Applied Sciences

In the Seoul Metropolitan Area of Korea, ongoing urban expansion continuously increases commuting demand toward Seoul, resulting in severe congestion in the urban core due to the large inflow of interregional buses. In response, the government proposed the introduction of a transfer-type interregional bus system as an alternative to alleviate downtown congestion. Transfer-type buses terminate at the Seoul boundary and rely on passenger transfers to other modes for access to the urban core. By shortening route lengths, this system enables reduced headways and increased service frequency. This approach can mitigate urban congestion. However, required transfers may generate user resistance, highlighting the need to analyze users’ willingness to shift. This study applies a latent class mixed logit model to stated preference survey data collected from 502 interregional bus users in order to capture heterogeneous preferences. As a result, users are grouped into three classes: transfer-avoidant, cost-sensitive, and time-sensitive. In all segments, more than half of respondents express a willingness to shift, with the highest level observed in the cost-sensitive group (64.3%). The class-specific choice models reveal that heterogeneity exists not only across segments but also within each segment. These findings indicate that a transfer-type interregional bus policy cannot operate uniformly across all users. Instead, a targeted strategy that simultaneously improves travel time and travel cost for subgroups with conversion potential is required. By systematically identifying users’ willingness to shift and heterogeneous response structures prior to implementation, this study provides empirical evidence to support the design of effective policies and operational strategies for transfer-type interregional buses.

To address sparse meteorological data and the “smoothing effect” over complex terrain, this study proposes a spatiotemporal model based on a Diffusion Graph Convolutional Network (DG model). Focusing on Quanzhou, China, and using 2020–2024 data from 198 stations, the model integrates diffusion graph convolution and residual learning to capture nonlinear meteorological patterns. Ensemble experiments (100 iterations) demonstrate that the DG model significantly outperforms Ordinary Kriging and the KCN baseline in stability and accuracy. Specifically, it improves mountainous temperature prediction by 23.4% (40.0% vs. KCN) through terrain-adaptive weighting, effectively reproducing physical distribution characteristics. Furthermore, the model reduces inherent ERA5 reanalysis bias by integrating historical station data while maintaining background consistency. Validated against spatial-only (OSI) and temporal-only (OTI) variants, the DG model offers a robust approach for high-resolution meteorological reconstruction in complex terrain.

Accurate state estimation is a key technology for improving battery utilization and ensuring operational safety in electric vehicles. The joint estimation of the state of charge (SOC) and the state of power (SOP) over a wide temperature range is therefore essential for intelligent battery management systems. To address modeling uncertainties and estimation accuracy degradation induced by ambient temperature variations, a dual-polarization equivalent circuit thermal model incorporating temperature bias is proposed, and online parameter updating is achieved using the forgetting factor recursive least squares (FFRLS) algorithm. Furthermore, an unscented particle filter (UPF) is constructed by employing the unscented Kalman filter (UKF) as the proposal density function of the particle filter, thereby improving the estimation accuracy and convergence speed of SOC under wide temperature conditions. Based on the coupling relationship between SOC and SOP, a stepwise progressive strategy is then developed to predict the peak power state under multiple constraints, enhancing the robustness of SOP estimation. Simulation and experimental results demonstrate that the proposed method can accurately estimate SOC and SOP under complex operating conditions over a wide temperature range from −5 °C to 45 °C, exhibiting favorable convergence performance and estimation accuracy, which contributes to the safe operation and performance optimization of electric vehicle battery systems.