Urban renewal significantly alters the built environment and triggers residential relocation, leading to profound life changes for residents compelled to relocate. Changes in students' travel are a significant consequence of these transformations, but have rarely been thoroughly examined. This study examines the school travel changes in Heze, where China's most intensive shantytown redevelopment started from 2016 to 2018. A community survey of 614 valid questionnaires and traffic simulations were used to analyze school travel patterns, and a generalized linear model was employed to identify factors influencing commuting time. The results indicate that commuting distance and time temporarily increased during the shantytown redevelopment but significantly decreased after resettlement. In addition, after redevelopment, commuting patterns became more efficient, with a notable increase in independent commuting and active commuting modes. Regarding impacting factors, the number of relocations, relocation distances, and duration at temporary residences significantly influenced commuting times, and revealed spatial evolution mechanisms. Additionally, the number of commuting children and family educational backgrounds positively correlated with commuting times, whereas family career backgrounds had a negative correlation. This study highlights the progressive impact of shantytown redevelopment on school travel and provides important insights for policymakers and urban planners to optimize urban renewal strategies.

Shared micromobility has emerged as a sustainable transport mode with the potential to promote mental health through everyday travel. This systematic review synthesizes evidence from 92 peer-reviewed studies to examine multidimensional mental health outcomes, the factors shaping these outcomes, and the pathways common to and unique to different micromobility modes. The review indicates that shared micromobility is generally associated with positive mental health outcomes, particularly psychological flourishing. However, evidence remains limited regarding mental disorders, cognitive health, and emerging modes such as shared e-bikes and e-scooters. These mental-health outcomes are jointly influenced by travel characteristics, user attributes, and built-environment conditions. Compared with non-shared micromobility, shared systems appear to influence mental health through common pathways, including physical activity, positive travel experiences, and social participation, while their distinctive service characteristics further shape accessibility, mobility autonomy, and everyday urban experiences. Building on these findings, this review proposes an integrated pathway framework linking multidimensional mental-health outcomes to travel characteristics, user attributes, and environmental contexts. The framework distinguishes direct evidence specific to shared micromobility from theoretically transferable pathways grounded in common behavioral and environmental exposures, providing a conceptual basis for future research, mental-health-oriented transport planning, and sustainable urban mobility policy.

Recovery experience refers to the process through which individuals achieve stress recovery via emotional responses and engagement in restorative activities. Although urban streets are known to support stress recovery, their specific effects on recovery experience, particularly in cycling-friendly environments amid growing bike-sharing adoption, remain underexplored. This study integrates multi-source data to examine how street environmental features and cycling density influence recovery experience. Using a Bidirectional Encoder Representations from Transformers (BERT) model and machine learning techniques, we systematically assess the impact of various street environmental features on recovery experience. Results indicate that traffic accessibility (e.g., metro and bus station density, intersection density), urban vitality (e.g., social vitality and commercial facility density), and aesthetic qualities (vegetation) significantly enhance recovery experience. Different dimensions of recovery experience exhibit distinct sensitivities: detachment and mastery are more responsive to traffic accessibility, while relaxation is more strongly influenced by street vitality. Specifically, shared-bike facility density exerts a positive effect on recovery experience, and moderate slope likewise contributes positively to mastery. Furthermore, high cycling density amplifies the beneficial influence of the street environments on recovery experience, particularly in terms of traffic accessibility and vitality. These findings highlight the critical role of cycling-friendly street environmental features and active cycling participation in promoting recovery experience. Based on the findings, this research provides evidence-based insights for designing urban streets that enhance sustainable mobility while fostering stress recovery.

Bike-sharing provides a convenient means of transport for short-trip travellers and is considered an efficient way to solve the first-and-last-mile problem. However, existing studies have paid little attention to cross-city travel in the bike-sharing context, or to its potential to reveal the spatial structure of cities. This paper considers two types of cross-city travel behaviour: the first involves a traveller taking a single trip from one city to another (Type I); and the second refers to a traveller making two consecutive trips in two cities, suggesting that a cross-city trip between these two cities has occurred (Type II). Using dockless bike-sharing big data from Zhejiang Province (China), we adopt a network-based approach to examining cross-city travel behaviour and its associated spatial structure. Our results show that it is less common for cyclists to undertake cross-city travel than to only travel within a single city, and that it is more difficult to travel across prefecture-level cities in comparison to county-level cities. Spatial structure varies greatly between different city levels and for different types of cross-city journeys. The county-level cross-city patterns identified 10 spatial communities, some of which show inconsistencies between their community-based and administrative boundaries. This study could help to advance our understanding of the characteristics of cross-city travel behaviour and its associated spatial structure. It also provides some useful implications for policy-making and practical operations.

Extensive research has been conducted on the usage patterns and potential impacts of shared micromobility, yet the distinct relationships with public transit between shared bikes and shared E-bikes - the two main micromobility modes in China - remain unexplored. Examining the potentially distinct modal shift patterns away from public transit is essential to understand the landscape of different micromobility modes and their different disruptions to traditional transportation modes. To bridge this gap, this study analyzed shared micromobility trip data from Ningbo, China, aiming to quantify the relationship between shared micromobility and public transit, and differentiate between the interactions of shared bikes and E-bikes with public transit. We employed a geospatial-based approach to categorize each shared micromobility trip into three types: Modal Substitution (MS), Modal Integration (MI), and Modal Complementation (MC), based on their interactions with buses and subways. Then we explored the spatial and temporal patterns of the shares of MS, MI, and MC trips, and investigated factors influencing these varied relationships using Spatial Autoregressive (SAR) models. Our findings indicate that shared E-bikes more frequently substitute for public transit, whereas shared bikes are predominantly used in MC roles. There are notable temporal and spatial variations in the usage of shared E-bikes and bikes: temporally, there is a morning peak of shared E-bikes that substitute public transit, and spatially, Ebike sharing has a higher concentration of substitution in suburbs while bike sharing has a higher concentration of complementation in the outer areas. The observed differences between E-bikes and bikes regarding their relationship with public transit are largely influenced by trip distance, speed, and public transit characteristics. This study highlights the importance of recognizing the diverse interactions between different shared micromobility modes and public transit, and sheds light on the development and management of shared micromobility and public transit systems.

Shared micro-mobility is experiencing growing global popularity. Previous studies mainly explored the shared bike-associated spatial equity issue faced by the residential population in Western cities, often neglecting shared e-bike systems and the working population. This paper examines the equity performance of both shared bikes and shared e-bikes, and considers both residential and working populations in a Chinese city (i.e., Wenzhou). Utilising an accessibilitybased framework integrating multi-source data like massive user-generated trip and mobile phone data, our results show that shared bikes provide better horizontal equity than e-bikes due to broader fleet availability and distribution. Vertical equity analysis reveals systematic disparities between residential and working populations. Among working populations, individuals aged 19-29 enjoy better access to shared e-bikes, while those aged 50-59 face consistently lower accessibility across both bike types. In contrast, among residents, the 50-59 age group show relatively higher accessibility, whereas younger residents (19-29) encounter limitations, particularly for shared e-bikes. Non-local populations are disadvantaged in both modes, and female users exhibit slightly higher accessibility. This study provides valuable insights into the micromobility equity in Asian urban contexts, offering implications for policy-making and planning to develop sustainable and equitable micro-mobility systems.

Incentive-based strategies tailored to individual preferences can motivate commuters to adopt public transit, potentially easing road congestion and fostering ecofriendly urban travel. However, understanding diverse responses to these incentives has been challenging due to low survey participation and certain homogeneity assumptions, limiting our knowledge of individuals' preferences for using public transit in different cities. To address this, our study employs a latent class choice model and mixed logit model to analyze individual responses to incentives and identify key factors that influence the effectiveness of these incentives. Data for this analysis was sourced from a mobile navigation application, covering 34 cities within China, thereby enabling the analysis of individuals within each latent class to reveal their diverse preferences for using public transit within different cities. Our findings indicate significant individual differences in response to incentives, categorized into three main latent classes: Class 1 individuals exhibit minimal influence from incentives; those in Class 2 demonstrate moderate responsiveness, especially to food and shopping coupons; and Class 3 individuals, whose decisionmaking is significantly affected by education level, gender, and travel mode preference, show a high degree of responsiveness to incentives. These insights are invaluable for policymakers seeking to design more effective, tailored incentive schemes to encourage public transit adoption.

Due to the growing demand for high-quality healthcare and the uneven medical resources distribution, intercity patient mobility has become prevalent. The recent transportation advancements, particularly high-speed rail (HSR), has been proved to facilitate intercity mobility, yet its effects on patient mobility remain unclear. To bridge this gap, this study examines the impacts of HSR on intercity patient mobility using 4 million records of national chronic kidney disease (CKD) hospitalization in China. Based on difference-in-differences models, OLS regression and yearly regression, we find that: (1) HSR implementation increases intercity patient flows by 8.24 ± 3.28%, with 28.51 ± 6.26% more flows when HSR travel speed is twice of conventional rails and 11.8% ± 0.33% more flows when the number of available trains doubles. (2) HSR boosts flows to megacities and large cities by 23.02 ± 11.57% and 7.26 ± 4.26%, respectively, while having no significant effects on small cities. (3) Three years after HSR implementation, the effect in megacities turns negative, while remaining positive and growing in magnitude in large cities. Our findings demonstrate that HSR not only influences the volume of intercity patient mobility but also reshapes its spatial patterns. These insights underscore the need to invest in advanced transportation technologies and to promote coordinated decision-making across healthcare, transportation, and urban development policies.

Transport sector is a major contributor to global carbon emissions. E-bike-sharing (EBS), emerging as the next generation of shared micromobility, offers an eco-friendly alternative to reduce mobility-associated carbon emissions. Based on user-generated EBS data of one week in three consecutive years between 2020 and 2022 in two small Chinese cities (i.e., Yuhuan and Rui'an), this paper aims to quantify trip-specific carbon emissions and then investigate the spatiotemporal dynamic changes of carbon emission reduction patterns. Results show that EBS reduced 29.50 and 49.12 t of carbon emissions over three weeks from 2020 to 2022 in Yuhuan and Rui'an, respectively. Over 95 % of the reduced carbon emissions are from substituting driving trips. The trip-level environmental benefit of EBS in Yuhuan is 306.29-382.64 g of CO2, higher than that in Rui'an (172.21-177.07 g). Carbon reduction patterns are temporally similar to typical travel patterns and show strong spatial auto-correlation. There are more carbon emission reductions in leisure-related places on weekends and education-related places on weekdays. This study informs relevant transport planning and policy-making for improving e-bike-sharing services and supporting sustainable development in cities.

The interdependence of climate change and agricultural land use remains a critical, yet unquantified, area of concern for future food production. Here we determine climate-driven cropland change based on an empirical model of cropland response to changes in agricultural productivity. By estimating counterfactual total factor productivity in a scenario without climate change, we find that 88 million hectares (90

The 15-minute city refers to a city where residents can obtain essential amenities within a 15-minute journey from their homes. Most existing studies discussed the 15-minute city by assuming walking as the default mode, while ignoring the cycling modes (e.g., bike-sharing). With the increasing popularity of bike-sharing worldwide, it is necessary to investigate how this emerging micromobility mode supports the development of 15-minute cities. This study proposes a methodological framework to measure and evaluate the 15-minute city with dockless bike-sharing. The framework is then applied to 41 cities in Zhejiang Province, China. We identify 1,887 spatial units under the walking scenario, 3,295 units under the bike-sharing scenario, and 9,852 units under the private-bike scenario as 15-minute units, and 10.26, 15.58 and 24.56 million residents can access ten types of essential amenities within 15 minutes, respectively. 15-minute units are predominantly located in city centres or built-up areas. Based on the 15-minute unit analysis, bike-sharing effectively enhances residents' accessibility to specific types of essential amenities (e.g., Post Offices), especially among disadvantaged population groups in these cities. The convenience and capacity of identified crucial parking places that support 15-minute cities also vary across cities. Overall, the results show that bike-sharing does support the development of 15-minute cities, and there is still a margin for improvement compared to the optimal scenario. The study provides valuable insights for transport planning and policy-making to develop 15-minute cities.

The carsharing service has experienced significant growth over the past few years in China, yet few studies have scrutinized the multi-city variations of this service. Using carsharing data from 61 cities in China, we analyzed the usage and efficiency of each city and investigated the impact of system and urban factors on the service performance. The study reveals vast differences in carsharing supply and demand across Chinese cities. Our results show the parking station density and the parking lot to vehicle ratio of the carsharing system are positively related to the usage. Urban factors such as public transportation availability, educational attainment levels, and vehicle restriction policies, are found to have significantly positive associations with the carsharing usage. However, no urban factors demonstrate significant associations with the efficiency measured by vehicle utilization rate. Moreover, the presence of other competing carsharing services within a city exhibits a positive impact on the performance of carsharing systems. This study also examined nonlinear effects of the factors. It provides valuable insights into the management of carsharing services in China, which can inform policy-making and operational strategies for sustainable development of carsharing.

Amid rapid urbanization, shantytown redevelopment profoundly transforms urban environments and catalyzes substantial changes in social, economic, and family structures. Yet, most previous studies have focused narrowly on the outcomes of single residential mobility events, overlooking the complex dynamics that unfold across the pre-redevelopment, during-redevelopment, and post-redevelopment phases. This study investigates changes in family structure and their correlation with living space throughout all three phases of shantytown redevelopment in Heze City, Shandong Province, China, from 2016 to 2023. Collecting data through a community survey, we gathered 1035 valid responses to analyze family structure and residential characteristics. Our analysis, which included Sankey diagrams and cross-lagged panel models (CLPM), revealed a predominant trend of large families fragmenting into smaller nuclear units and demonstrated a significant positive correlation between family structure complexity (FSC) and living space. Notably, the FSC from a previous phase had a significant influence on the living space of the following phase, indicating lagged effects where housing choices are influenced by prior family structure. Further comparisons across different housing types and tenures during the temporary phase highlighted diverse structural changes among families. These insights are crucial for policymakers to refine urban redevelopment strategies, better meet residents’ needs, and enhance the efficacy of policies.

Changes in transportation ridership during COVID-19 indicate several important factors, including the need to serve changing spatial and temporal patterns of travel demand, and the equity implications of pandemic impacts across lines of race, age, and income. Various papers have sought to understand changes in transportation ridership during the pandemic, but have been focused solely on a single mode (often public transit), and have been limited to a single data source for analysis. This paper examines and compares the changes in public transit and ride-hailing ridership in Chicago during the COVID-19 pandemic, investigating ‘who’ stopped using transit and Transportation Network Company (TNC) services from a demographic perspective, how remote work relates to changes in transit use, how pandemic ridership changes are clustered in space, and what factors will impact a return to regular travel. Analysis integrates datasets spanning over a year of the pandemic, including aggregate spatial ridership counts that are used to form spatial regression models, and a six-month panel survey that received input from approximately 1,000 Chicago Transit Authority (CTA) riders. Continued transit use is correlated with areas with a greater percentage of African American and Spanish-speaking people, and with a greater percentage of pre-pandemic bus riders and off-peak riders, while peak-period, frequent, and rail system riders stopped using transit to the greatest extent. Areas with a higher share of young, college-educated people, and those with a high walkability metric, generally saw the greatest decreases in TNC use, reflecting a potential loss of trips for those who used TNCs to access social events or employment, and moved to virtual work during COVID-19. Our findings can help to guide transportation service providers and policy-makers in planning service for public safety and a changing demand profile, advancing equity of access to mobility, and anticipating long-term mobility patterns.

Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

The examination of the robustness of transportation networks holds considerable importance in safeguarding network stability in the face of potential threats. However, previous studies have primarily focused on assessing the robustness of individual railway or aviation networks at a single layer for a long-distance traveling. Indeed, the demonstration of the interconnected robustness of a bilayer network that combines railway and aviation systems is approaching a state of actualization. In this study, the robustness of single-layer railway, aviation, and bilayer railway-aviation networks were assessed and compared, taking into account the interaction effects of various transportation modes in response to multiple attack scenarios. Several interesting patterns of bilayer transportation network robustness were discovered, such as the weak robustness to intentional attacks and node removals, the comparable average robustness combined with high heterogeneity against localized attacks, and other findings. Furthermore, the robustness patterns can be effectively explained by the proposed node/edge betweenness indicators.

Active mobility, encompassing walking and cycling for transportation, is a potential solution to health issues arising from inadequate physical activity in modern society. However, the extent of active mobility's impact on individual physical activity levels, and its association with health as mediated by physical activities, is not fully quantified. This study aims to clarify the direct relationship between active mobility usage and individual health, as well as the indirect relationship mediated by physical activity, with a focus on varying levels of physical activity intensity. Utilizing data from the 2017 U.S. National Household Travel Survey (NHTS), we employed Poisson regression to predict active mobility usage based on socio-demographic and household socio-economic characteristics. A Structural Equation Model (SEM) was then used to investigate the direct and indirect effects of active mobility on individual health, mediated by physical activity. We further segmented individuals according to their intensity of physical activity to examine how such effect differs between different levels of physical activity. The study demonstrates that active mobility usage positively correlates with both the amount and intensity of physical activity. The effect of active mobility on individual health includes a direct positive effect (29% for intensity, 67.7% for amount) and an indirect effect mediated by physical activity (71% for intensity, 32.3% for amount). Notably, the mediation effect of active mobility on health is more substantial in the context of vigorous physical activities compared to light or moderate activities. Our findings reveal a significant positive influence of active mobility on individual health, encompassing both direct and indirect effects mediated by physical activities. These results quantitatively underscore the health benefits of active mobility and suggest the importance of promoting active mobility as a strategy to improve public health.

Existing studies on urban renewal have primarily focused on the final effects of urban redevelopment, while often overlooked the social costs incurred during the temporary displacement phase. This gap is significant, as many residents must vacate their homes for an average of 3-5 years during Shantytown redevelopment, which brings about challenges of renting houses and the associated negative impacts on their well-being before returning to their resettled homes. Therefore, this study focuses on examining the temporary residence arising during Shantytown redevelopment while awaiting resettlement. We selected Heze city as our case study area, which has been through China's most intensive redevelopment between 2016 and 2018 that affected about 1.2 million population. A structured community survey was conducted, and 1035 valid samples were collected. We then applied spatiotemporal analysis and the Random Forest model to examine stability, direction, and distance of temporary residence mobility, along with its influencing factors. Findings reveal that 92.4% of households move just once or twice during the temporary phase, indicating the preference for stable residence. Regarding moving direction, households seek life service centers rather than city centers, and prefer familiar community environments. Furthermore, 74.8% of households resettled within 2.5 km of their original residence, indicating a preference for nearby temporary housing. The built environment emerged as the most critical factor influencing the mobility, followed by family socioeconomic status, while housing costs, surprisingly, having the minimal impact. This study highlights the importance of considering the interim social costs in urban renewal projects and provides valuable insights for housing market regulation and urban planning to mitigate these effects.

Shared micro-mobility systems (SMSs) have recently experienced rapid growth, providing new green mobility options to reduce energy use. To better understand SMS's environmental impact, this paper comparatively examines the potential of carbon emission reductions from dockless shared bikes and e-bikes in three Chinese cities at different economic development stages (i.e., Binjiang, Wucheng and Xiangshan) using massive user-generated trips. Results show that shared bikes and e-bikes in the three cities reduced 41.51 and 31.84 tonnes of CO2 emissions over a week, respectively. The trip-level environmental benefit of shared e-bikes is 155.11 g of CO2, higher than that of shared bikes (132.03 g). Most of the reduced carbon emissions are from substituting driving trips, but shared e-bikes even generate extra carbon emissions by substituting public transit and walking. Patterns of carbon emission reductions show spatiotemporal heterogeneity. Spatially, reduced carbon emissions are concentrated in central areas of Wucheng/Xiangshan and more dispersedly distributed in Binjiang, which is a more economically vibrant city. From a temporal perspective, SMS's carbon reduction patterns are similar to typical temporal usage patterns. The Dining and Shopping trips contribute the most to SMS's carbon reductions in all cities. Binjiang has more carbon emissions reduced from the Transfer and Working trips, and shared bike trips with the Home and Schooling purposes reduced relatively more carbon emissions in Wucheng. Our analysis helps us better understand the environmental impact of SMSs across various urban contexts and inform relevant transport planning to achieve carbon neutrality in cities.

Electric vehicles (EVs) have been proposed as a key solution for decarbonizing urban transportation and addressing climate change. As the use of EVs increases in cities worldwide, it may lead to significant transformation in urban development, including changes in the electrical system and people's travel behavior, such as charging preferences and choices of where to live and work. Some questions arise, will the rise of EVs lead to more suburbanization or drive people towards a more compact urban form? Additionally, how can the relationship between EV users' residential locations and new energy infrastructure be best coordinated? A study in the rapidly growing metropolis of Beijing aims to address these questions by combining geo-spatial big data analysis, machine learning, and theories of urban development to understand the relationship between EV users' residential locations and new energy infrastructure. A novel data mining strategy was proposed to identify actual EV users based on location data from smartphones. By analyzing observation data of EV users, the study applies the Gradient Boost Decision Tree model to examine the nonlinear associations between the spatial distribution of EV residents and neighborhood attributes such as employment density, GDP, land use mix, public charging accessibility, building areas, access to public transit, and suburbanization. The results indicate that a higher percentage of EV users prefer to live in areas that are neither too far away from the city center nor too close to it, particularly the threshold effects show that they are concentrated in areas where it has a 10 km distance from the city center. Additionally, the study found that most public charging activities tend to occur within 1.5 km from home, suggesting an optimal threshold for public charging station deployment. The findings of this study can help inform energy management and infrastructure planning at the local, regional, and national levels to promote sustainable urbanization and smarter energy planning in policy-making.