Fractal geometry has provided a new perspective for urban road network morphology research. This study systematically verifies and analyzes the spatial heterogeneity of fractal characteristics and influencing factors of urban road networks using spatial analysis. Here, Tokyo Metropolis was selected as a case, and the fractal dimensions of road networks were calculated. To determine the spatial heterogeneity in the relationship between fractal dimensions and influencing factors, we examined the spatial distribution characteristics of fractal dimensions using spatial autocorrelation analysis, selected population, build-up area density, and road network density as the explanatory variables, and established the global regression model and local regression model using ordinary least squares (OLS) and geographically weighted regression (GWR), respectively. The results indicated that the spatial distribution of fractal dimensions of the urban road network exhibited an obvious tendency toward geographical dependency. Considering the spatial heterogeneity in the relationship between the fractal characteristics of the road network and the influencing factors not only improves the reliability of analysis but also helps planners and decision-makers grasp the morphological characteristics of the urban road network and estimate the evolution of the road network, thereby promoting the development of urban road networks in a more orderly, efficient, and sustainable direction.

Road traffic has become one of the major sources of carbon dioxide emissions. However, there remains a lack of consensus on how urban land use influences these emissions. This study investigates the spatial distribution of municipal-level road traffic carbon dioxide emissions and their land use drivers in Japan’s Kanto region. Ordinary least squares (OLS), geographically weighted regression (GWR), and multi-scale geographically weighted regression (MGWR) models are employed to analyze the impacts of residential, industrial, and commercial land use on emissions, as well as to examine spatial heterogeneity in model residuals. The results reveal a clear spatial clustering pattern, with higher emissions in central Tokyo and lower emissions in peripheral areas. The MGWR model demonstrates the best fit and shows no spatial autocorrelation in residuals, highlighting the necessity of incorporating spatial heterogeneity. The MGWR results indicate that all three land use types significantly affect emissions, but their impacts vary spatially. Residential land use has the largest and most spatially heterogeneous effect, making it the most decisive factor. The findings underscore that effective low-carbon transportation policies cannot adopt a one-size-fits-all approach but must be tailored to locally dominant land-use–emission relationships. This study deepens the understanding of the spatial relationship between urban land use structure and road traffic carbon emissions and provides empirical support for sustainable urban development and low-carbon transportation policies.

Understanding how travel contexts are associated with electric vehicle (EV) charging behavior is important for managing expressway charging facilities under variable travel demand. Using event-level data from 446 charging sessions at two expressway service areas in China, this study examines holiday-related differences in charging concerns and observed outcomes through nonparametric comparisons, multinomial logit models, and multivariable regressions. Holiday sessions were 6.97 min shorter on average than non-holiday sessions, whereas charging-start and charging-end state of charge (SOC) showed no significant differences. After covariate adjustment, holiday context was associated with a 42.09-percentage-point higher probability of remaining-distance-related concerns and a 47.78-percentage-point lower probability of battery-level-focused concerns. Remaining-distance concern was associated with a 13.5% shorter conditional mean charging duration, while no concern dimension was significantly associated with charging-start SOC or SOC gain. These findings suggest that travel-context information may complement SOC-based indicators in developing context-responsive charging information services and demand-management strategies.

This study examines how travel context shapes electric vehicle (EV) users’ charging concerns and their correspondence with observed charging behavior at expressway service areas. Event-level data from 446 charging sessions at two service areas in Shandong, China, were analyzed using nonparametric tests, multinomial logit models, and multivariable regressions. Holiday and non-holiday periods differed markedly in user composition, trip conditions, and charging duration, whereas charging-start and charging-end state of charge (SOC) remained comparatively stable. After adjustment for user, vehicle, and trip characteristics, holiday context was strongly associated with a shift from battery-level-focused concerns toward price- and remaining-distance-related concerns. However, concern–behavior correspondence was selective: none of the three concern dimensions was significantly associated with charging-start SOC or SOC gain, while battery-level and remaining-distance concerns were associated with shorter charging durations. The findings support context-responsive charging management and highlight charging duration as a key behavioral manifestation of subjective concerns.