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Abstract As it is central to sustainable urban development, urban mobility has primarily been scrutinised for its scaling and hierarchical properties. However, traditional analyses frequently overlook spatial directionality, a critical factor in city centre congestion and suburban development. Here, we apply vector computation to unravel the spatial directionality of urban mobility, introducing a two-dimensional anisotropy-centripetality metric. Utilising travel data from 90 million mobile users across 60 Chinese cities, we effectively quantify mobility patterns through this metric, distinguishing between strong monocentric, weak monocentric, and polycentric patterns. Our findings highlight a notable difference: residents in monocentric cities face increasing commuting distances as cities expand, in contrast to the consistent commuting patterns observed in polycentric cities. Notably, mobility anisotropy intensifies in the outskirts of monocentric cities, whereas it remains uniform in polycentric settings. Additionally, centripetality wanes as one moves from the urban core, with a steeper decline observed in polycentric cities. Finally, we reveal that employment attraction strength and commuting distance scaling are key to explaining these divergent urban mobility patterns. These insights are important for shaping effective policies aimed at alleviating congestion and guiding suburban housing development.

This paper critically evaluates the prevailing conceptualisations in transport equity research, emphasising the inadvertent Eurocentrism inherent in many contemporary frameworks. By distinguishing between social equality and social equity, the paper underscores the need for differentiated treatments in varied contexts to truly achieve fairness.Central to this discourse is the assertion that the prevailing Eurocentric models may misrepresent or oversimplify non-Western contexts, particularly in regions with complex societal structures and cultural nuances, such as China. Drawing from China’s experiences, the paper illustrates the potential pitfalls of uncritically adopting Western transport equity theories, revealing how they can lead to significant misunderstandings. These inaccuracies are often perpetuated by a reliance on reductionist methodologies that do not account for the serpentine regional challenges and cultural dynamics unique to non-Western nations. Therefore, I advocate for an evolved, contextually apt approach to transport equity research that is sensitive to cultural and regional intricacies, ensuring a more comprehensive and accurate understanding of transport equity dynamics in global contexts.

Aircraft ducts play an indispensable role in various systems of an aircraft. The regular inspection and maintenance of aircraft ducts are of great significance for preventing potential failures and ensuring the normal operation of the aircraft. Traditional manual inspection methods are costly and inefficient, especially under low-light conditions. To address these issues, we propose a new defect detection model called LESM-YOLO. In this study, we integrate a lighting enhancement module to improve the accuracy and recognition of the model under low-light conditions. Additionally, to reduce the model’s parameter count, we employ space-to-depth convolution, making the model more lightweight and suitable for deployment on edge detection devices. Furthermore, we introduce Mixed Local Channel Attention (MLCA), which balances complexity and accuracy by combining local channel and spatial attention mechanisms, enhancing the overall performance of the model and improving the accuracy and robustness of defect detection. Finally, we compare the proposed model with other existing models to validate the effectiveness of LESM-YOLO. The test results show that our proposed model achieves an mAP of 96.3%, a 5.4% improvement over the original model, while maintaining a detection speed of 138.7, meeting real-time monitoring requirements. The model proposed in this paper provides valuable technical support for the detection of dark defects in aircraft ducts.

Abstract Rural mobility inequality is an important aspect of inequality-focused Sustainable Development Goals. To reduce inequality and promote global sustainable development, more insight is needed into human mobility patterns in rural areas. However, studies on rural human mobility are scarce, limiting our understanding of the spatial and social gaps in rural human mobility and our ability to design policies for social equality and global sustainable development. This study, therefore, explores human mobility patterns in rural China using mobile phone data. Mapping the relative frequency of short-distance trips across rural towns, we observed that geographically peripheral populations tend to have a low percentage of short-distance flows. We further revealed social gaps in mobility by fitting statistical models: as travel distances increased, human movements declined more rapidly among vulnerable groups, including children, older people, women, and low-income people. In addition, we found that people living with low street density, or in rural towns in peripheral cities with long distances to city borders, are more likely to have low intercity movement. Our results show that children, older adults, women, low-income individuals, and geographically peripheral populations in rural areas are mobility-disadvantaged, providing insights for policymakers and rural planners for achieving social equality by targeting the right groups.

In this study, we applied an explainable machine learning technique based on the LightGBM method, a category of gradient boosting decision tree algorithm, to conduct a quantitative radar precipitation estimation and move to understand the underlying reasons for excellent estimations. By introducing 3D grid radar reflectivity data into the LightGBM algorithm, we constructed three LightGBM models, including 2D and 3D LightGBM models. Ten groups of experiments were carried out to compare the performances of the LightGBM models with traditional Z–R relationship methods. To further assess the performances of the LightGBM models, rainfall events with 11,483 total samples during August-September of 2022 were used for statistical analysis, and two heavy rainfall events were specifically chosen for the spatial distribution evaluation. The results from both the statistical analysis and spatial distribution demonstrate that the performance of the LightGBM 3D model with nine points is the best method for quantitative precipitation estimation in this study. Through analyzing the explainability of the LightGBM models from Shapley additive explanations (SHAP) regression values, it can be inferred that the superior performance of the LightGBM 3D model is mainly attributed to its consideration of the rain gauge station attributes, diurnal variation characteristics, and the influence of spatial offset.

The mega-city region was projected as the future outcome of the urbanization process, which is commonly composed of single or multiple main cities and peripheral cities through highly developed transportation networks. Because passenger and freight systems commonly share a physical transport network, the effects of urban morphology changes act on both systems at the same time. Although passenger and freight transport in mega-city regions have been addressed in a growing number of journal publications, an overview of research focusing on both topics together is still lacking. Meanwhile, the potential crossed topics between both systems in mega-city regions were limited in the discussion. This situation may hinder local authorities in the formulation of an appropriate transportation agenda for the promotion of collaboration among the transport system between different cities in mega-city regions, even further aggravate the problem of land-use conflict between city and transport, the unreasonably distributed transport infrastructure, and the excessive allocation of transportation resources, etc. Therefore, it's essential to understand the state-of-the-art in the field of passenger and freight transport in the mega-city regions, and to further analyze the potential crossed topics between the two topics. To this end, this paper adopts a systematic literature review method, covering 468 papers and research works published from 2016 to 2021. The cross-relevance analysis shows that, thus far, only seven crossed topics have covered passenger and freight transport simultaneously, while five potential topics can be further studied. Finally, four research gaps have been identified to which scholars could be encouraged to contribute.

On the evening of 30 April 2021, a severe hailstorm swept across eastern China, causing catastrophic gale and damaging hailstones. This hailstorm event was directly caused by two mesoscale convective systems associated with strong squall lines, with mid-level cold advection from the northeast cold vortex, and strong low-level convergence associated with the low-level vortex and wind shear line. Double nesting of the high-resolution weather research and forecasting model (9–1 km) is utilized to simulate this hailstorm with five microphysics schemes. The radar-based maximum estimated size of hail (MESH) algorithm, differential reflectivity and fractions skill scores were used to quantitatively evaluate the precision. All schemes basically captured the two squall lines that swept through eastern China, although they appeared one or two hours earlier than observation. Particularly, Goddard and Thompson performed better in the MESH swath and fractions skill scores among the five different schemes. However, Thompson most realistically captured the reflectivity pattern, intensity and vertical structure of mesoscale convective systems. Its high-reflectivity column corresponded to the maximum center of the hail mixing ratio within the updraft region, which is consistent with the characteristics of a pulse-type hailstorm in its mature phase.

Understanding the determinants of elderly people’s public transport usage patterns can offer new insights into elderly mobility issues and provide policy implications for planning toward an aging-friendly and sustainable city. However, few studies have examined the impact of the built environment on the trip time of the elderly using big data. Moreover, the elderly’s trip time has been mostly investigated by the multivariate linear regression model (MLR), ignoring the non-linear association between explanatory variables and trip time. Using smart card data from Nanjing in 2019, this study employs a gradient boosting regression trees (GBRT) model to probe into the correlations between the built environment and the elderly’s trip time. The results show that significant non-linear relationships exist between trip time and the selected explanatory variables, which cannot be captured by the MLR model. It suggests that relevant policy implementations should be carried out in conjunction with the elderly’s travel environment by regarding their threshold effects. Besides, interaction effects of spatial attributes on trip time are identified in our study. For example, elderly people living in the exurban area are more likely to take long-distance metro travel for their physical exercise. These findings demonstrate that planners and policymakers should not only consider one single built-environment factor, but also the interactions of various factors to enhance elderly mobility.

Open biomass burning (OBB) has a significant impact on the heavy haze pollution in Northeast China (NEC) in recent years, which requires the investigation of the spatiotemporal variations of OBB with different vegetation types to better monitor and control OBB in NEC. The MODIS C6 fire and land cover products, together with the emissions inventory from the Global Fire Assimilation System, were used in this study. The changes in the total number of MODIS fire points in NEC from 2003 to 2017 demonstrated a fluctuating but generally rising trend, with a peak during 2013–2017. Most fire points concentrated in two key periods, i.e. March–April (37%) and October–November (46%). The total number of crop residue burnings in March–April was basically slightly fluctuating and increased sharply from 2013, whilst the number in October–November had a fluctuating and upward trend until 2015, when a decline appeared. The amount of OBB in March–April was higher than that in October–November during 2016–17. OBB in Heilongjiang Province comprised a major proportion of all fires, which accounted for 70.7% from 2003 to 2017; however, the proportion was only 66.2% during 2013–2017. The largest proportion of all fires was in cropland (90.8%), then forest (5.3%) and grassland (3.1%). The cumulative emissions of fine particulate matter, nitrogen oxides, and ammonia from agricultural open burning in NEC reached 78.43 Gg, 24.9 Gg, and 13.7 Gg for March–April during 2013–17, respectively, which were close to those in October–November.

Understanding the spatiotemporal variation of high-efficiency ride-hailing orders (HROs) is helpful for transportation network companies (TNCs) to balance the income of drivers through reasonable order dispatch, and to alleviate the imbalance between supply and demand by improving the pricing mechanism, so as to promote the sustainable and healthy development of the ride-hailing industry and urban transportation. From the perspective of TNCs for order management, this study investigates the spatiotemporal variation of HROs and common ride-hailing orders (CROs) for ride-hailing services using the trip data of Didi Chuxing in Haikou, China. Ordinary least squares (OLS) and geographically weighted regression (GWR) models are established to examine the factors that affect the densities of HROs and CROs during different time periods, such as morning, evening, afternoon and night, with considering various built environment variables. The OLS models show that factors including road density, average travel time rate, companies and enterprises and transportation facilities have significant impacts on HROs and CROs for most periods. The results of the GWR models are consistent with the global regression results and show the local effects of the built environment on HROs and CROs in different regions.

Due to the coupling of different energy systems, optimization of different energy complementarities, and the realization of the highest overall energy utilization rate and environmental friendliness of the energy system, distributed energy system has become an important way to build a clean and low-carbon energy system. However, the complex topological structure of the system and too many coupling devices bring more uncertain factors to the system which the calculation of the interval power flow of distributed energy system becomes the key problem to be solved urgently. Affine power flow calculation is considered as an important solution to solve uncertain steady power flow problems. In this paper, the distributed energy system coupled with cold, heat, and electricity is taken as the research object, the influence of different uncertain factors such as photovoltaic and wind power output is comprehensively considered, and affine algorithm is adopted to calculate the system power flow of the distributed energy system under high and low load conditions. The results show that the system has larger operating space, more stable bus voltage and more flexible pipeline flow under low load condition than under high load condition. The calculation results of the interval power flow of distributed energy systems can provide theoretical basis and data support for the stability analysis and optimal operation of distributed energy systems.

In order to explore the factors affecting users’ behaviors in a free-floating bike sharing (FFBS) system in China, a survey was conducted in Jiangsu province, China in 2017, and the travel characteristics of FFBS users were analyzed. A binary logistic model was applied to quantify the impact of various variables regarding residents’ usage preference based on 30401 valid questionnaires. The findings show that (1) FFBS was mainly used for short-distance travel in cities, especially for commuting and schooling, and the time period of travel in FFBS coincided with the rush-hour in urban areas; (2) a higher level of education, a higher daily transportation cost, the convenience of picking up and parking, and the contribution to users’ health could promote the usage of FFBS, while malfunctioning bicycles and limited regulations were major obstacles restricting the development of FFBS; (3) interestingly, people with high-incomes rather than those with low-incomes showed an inclination for FFBS owing to the charge mode. This research provides empirical evidence to facilitate the formulation of urban transportation policies and to improve the management of FFBS for the operators.

Governments worldwide have announced stimulus packages to remobilize the labor force after COVID-19 and therefore to cope with the COVID-19-related recession. However, it is still unclear how to facilitate large-scale work resumption. This paper aims to clarify the issue by analyzing the large-scale prefecture-level dataset of human mobility trajectory information for 320 million workers and about 500,000 policy documents in China. We model work resumption as a collective behavioral change due to configurations of capacity, motivation, and policy instruments by using qualitative comparative analysis. We find that the effectiveness of post-COVID-19 recovery stimulus varied across China depending on the fiscal and administrative capacity and the policy motivation of the prefecture. Subnational fiscal and procurement policies were more effective for the wholesale and retail sector and the hotel and catering sector, whereas the manufacturing and business services sectors required more effort regarding employment policies. Due to limited prefectural capacity and wavering policy motivation, the simultaneous adoption of fiscal, employment, and procurement policy interventions endangered post-COVID-19 work resumption. We highlight the necessity of tailored postcrisis recovery strategies based on local fiscal and administrative capacity and the sectoral structure. [ABSTRACT FROM AUTHOR]

Mass volume train security detection network is a real-time and reliable onboard data communication network based on the train level and vehicle level Ethernet network, which will transmit and send mass volume train status and fault diagnosis information, the event log data, rich audio and video data. The network structure and topology is presented, which include all devices which can attach to the network. Then, a delay model of different network topology is put forward, which indicates that the onboard ring network is superior to the linear network topology. Finally, the analysis is confirmed by simulation results. The results indicated that onboard ring topology network will improve control and network function of train, enhance trains' service and maintenance efficiency. [ABSTRACT FROM AUTHOR]