market, government, law, and humanistic, was established. Second, A hybrid model combined entropy weight method (EWM), genetic algorithm (GA), and back propagation (BP) neural network was constructed to quantise the business environment. Then, the Chengdu-Chongqing and Beibu Gulf urban agglomerations in China, the two most important urban agglomerations of the new western land-sea corridor, were selected as cast study. The results are as follows: (1) The business environments of the two most important urban agglomerations of the new western land-sea corridor have been improved, but the differences between them are significant. The level of development of the business environment of Chengdu-Chongqing is higher than that of Beibu Gulf; specifically, the biggest gap is of the rule of law environment. The gap in the governmental business environment is the smallest. (2) From 2015 to 2020, the difference in business environment between the two most important urban agglomerations in the new western land-sea corridor decreased. However, intra-group differences were the largest. (3) The spatial pattern of the business environment in the new western land-sea corridor shows a "northwest-southeast" distribution, with the center of gravity of the business environment moving to the southeast from 2015 to 2020. (4) Compared with focusing on the development of the business environment in large cities, promoting the business environment in small and medium-sized cities has a more significant effect on improving the business environment in urban agglomerations. • A quantification method is proposed. • Business environment in the corridor is uneven. • Small cities should develop firstly. [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]

• Present a methodology for investigating ring road investment and cordon tolling problems in a congested ring-radial city. • Establish a two-dimensional urban system equilibrium for a ring-radial city. • Develop two social welfare maximization models (a short-sighted decision and a far-sighted decision) to optimize ring road investment and cordon tolling schemes. • Carry out a case study on the city network of Chengdu China. Ring roads, as candidate cordons, provide a convenient condition for implementing cordon tolling schemes. This paper presents a methodology for investigating the ring road investment and cordon tolling problems in a congested ring-radial city. A two-dimensional urban system equilibrium for a ring-radial city is first formulated, in which interrelated equilibria among stakeholders, including the authorities, property developers, households and commuters, are explicitly considered. Two social welfare maximization models for optimizing the ring road investment and cordon tolling schemes, a short-sighted and a far-sighted one, are then proposed. In the short-sighted model, the ring road investment decision is first made, and then the cordon tolling scheme is optimized based on the determined ring road locations as candidate cordons. However, in the far-sighted model, a simultaneous decision of the ring road investment and cordon tolling is made. The proposed models explicitly incorporate the estimation of the intra-area travel. A case study applied to the city network of Chengdu China shows that ring road investment and cordon tolling can reshape the urban spatial structure as a result of the tug-of-war between the dispersion effects due to ring road investment and the concentration effects due to cordon tolling. The far-sighted solution entails building more ring roads than the short-sighted solution, and is closer to the social optimum solution. Whether all ring roads are tolled, or just one, the far-sighted solution yields an appreciably higher welfare gain. The optimal single-cordon tolling scheme performs nearly as well as the optimal multi-cordon tolling scheme in terms of the social welfare. Ignoring the household residential relocation behavior leads to underestimates of total cordon toll revenue and welfare gain. [ABSTRACT FROM AUTHOR]

Adding new links to an existing urban rail transit (URT) network helps improve its operations by shortening passenger travel time under normal operations and disruptions. However, only a few studies have considered the impact of uncertain disruption occurrence stations on URT network design. This paper addresses this gap by proposing and solving a scenario model for determining the optimal scheme for adding new links to an existing URT network while considering the uncertainty of disruption occurrence stations. Numerical experiments are conducted on the Chengdu subway system to verify the effectiveness of the proposed model. Results indicate that disruptions occurring at a station result in an average performance loss of 0.61%. The scheme for adding new links obtained from this model helps improve network performance under normal operations and disruptions. The cumulative improved normal operating performance during the entire day and the ratio of improved weighted resilience metric are 1.638 and 24.59%, respectively. The solution of the proposed model is greatly affected by several parameters such as the total length of new links. Some useful suggestions for guiding URT network extensions are proposed based on the results of the sensitivity analysis of the above parameters. [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 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 Xigeda formation, widely distributed in southwestern China, is a special semi-diagenetic rock with horizontal bedding，which is mudding in water. In order to study the failure law, catastrophe characteristics and catastrophe mechanism of the interbed type to the surrounding rock and structure of Xigeda formation tunnel, this paper, by means of field investigation, indoor test, numerical simulation, field monitoring and theoretical research, relying on more than ten Xigeda formation tunnels in the Chengdu-Kunming double track railway, analyzes the apparent failure characteristics and catastrophe mechanism of the surrounding rock of Xigeda formation tunnels with different interbed types. The results show that: the catastrophe characteristics of Xigeda formation tunnel have certain water content sensitive area and influence area division of interbed type. The water content of Xigeda formation is 25% - 30% of the catastrophic critical region of Xigeda formation tunnel. In this critical region, the vault of the overlying Xigeda formation tunnel is prone to large deformation, the invert of the underlying Xigeda formation tunnel is prone to large uplift deformation, and the side wall and tunnel face of interlayer Xigeda formation tunnel are prone to large extrusion deformation; when the water content of Xigeda formation is 30%, it becomes a weak layer, and under the effect of interbed, the rock and soil of Xigeda formation are prone to large deformation, resulting in catastrophes by extruding the tunnel structure; based on the analysis of chemical effect, physical effect and mechanical effect, the structural components of interbed rock chemically react with water, leading to weakening of the strength and causing catastrophic effects. The catastrophic effect is similar to that of simply supported beam in the strong and weak regions of the interbed, while the weakening effect of the interlayer structural plane leads to the opening of the interlayer fracture plane of the tunnel surrounding rock and the large interlayer dislocation displacement, which forces the shear failure of the supporting structure. The research results of this paper can provide theoretical guidance for the Sichuan-Tibet railway to be constructed. • The research object is Xigeda stratum which is sensitive to water. • Reveal the disaster modes of Xigeda stratum tunnels with different interbed types. • Investigate the catastrophic mechanism of Xigeda stratum tunnel under interbed effect. • Obtain the catastrophic critical water content of Xigeda stratum tunnel. • Provide theoretical basis for Xigeda stratum tunnel construction. [ABSTRACT FROM AUTHOR]

Accessibility-oriented development (AOD) strategies are the extension of transit-oriented development (TOD) strategies but focus more on access to destinations. Adopting AOD strategies can help identify and improve the defects of imperfect TOD construction. Based on AOD strategies, this paper evaluated the comprehensive accessibility to multiple public facilities with the Gaussian-based two-step floating catchment area method (G2SFCA) in the heart area of Chengdu City and further analyzed its equity issues. The comprehensive accessibility analysis proved the efficiency of AOD-based analysis in public resources shortage area determination and TOD level evaluation. The equity analysis indicated that the improvement of equity could not reflect the progress on total accessibility but only rationalize the allocation structure of public resources. Based on the case study, several suggestions for policy-making are given, including the construction of the non-motorized transportation system, adjustment of supply allocation, and comprehensive consideration of equity and accessibility in urban planning. [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]

• An efficient simulation system is proposed for the urban rail networked operation. • The interaction between passenger and train is delicately depicted in simulation. • A simulation-based constrained SPSA algorithm is used to coordinate train timetables. • The improved solution reduces passenger waiting time without adding operating cost. Synchronous optimization of train timetables is to solve the problem of train service coordination among different lines in the urban rail transit network, which can reduce passenger waiting time and subsequently enhance passenger satisfaction. This paper develops an efficient simulation system to reproduce the train operation of an urban rail network. It takes into account the realistic characteristics of urban rail operations, including time-dependent demand, dynamic dwell time, and train capacity constraint. In the simulations, both the train trajectories and the passenger waiting time can be easily obtained. On this basis, a simulation-based algorithm (i.e., simultaneous perturbation stochastic approximation, SPSA) is designed to search an improved flexible-interval dispatch solution in the network. A case study of the Chengdu urban rail transit network is implemented to verify the effectiveness of the simulation system and the optimization algorithm. The simulation system is found to be time efficient since it takes less than 1 s to simulate the network with 4 h of operation and more than 200,000 passengers. The optimization algorithm is also effective as it can reduce the average passenger waiting time by 9.52 % compared to the equal-interval dispatch solution without increasing the train service frequency. It is also found that the reduction in waiting time is mainly due to the improved coordination of train services in the transfer station. [ABSTRACT FROM AUTHOR]

Publicly developed logistics parks (PDLPs) have been considered one of the measures in urban logistics policies to reduce carbon emissions. In response, this paper draws on the "pollution transfer" hypothesis, constructs a carbon emission analysis framework for PDLPs, and uses spatial econometric methods to explore their carbon emission effects. The findings suggest that from a global perspective, PDLPs can lead to the emergence of "pollution havens" in the suburban and exurban areas while relieving the carbon emission burden in the urban core. Secondly, PDLPs can further contribute to forming "pollution havens" by promoting logistic re-agglomeration, and the "innovation compensation" effect of PDLPs is not significant. Finally, the carbon emission effects of PDLPs are spatiotemporally heterogeneous. A carbon reduction effect is noted when the logistic re-agglomeration in the location of PDLPs is higher, and an "innovation compensation" effect is also produced when the technological innovation is higher. [ABSTRACT FROM AUTHOR]

Drive-by sensing (i.e. vehicle-based mobile sensing) is an emerging data collection paradigm that leverages vehicle mobilities to scan a city at low costs. It represents a positive social externality of urban transport activities. Bus transit systems are widely considered in drive-by sensing due to extensive spatial coverage, reliable operations, and low maintenance costs. It is critical for the underlying monitoring scenario (e.g. air quality, traffic state, and road roughness) to assign a limited number of sensors to a bus fleet to ensure their optimal spatial–temporal distribution. In this paper we present a trip-based sensor deployment problem, which explicitly considers timetabled trips that must be executed by the fleet while a portion of them perform sensing tasks. To address the computational challenge in large-scale instances, we design a multi-stage solution framework that decouples the spatial–temporal structures of the sensing task through line pre-selection and bi-level optimization. As a result, the computational complexity is reduced to be sub-linear w.r.t. the number of lines, rather than combinatorial w.r.t. the number of buses in existing vehicle-based approaches. A real-world case study covering 400 km 2 in central Chengdu demonstrates the effectiveness of the model in solving large-scale problems. It is found that coordinating bus scheduling and sensing tasks can substantially increase the spatial–temporal sensing coverage. We also provide a few model extensions and recommendation for practice regarding the application of this method. • Bus drive-by sensing is a promising way to survey urban environment at low costs. • Existing vehicle-based sensor deployment is a combinatorial problem. • A trip-based model considers timetabled trips while performing sensing tasks. • A multi-stage solution algorithm yields sub-linear complexity w.r.t. the number of lines. • A real-world case study reveals its effectiveness and managerial insights. [ABSTRACT FROM AUTHOR]

Park City is a new concept for sustainable urban development. Urban Parks are the core of Park City, where park accessibility is the central index of park services. Previous studies have been conducted primarily from residents' perspective, based on the "O-D" (origin to destination) model to access the park accessibility. However, urban parks should be considered a place supporting the coexistence of tourists and residents. Moreover, tourists' movement patterns differ from those of residents, which are expressed as "no turning back" and traverse circular or nodal routes. Consequently, this paper proposes that the traditional "O-D" model be expanded to be consistent with the movement patterns of tourists. This paper employs Park City Chengdu as an exemplar and proposes a "D-D" (destination to destination) model to facilitate further park accessibility. The calculation process was based on a multiple transportation mode measure with multiple time thresholds, entrances, and exits, supported by Web API. The results reveal the key time nodes and park accessibility rankings for four transportation modes. In particular, the park accessibility presents a "dual-core, several sub-centers and cluster-types" circle structure. Furthermore, the park accessibility in Chengdu was generally balanced, except within 25 min, as well as walking and public transport. This paper provides new hypothetical perspectives and practical experiences for studying urban park accessibility, and novel ideas for parks' sites and distribution while serving as a model for cities worldwide for sustainable urban development. • "D-D" (destination to destination) model based on the travel behaviors of tourists to facilitate park accessibility • Multiple transportation mode, time thresholds and entrances and exits, supported by Web API • Determine the transportations' weights and time thresholds for spatial analysis by data itself • New ideas for urban parks' sites and distributions • "Park Stepping Stones" to form park networks based on "D-D" model results [ABSTRACT FROM AUTHOR]

Improving the transport system to enhance women's access to social opportunities and services has been a key initiative in mitigating gender inequality. Studies have examined women's different travel demands and experiences from men, and the mismatch between women's demands and transport services. However, little attention has been given to the gender gap in ride-hailing usage in the context of the fast development of platform economy-based new transportation services. Thus, this paper examines the nexus between gender and inequalities in ride-hailing from the demand side. Two key questions are explored: if ride-hailing serves women more/less, how does this gender difference in using ride-hailing occur? Does the emergence of ride-hailing mitigate or deteriorate the existing gender gap in capability to move? An innovative integration of big data and time-space geography approaches is developed to examine the multi-source data collected from Chengdu, China. The modelling results indicate that ride-hailing emerges as an affordable travel mode addressing women's demand for long-distance travel in the Chinese context of women's high labour participation and thus the existence of a considerable number of employed women with a sizeable daily activity space and a considerable income. The gender gap in capability to move is thus mitigated by ride-hailing at an aggregate level. This paper calls for a more nuanced and context-specific understanding of how ride-hailing may provide challenges and opportunities to gender equity in daily travel. [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 GHG emissions of Chengdu Metro were calculated using the LCA framework. • A WOA-DELM prediction model for Chengdu Metro GHG emissions was developed. • The effectiveness of the model was verified with other algorithm models. • The rough calculation formulas for GHG emissions of Chengdu Metro were obtained. With the mass construction of urban subways, the global greenhouse gas (GHG) emissions have been on the rise. This paper provides statistical evidence to support the infrastructure of subway emissions reduction through a study of GHG emissions during the construction stage of 6 stations and 7 sections of Chengdu Metro Line 18. Using the emission coefficient method, the GHG emissions from building material production, transportation and site construction in subway stations and shield sections were calculated, and a subway GHG emissions prediction model dependent on deep extreme learning machine (DELM) with whale optimization algorithm (WOA) was established(i.e., WOA-DELM). Compared with some optimized DELMs, namely wind driven optimizer (WDO) -DELM, grey wolf optimizer (GWO) -DELM, particle swarm optimizer (PSO) -DELM, artificial bee colony (ABC) -DELM, multi verse optimizer (MVO) -DELM, and atom search optimizer (ASO) -DELM, and some non-optimized algorithm models, namely back propagation neural network (BPNN), kernel extreme learning machine (KELM) and DELM, the correlation consistency of WOA-DELM algorithm prediction results (0.757) was found to be slightly higher. Through sensitivity analysis of the main input variables of subway GHG emissions with the WOA-DELM algorithm model, it was determined that the key influencing factors of station GHG emissions prediction were the station length and the depth of track surface, with relative change rates of corresponding variables of GHG emissions at 30.1% and 23.1% respectively. Finally, a rough prediction formula of GHG emissions from Chengdu Metro stations and shield sections were fitted based on the key influencing factors of GHG emissions. This study provides a practical and effective reference for reducing GHG emissions in subway construction and operation. [ABSTRACT FROM AUTHOR]

The rebalancing of idle vehicles is critical to mitigating the supply–demand imbalance in on-demand ride services. Motivated by a ride service platform, this paper investigates a short-term vehicle rebalancing problem under demand uncertainty in the presence of contextual data. We deploy a novel data-driven robust optimization approach that takes a direct path from "Data" to "Decision" instead of the predict-then-optimize paradigm and leverages the prediction problem structure to seamlessly integrate demand predictions with optimization models. We further develop a risk-based uncertainty set to evaluate how well uncertain demand is estimated from contextual data by prediction models, and discuss the classes of prediction models that are highly compatible with robust optimization models. Based on the convex analysis and duality theory, we reformulate the original models into equivalent Mixed Integer Second Order Cone Programmings (MISOCPs) that are solvable via state-of-the-art commercial solvers. To solve large-scale instances, we utilize the affine decision rule technique to derive polynomial-sized reformulations. Extensive experiments are conducted on the instances based on a real-world on-demand ride service in Chengdu. The computational experiments demonstrate the promising performance of our rebalancing strategies and solution approaches. • Study a contextual vehicle rebalancing problem under demand uncertainty. • Deploy a data-driven approach to integrate optimization and demand prediction. • Develop a risk-based uncertainty set characterized by a set of prediction models. • Present exact and approximation reformulations in a master–subproblem framework. [ABSTRACT FROM AUTHOR]

The modern adaptability of traditional villages has received considerable attention worldwide. The composition of different local elements leads to a differentiation between native and constructed localities in the modern adaptation of rural areas. By introducing a circular perspective based on field-spatial production theory, this study takes Qucun Village in Sanmenxia and Mingyue Village in Chengdu, China, as case sites and finds that the local system has full potential to serve as the keynote of rural development and that foreign resources are embedded in the local area to achieve two-way support with local elements. Through research, it can be found that economic capital supports the existence of locality in modern times, cultural capital clarifies the level of native and constructed characteristics, social capital expands the connotation of "locality", and symbolic capital shapes the value of native and constructed localities. Subsequently, the paper reveals that native and constructed localities are not opposites, but shape new spatial hotspots and enrich the network levels of localities, thus enabling the countryside to adapt to dynamic evolution. • Localities of the two villages were compared based on multiple subjects. • Constructing field-spatial circular production theory to explain locality changes. • Constructed locality sometimes strengthens the native locality of the village. • Locality promotes rural adaptation to modernity by integrating complex resources. [ABSTRACT FROM AUTHOR]

• A novel framework was proposed to jointly optimize demand-responsive flexible bus dispatching, pricing, and passenger assignment. • Stochastic elastic demand volume, stochastic location, and random cancellation behaviour due to competition with taxis are jointly captured in the model. • The problem is formulated as a two-stage stochastic problem with recourse and solved by a reliability-based solution method. • Sensitivity analyses are conducted on cancellation penalty, taxi price, and zone division, and the model is applied to a scenario based on real data. Order cancellation is ubiquitous in on-demand mobility pre-booking systems, which affects the pricing and operational strategies of on-demand mobility services. This paper investigates the order cancellation behaviour in a frequency-based flexible bus (FB) system, which provides customized door-to-door service, considering stochastic elastic demand volume and stochastic spatial demand distribution. A two-stage stochastic model that simultaneously optimises the FB service price, FB dispatching, and passenger assignment is developed while capturing the effect of pre-booked order cancellation probability. This cancellation probability is calculated based on the pricing and availability of street-hailing taxi and FB services. The availability of FBs is derived from the underlying stochastic demand and order assignment process, which makes the model nonlinear. To solve the mixed integer nonlinear model, a two-phase decomposition scheme is employed, and then approximation techniques are implemented to make each decomposed problem solvable by commercial solvers. Finally, a gradient decent solution algorithm is applied. Numerical studies are conducted to show the necessity of capturing the impact of order cancellation and illustrate the influence of cancellation penalty, taxi price, and zone division. Finally, a case study with Chengdu data is implemented to examine the performance of our solution algorithm for large-scale optimisation. [ABSTRACT FROM AUTHOR]

Since the 2010s, two worldwide trends have reshaped the urban housing system and induced drastic neighborhood change: the financialization of rental housing and the rise of the platform economy. In China, with continued investment from speculative financial institutions, platform companies aggressively acquired rental houses from individual landlords to develop a platform-based housing rental economy. How does this new rental economy affect housing supply, rents and inequality? This research answers this question by taking Chengdu, China as a case study. A mixed method approach of big data analytics, hedonic pricing model, and field investigations were used to unpack: (a) the pattern of platform houses distribution and its indication of spatial strategies of the financialized platform economy to grab land rent; (b) the effect of such new housing rental economy on housing rents; (c) the effect of the degree of financialization on platform houses' rental prices. The results inform the debate on the disrupt effect of platform economy and housing financialization on equitable urban development, particularly the heterogeneity among cities and countries. This paper contributes to understanding financial investors' glocalization strategy and the state's territorialization strategy as two crucial factors for the variegation of rental housing financialization. • Houses with small living room and more bedrooms that can generate more rent. • Platform companies tend to choose houses nearby public transport services. • High concentration of platform houses increases rental price of surrounding normal houses. • Degree of financialization significantly increases the price premiums of platform houses. [ABSTRACT FROM AUTHOR]

Rail corrugation is a common form of damage in subway tracks. This paper presents the results of a 1.5-year study of track wear at two sites on the Chengdu Metro. Findings revealed the appearance of corrugation with a 30 mm main wavelength near the middle of the curve on the low rail of a 350 m radius track. As the subway's operating hours extended, the corrugation expanded toward the transition curves, and the wavelength changed. Conversely, no rail corrugation was seen on the rails of a 610 m radius curved track. A finite element model of the wheelset–rail system was established based on friction-induced self-excited vibration (FSV), and complex eigenvalue and instantaneous dynamic analysis methods were employed on the model to predict corrugation. Results showed that the increased elasticity modulus of the rail pad and baseplate pad could restrain long-pitch corrugation, and matching the material parameter of the pads reasonably reduced the frequency of short-pitch corrugation (SPC). In addition, the lack of corrugation on the 610 m radius track was studied, with results showing that the creep forces at the interface were not saturated. Thus, no FSV occurred, and no corrugation arose. • Long-term tracking of corrugation on the rails of a 350 m and 610 m radius track. • Corrugation position and wavelength changed as the subway's operating accumulated. • Friction-induced self-excited vibration was the leading cause of rail corrugation. • Vibrational interference of guiding wheelsets causes the change of corrugation. • Increasing the elastic modulus of pads can inhibit long-pitch corrugation. [ABSTRACT FROM AUTHOR]

Cities worldwide are deploying electric buses rapidly to enhance eco-friendly public transportation. Most existing electric transit systems rely solely on stationary charging at terminals and have to equip buses with large-sized batteries to maintain operation in networks. Consequently, the stationary-charging electric bus (SE-Bus) systems are subject to high battery costs, limited range per charge, long charging time (layover time), etc. These limitations may be addressed by integrating the new wireless power transfer technology, which provides contactless charging to the operating buses without extra delays. To guide wireless-charging electric bus (WE-Bus) designs in corridors, this paper proposes an optimization model to jointly optimize (i) the electric supply system and (ii) the transit service that are interdependent. Specifically, we construct parsimonious models for system metrics using five design variables/functions, i.e., battery size, the number of wireless power transfer (WPT) pads, stationary charging rate, service headways, and stop densities. Accordingly, we formulate the optimal design problem to minimize the generalized system cost as the sum of the transit agency's and patrons' costs. The problem is attacked by first decomposing into two sub-problems regarding (i) electric supply system design and (ii) transit service design and then solving them iteratively. The (i) sub-problem is converted to a linear programming problem by linearization, and the (ii) sub-problem is analyzed with optimal conditions. Numerical tests show the proposed solution approach produces accurate results. Extensive numerical experiments unveil several findings. First, the WE-Bus systems outperform the SE-Bus in various scenarios except when wireless charging facilities are too costly. Second, the optimized WE-Bus designs can reduce costs not only to the agency (with smaller-sized bus battery and reduced fleet cost) but also to patrons (who enjoy improved services with shorter headways). Additional parameter analyses also investigate the impacts of demand levels, wireless charging rates, depths of discharge, etc. Lastly, a case study in Chengdu (China) illustrates the application of the proposed model. • Models are built for wireless-charging electric bus (WE-Bus) designs. • The electric system and the transit service are jointly optimized. • WE-Bus systems show a wide range of application. • Optimized WE-Bus designs may save costs for the agency and transit patrons. [ABSTRACT FROM AUTHOR]

Vehicle-based mobile sensing (a.k.a drive-by sensing) is an important means of surveying urban environment by leveraging the mobility of public or private transport vehicles. Buses, for their extensive spatial coverage and reliable operations, have received much attention in drive-by sensing. Existing studies have focused on the assignment of sensors to a set of lines or buses with no operational intervention, which is typically formulated as set covering or subset selection problems. This paper aims to boost the sensing power of bus fleets through active scheduling, by allowing instrumented buses to circulate across multiple lines to deliver optimal sensing outcome. We consider a fleet consisting of instrumented and normal buses, and jointly optimize sensor assignment, bus dispatch, and intra- or inter-line relocations, with the objectives of maximizing sensing quality and minimizing operational costs, while serving all timetabled trips. By making general assumptions on the sensing utility function, we formulate the problem as a nonlinear integer program based on a time-expanded network. A batch scheduling algorithm is developed following linearization techniques to solve the problem efficiently, which is tested in a real-world case study in Chengdu, China. The results show that the proposed scheme can improve the sensing objective by 12.0%–20.5% (single-line scheduling) and 16.3%–32.1% (multi-line scheduling), respectively, while managing to save operational costs by 1.0%. Importantly, to achieve the same level of sensing quality, we found that the sensor investment can be reduced by over 33% when considering active bus scheduling. Comprehensive comparative and sensitivity analyses are presented to generate managerial insights and recommendations for practice. • Bus drive-by sensing is a promising way to survey urban environment at low cost. • Studies have focused on sensor deployment without intervening bus operations. • We explore the sensing power of bus fleets unlocked by multi-line scheduling. • This allows sensors to be optimally circulated while timetabled trips are served. • A real-world case study reveals its cost-effectiveness and managerial insights. [ABSTRACT FROM AUTHOR]

Urban traffic flow prediction has received much attention in the past few years, especially after the availability of huge traffic data. In addition, the efficacy of some existing traffic flow techniques heavily depends on some influential external factors (like weather, geographical information, point-of-interests (POIs), or road information). However, exploitable data containing such auxiliary information are extremely limited. One primary reason is that some rising cities lack the resources necessary for collecting such data. Therefore, it is difficult to pick an effective method that can accurately predict the traffic state when the data is limited. Due to that, this paper proposes a framework for achieving accurate predictions based on four data characteristics and the temporal correlation of neighbors without using additional influential factors. Specifically, we first propose a novel Multi-Region Correlation (MRC) method to make regions cooperate and share their traffic flow and patterns. Then, a deep recurrent network is used to capture the neighbors' temporal correlation by integrating their traffic history that flow among regions and their neighbors. When analyzing the traffic data history, we model four data characteristics (seasonality, trend, residual, and cyclic). And lastly, we employ a Multiple Linear Regression Unit (MLRU) to predict the future traffic directly from the neighbors' traffic. We conducted extensive experiments on two real-world datasets collected from two major cities in China, Chengdu and Xi'an. The results demonstrate that the proposed model can achieve superior performance compared to many existing models in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

• We develop a bottom-up approach for urban flood hazard at multiple levels. • The pluvial flood dynamic model can map flood-affected zones at gridded level. • Upscaling mapping of inundation can reduce the effects of data bias or availability. • Coarser hazard mapping is less sensitive to the data quality and model uncertainty. With increasing urban flood risk due to urbanisation and climate change, flood hazard prediction is ever more crucial for flood risk management and emergency response. However, when sufficient high-quality data are lacking, a standard flood inundation modelling approach has significant uncertainties. Therefore, this study develops a bottom-up approach for urban flood hazard mapping at multiple levels (grid-kilometre-district), built upon the integration of grid-based flood modelling with data acquisition from open sources. This reduces the adverse effects of data scarcity and quality on hazard modelling. In the paper, we first set out an integrated approach for gridded inundation mapping in an urban basin using a hydrodynamic model supported by crowd-sourced social media data. Then, applying the model to flooding in Chengdu in August 2020, we articulate how input data bias and parameter uncertainty both affect urban inundation modelling. The results show that the choice of terrain data and the quantification of urban drainage flows significantly influence the modelled urban inundation extent, depth and duration. This indicates the potential for large variation when using urban inundation mapping merely relying on a single-scale flood hydrodynamic modelling even when supported by crowd-sourced data. The multilevel hazard mapping approach developed here presents multi-layered and comprehensive inundation mapping; thus the effects of data bias or availability are reduced and the coarser hazard mapping shows less sensitivity to the data input quality and model uncertainty, indicating relatively higher reliability at this higher spatial scale. The grid-kilometre-district three level approach provides more reliable flood hazard mapping, which can support rainstorm-induced flood management in data scarce cities. [ABSTRACT FROM AUTHOR]

• We envision the emerging Mixed Operation Zones and investigate the impact of the complementarity of autonomous vehicles and human-driven vehicles on the ride service. • We propose a demand-oriented robust minimum fleet problem and employ a two-stage robust optimization approach. • A tailored column-and-constraint generation approach is developed to solve the robust models to optimality efficiently. • Experimental results imply huge potential benefits from Mixed Operation Zones on improving service performance for ride service platforms. In this paper, we envision an emerging Mixed Operation Zone (MOZ) where both autonomous vehicles (AVs) and human-driven vehicles (HVs) are present for on-demand ride services. This paper aims to size and operate a fleet of AVs and HVs in the presence of MOZs and to investigate the impact of MOZs on on-demand ride services. Considering the demand uncertainty, we propose a demand-oriented robust minimum fleet problem (RMFP) and employ a two-stage robust optimization (RO) to model the decision-making. Fluctuant demand is bounded by distribution-free uncertainty sets. For the convenience of solving RO models, we reformulate the second-stage recourse problem with an equivalent mathematical programming formulation. A tailored column-and-constraint generation algorithm is developed to solve the RMFP exactly. The algorithm is proved to converge in a finite number of iterations. Extensive experiments are conducted on the instances based on a real-world on-demand ride service in Chengdu. The developed algorithm performs better than the state-of-the-art Benders decomposition approach. Numerical results imply huge potential benefits from MOZs on improving service performance for ride service platforms. [ABSTRACT FROM AUTHOR]

Due to the limited underground space in the city, it is inevitable that the new subway may undercross the existing subway during the construction process. The current research mainly focuses on the dynamic response of single tunnel or the construction mechanics and ground motion response of tunnels with short-distance, while there is little research on the dynamic characteristics of subway tunnels undercrossing the existing subway with zero-distance. Therefore, in this paper, relying on a subway tunnel closely undercrossing the existing Line 1 with zero distance in Chengdu, using on-site test and numerical calculation, the vehicle-induced dynamic response characteristics of the new subway tunnel is analyzed under the moving train load of the existing Line 1, and the dynamic response differences of the new subway tunnel under different vehicle speeds and operation modes of the existing Line 1 is also discussed. The results show that the relationship between the acceleration of each part of the new subway tunnel is: roof > the middle wall > side wall > floor. For the horizontal acceleration, since the thickness of the side wall is larger than that of the middle wall, and the difference in the boundary constraints between the middle wall and side wall, the acceleration peak value of the middle wall is larger than that of the side wall. The dynamic response of the new subway increases with vehicle speeds, and the increasing rate of the vehicle speed in the range of 40–60 km/h is greater than that of 60–80 km/h. Compared with one-way operation, the two-way meeting has a certain superimposed effect on the acceleration response of the new subway tunnel and has the greatest impact on the acceleration response of the middle wall. The Fourier acceleration spectrum shows the strongest response around 60 Hz and 130 Hz. It is also found the operation of existing subway has adverse impact on the new subway, and certain vibration reduction measures should be taken to ensure the stability of new subway. • The numerical calculation of vehicle-induced dynamic response of undercrossing subway was calibrated by on-site test. • The comparisons of dynamic response of undercrossing subway under different vehicle speeds of upper subway were discussed. • The comparisons of dynamic response of undercrossing subway under different operation modes of upper subway were discussed. [ABSTRACT FROM AUTHOR]

• A hybrid deep learning model within decomposition-ensemble strategy named DEASeq2Seq is proposed for multistep metro passenger flow prediction. • DEASeq2Seq employs CEEMDAN and RQA to capture intrinsic dynamics. • An attention-based Seq2Seq model is developed to characterize temporal dependencies. • Real-world datasets to verify the proposed model and further model interpretation. Short-term passenger flow prediction has practical significance for metro management and operation. However, the complex nonlinear and non-stationary characteristics make it challenging to detect evolution characteristics of passenger flow. To address this problem, a hybrid short-term metro passenger flow prediction model named decomposition ensemble attention sequence to sequence (DEASeq2Seq) is proposed in this paper. The proposed DEASeq2Seq includes three phases: decomposition, ensemble, and prediction. First, complete empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to decompose the original passenger flow data into several intrinsic mode functions (IMFs) and a residue. Second, recurrence quantification analysis (RQA) is performed to reconstruct the decomposed modes into a stochastic part, a deterministic part, and a trend part via determinism evaluation. Third, a Seq2Seq model with the attention mechanism is proposed to execute multistep prediction for short-term passenger flow and explore the influence mechanism of the reconstructed components on the prediction targets. The real dataset from Chengdu metro, China, is used to verify the proposed model. The experiment results show that the proposed DEASeq2Seq model outperforms the benchmark models. Further model interpretations are conducted to analyze the impacts of decomposition strategy, ensemble strategy, and attention mechanism. [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]

The urbanization of China is changing from incremental growth to urban intensification, and urban compactness is a significant attribute for measuring the intensified degree of regional space and societal frameworks. We carefully outline the inadequacies of compactness measuring methodologies and associated research, and argue that compact cities and metro systems contribute to urban sustainable development in tandem. Thereby, this paper investigated 27 strategic region of metro stations on Chengdu Metro Line 1 and proposed a novel urban compactness model in three-dimensions. The result demonstrates that the old urban area before the development of the metro system and the new town thereafter have extreme compactness outcomes, resulting in significant contrasts in morphology and intensification indices. This study investigates the correlation and interaction processes among compactness, morphology, and intensive multiple variables in order to give theoretical and practical support for the intensification design of these two regions in Chengdu. [ABSTRACT FROM AUTHOR]

This paper studies a vehicle relocation problem for one-way carsharing systems in which vehicles are relocated by temporary workers during the night. The goal is to develop a method for constructing employment plans and work schedules simultaneously to minimize the total relocation cost. An integer programming model is proposed, which incorporates employment planning, return restrictions and ride sharing of temporary workers. An iterative optimization approach is developed to address large-scale rebalancing problems. The unbalanced stations are clustered based on their relocation demands. Considering a new employment limit in each rebalancing model, the optimization procedure iteratively solves a set of cluster relocation problems and an inter-cluster relocation problem until a convergent solution is obtained. Numerical experiments based on data from a carsharing company in Chengdu reveal two interesting findings: (1) Employing temporary workers from understocked stations costs less than that from overstocked stations; (2) Using a time-based salary contributes to 49.1% reduction in the working hours and 33.8% in the relocation costs. • A relocation problem with employment planning and ride sharing of temporary workers. • An integer programming for the worker employment plan and working time schedule. • An iterative optimization algorithm for large-scale overnight relocation problems. • We identify two factors that can lead to a reduction in the cost of the relocation. [ABSTRACT FROM AUTHOR]

• A zonal-based flexible bus service (ZBFBS) is scheduled under dynamic stochastic demand and time-dependent travel time. • Zonal-based time-space network is proposed for flexible bus scheduling with flexibility in arrival and departure times. • A reliability-based gradient solution method with relaxations is developed to solve this inherently non-convex problem. This paper schedules the zonal-based flexible bus service (ZBFBS) considering elastic stochastic demand, stochastic location, time-dependent travel time, and passenger time window constraints based on a scheduled-based formulation. Unlike a traditional time–space network that stipulates the precise arrival and departure times on specific nodes, a zonal-based time–space network is proposed to define the routes in terms of zonal visits of the flexible buses while allowing for flexibility in their arrival and departure times to cater for randomness. The ZBFBS scheduling problem is formulated as a two-stage decision-dependent stochastic problem with recourse. The first stage schedules the zonal visits of flexible buses and the second stage matches each passenger with either flexible bus or ad hoc service, with the latter incurring extra cost to carry the unmatched passengers. To effectively solve the problem, a state-augmented network, that integrates time and zone, is proposed to reduce the number of variables. Moreover, relaxation formulations based on vehicle types and routes are introduced, with an insertion heuristic implemented for vehicle scheduling. The problem is solved by a gradient-based solution approach. Numerical studies demonstrate the efficiency and quality of the solution methods under a variety of ride requests, as well as its advantage over the frequency-based approach in substantially reducing the ad hoc service cost. The applicability of the model is validated by solving an instance of Chengdu, China, with real data. [ABSTRACT FROM AUTHOR]

The accurate prediction of fuel cell voltage is critical for fuel cell health management, which directly affects the performance and cost of fuel cell vehicles. This paper proposes analysis and prediction approaches for the start-stop voltage of fuel cell based on data from a proton exchange membrane fuel cell (PEMFC) bus operated under real traffic conditions in Chengdu, China. First, three correlative analysis methods are used to identify the correlation between the PEMFC voltage and other variables during start-stop operation. Then, the original sequence of PEMFC voltage at start-stop is processed using the grey model, residual grey model, logarithmic-power function grey model, and cotangent function grey model. Four grey prediction models are combined and optimized based on a backpropagation neural network (BPNN). Finally, an organic grey BPNN model (OGNNM) is established to predict the PEMFC voltage during start-stop operation and analyze its accuracy. Results indicate that the PEMFC voltage changes sharply during start-stop, which is closely connected to the current, hydrogen gas tank pressure, and PEMFC stack temperature. In addition, the proposed OGNNM has higher prediction precision than the other data-driven models that provides a basis for the control of fuel cell hybrid systems. • Start-stop voltage of the PEMFC bus is researched at real-world driving conditions. • Correlation between voltage and other varieties at start-stop is analyzed. • The logarithmic-power and cotangent function improve smoothness of the original series. • An organic grey BP neural network is used to predict PEMFC start-stop voltage. [ABSTRACT FROM AUTHOR]

Sequential coupling of applications is the major cause of traffic congestion and low application quality. Modeling traffic congestion under sequential coupling applications is the primary means to explore congestion mechanism, which can further support application design and adjustment to improve application quality. Existing congestion models concentrate more on traffic flow modeling based on network components, with little consideration of the influence of network application and its coupling. This paper proposes a network-cell-based congestion model for describing dynamics of congestion propagation under sequential coupling multi-applications. First, the basic characteristics of network applications are analyzed, and network applications are abstracted into network cells as functional units, which is specifically described from three perspectives: Structure, function and connection. The dynamic process of network can be modeled as intracellular reaction (single application function behavior) and intercellular interaction (coupling behavior of multi-applications). Furthermore, a network-cell-based metric to evaluate application quality is presented, and the implementation algorithm of the model is also given. Numerical simulations are implemented with comparison of typical information flow model to verify the model. Moreover, the proposed model is applied to Chengdu Metro system case to show the applicability. Furthermore, the influence of sequential coupling on congestion is further investigated, and the results show that the larger coupling strength corresponds to less congestion in peak period. This study provides guidance for application design and management to improve application quality of transportation networks. • The impact of sequential coupling on traffic congestion is investigated. • Sequential coupling applications are modeled as network cells and their connections. • A network-cell-based metric to evaluate application quality is presented. • The proposed method is applied to modeling congestion of real metro systems. [ABSTRACT FROM AUTHOR]

The congestion propagation pattern in urban traffic networks has long been viewed as a complex question. Inspired by the fact that virus spread patterns are similar to traffic congestion propagation in some ways, this paper proposes a congestion propagation model (SIS-CP) to describe the congestion propagation patterns in large-scale traffic networks with very few parameters. In this SIS-CP model, the attribute of the real traffic network in the actual operation process is considered, a method based on Hilbert–Huang transform (HHT-based) is proposed to determine traffic states of links, and the congestion propagation patterns are analyzed. The real-world data of 'DiDi Chuxing GAIA Open Dataset Initiative of Chengdu city from March to June is used to calibrate and validate the proposed models. The results show that the basic and limit value of congestion links' ratio do exist in real traffic networks; the relationship between congestion links' ratio and its derivative is conformed to the parabolic function; the HHT-based method can determine traffic states of links accurately; and the SIS-CP model can describe the congestion propagation patterns correctly. [ABSTRACT FROM AUTHOR]

This paper examines the recent green turn in China by investigating a large-scale urban greenway project. Using the perspective of the socio-ecological fix, we demonstrate that multi-scalar states strive to upgrade environmental quality. Specifically, the local state seizes the opportunity for 'ecological civilisation' envisioned by the central state to carry out green infrastructure development. We reveal complex motivations to incorporate ecological changes into entrepreneurial urban governance instead of encroaching greenspace for economic growth. Our state-centred analysis reveals that such an environmental strategy, the making of Chinese green urbanism, is promoted like a political mission, despite its operation by the development corporation. We argue that, while the socio-ecological fix facilitates capital accumulation, its deployment must be understood through state politics and actors. [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]

• We identify the relationship between bike sharing and public transit (PT). • We estimate the environmental impacts of bikeshare as it also substitutes PT. • 27.9% of bikeshare trips substitute buses and 8.1% substitute subways. • Bikeshare has significant environmental benefits by replacing other modes. • Environmental benefits are overestimated if not considering the substitution for PT. Bike sharing has the potential to benefit the environment with less energy consumption and CO 2 emissions. Most previous studies on this topic assumed that bikeshare trips mainly substitute cars. However, bikeshare also replaces ecologically friendly transportation modes like public transit (PT). Therefore, it is vital to examine bikeshare's modal shift from PT to fully assess its environmental impacts. This paper examines the bikeshare and PT relationships in Chengdu,and environmental effects of bike sharing under two cases based on this relationship. The first case only considers the substitution of bikeshare for car and walking, and 19,964.31 L of fuel and 4125.13 kg of CO 2 can be saved; while the second case also considers its substitution for buses and subways, and 13,198.68 L of fuel and 2564.31 kg of CO 2 can be saved. Results indicate that the environmental benefits of bike sharing may be overestimated if not considering its substitution for PT. [ABSTRACT FROM AUTHOR]

• Introduces the concept of the Near-on-Demand Mobility service. • Proposes algorithms for large-scale request assignment and vehicle rebalancing. • Assesses the impact of design aspects on its performance using data from China and United States. • Develops a simulation platform facilitating the evaluation of a wide range of TNC designs. Mobility-On-Demand (MoD) services have been transforming the urban mobility ecosystem. However, they raise a lot of concerns for their impact on congestion, Vehicle Miles Traveled (VMT), and competition with transit. There are also questions about their long-term survival because of inherent inefficiencies in their operations. Considering the popularity of the MoD services, increasing ride-pooling is an important means to address these concerns. Shareability depends not only on riders' attitudes and preferences but also on operating models deployed by providers. The paper introduces an advance requests operating model for ride pooling, users may request rides in advance of their desired departure times. A platform with efficient algorithms for request matching, vehicle routing, rebalancing, and flexible user preferences is developed. Data from Chengdu, China, and New York, United States are used to evaluate the performance of the advance requests system. The impact of various design aspects of the system (e.g. advance requests horizon, vehicle capacity) on its performance is investigated. The sensitivity of the results to user preferences in terms of the level of service (time to be served and excess trip time), willingness to share and place requests in advance, and traffic conditions are explored. The results suggest that significant benefits in terms of sustainability, level of service, and fleet utilization can be realized when advance requests are along with an increased willingness to share. Furthermore, even "near-on-demand" (relative short planning horizons) operations can offer benefits for all stakeholders (users, operators, cities). [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]

• Formulated CA models for designing shared bikes and transit in corridors. • The model admits spatially non-uniform demand and their choices of multiple routes. • Obtained analytic optimal conditions that yield new insights and promise an efficient solution algorithm. • The hybrid system outperforms single-mode transit system in a wide range. The prevalence of shared bikes in many cities is a double-edged sword to the local mass transit services. Some transit patrons may switch to cycling, causing declines in the transit ridership. Others may benefit from using bikes instead of walking to access transit. The complicated interactions between the two modes entail the necessity of designing them jointly. Unfortunately, the literature has focused on either the design of a single mode or joint designs idealized by assuming a uniform demand pattern. The latter class of works has limited practical values. This paper develops a continuum approximation (CA) model for optimizing the hybrid design of shared-bike and transit services in a corridor under spatially heterogeneous demand patterns. The model minimizes the generalized system cost considering various route options that patrons can choose from, including transit routes with walking or biking access and bike-only routes. The methodological challenges that arise due to patrons' route choices in the heterogeneous operating environment are overcome by incorporating a route assignment model into the CA modeling framework. We propose a bi-level algorithm to solve the model, where the upper level optimizes the hybrid design by exploiting some analytical properties of the CA model, and the lower level calculates the route assignment equilibrium. Numerical experiments show that the optimal hybrid design outperforms the conventional transit design for a wide range of operating conditions. The cost saving can be over 20%. Under certain conditions, the hybrid design can even reduce the total operating cost, making bike-sharing profitable for transit agencies. The practical applicability of our model is demonstrated via a case study of a real bus line in Chengdu, China. [ABSTRACT FROM AUTHOR]

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]