Xiaoqi Duan

1. Network-wide traffic forecasting supports routing, control, and operational risk management. The field has progressed from seasonal statistical models (Williams and Hoel 2003) to nonlinear spati...

Land use and land cover change (LULC) is a critical catalyst for global climate patterns, environmental conditions, and ecological dynamics. Remote sensing and geographic information system (GIS) methods have accelerated research on the impacts and variability of climate change. In ecologically sensitive karst regions, LULC poses significant challenges to sustainable urbanization. As a representative karst mountain city in China, Guiyang has undergone rapid spatial transformation, yet quantitative studies on its long-term LULC trajectories within an integrated spatial modeling framework remain insufficient. This study analyzed LULC dynamics in Guiyang from 2007 to 2022 and projected changes for 2027, 2032, 2037, and 2042. Using the CA-ANN model within the QGIS MOLUSCE plugin, we calibrated the model with multi-temporal LULC data and nine spatial drivers, including topographic, proximity, and socioeconomic factors. The model structure was optimized through iterative testing, resulting in a final configuration of 8 hidden layers and 500 iterations. This setup achieved high validation accuracy during training, with a hindcast simulation overall accuracy of 84.42% and a Kappa coefficient of 0.73 for simulating the 2022 land cover. Future projections indicate that impervious surfaces will continue to expand in a spatially constrained manner, reaching 332.82 km² by 2042, while shrubland area will sharply decrease to 10.75 km². Cultivated land and forest areas show relative stability with fluctuations. The projected patterns may exacerbate risks associated with surface runoff and ecological fragmentation due to established linkages between land use/cover change and ecosystem services. Through spatially explicit, multi-temporal scenario simulations, the findings underscore the urgent need in Guiyang’s unique karst setting to deeply integrate land-use planning with ecological conservation strategies, so as to strengthen regional ecological resilience.

1. Rapid urbanization has accelerated growth in vehicle ownership and travel demand, outpacing the development of transportation infrastructure. This disparity between supply and demand severely co...

Accurate and reliable precipitation prediction remains a significant challenge due to an incomplete understanding of regional meteorological dynamics and limitations in forecasting routine weather events. To overcome these challenges, we propose a novel model, DC-CNN-BiLSTM, which integrates a dilation causal convolutional neural network (DC-CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network. The DC-CNN component, by fusing causal and dilated convolutions, extracts multi-scale spatial features from time series data. In parallel, the BiLSTM module leverages bidirectional memory cells to capture long-term temporal dependencies. This integrated approach effectively links localized meteorological inputs with broader hydrological responses. Experimental evaluation demonstrates that the DC-CNN-BiLSTM model significantly outperforms traditional models. Specifically, the model improves the Root Mean Square Error (RMSE) by 9.05% compared to ConvLSTM and by 32.3% compared to ConvGRU, particularly in forecasting medium- to long-term precipitation. In conclusion, our results validate the benefits of incorporating advanced spatio-temporal feature extraction techniques for precipitation forecasting, ultimately improving disaster preparedness and resource management.

The elaborate description of passenger travel profiles is of significant importance in urban planning, socioeconomic structural design, and individual travel preference analysis. Traditional models often lack consideration of personalized features and exhibit suboptimal performance in constructing spatiotemporal dependencies. To address these issues, this paper proposes a method that integrates spatiotemporal information with travel-related information and employs generative adversarial networks (GANs) for adversarial training. This method accurately fits the true distribution of user travel data, thereby providing detailed profiles of public transportation passengers’ travel behavior. Specifically, the proposed approach considers the complete travel chain of individuals, establishes a spatiotemporal constraint representation model, and utilizes GANs to simulate the distribution of passenger travel, obtaining more compact and high-level travel vector features. The empirical results demonstrate that the proposed method accurately captures passengers’ travel patterns in both the temporal and spatial dimensions, offering technical support for urban transportation planning.

Crowdsourced localization using geo-spatial big data has become an effective approach for constructing smart-city-based location services with the fast growing number of Internet of Things terminals. This paper presents a self-calibrated multi-floor indoor positioning framework using a combination of Wi-Fi ranging, crowdsourced fingerprinting and low-cost sensors (SM-WRFS). The localization parameters, such as heading and altitude biases, step-length scale factor, and Wi-Fi ranging bias are autonomously calibrated to provide a more accurate forward 3D localization performance. In addition, the backward smoothing algorithm and a novel deep-learning model are applied in order to construct an autonomous and efficient crowdsourced Wi-Fi fingerprinting database using the detected quick response (QR) code-based landmarks. Finally, the adaptive extended Kalman filter is adopted to combine the corresponding location sources using different integration models to provide a precise multi-source fusion based multi-floor indoor localization performance. The real-world experiments demonstrate that the presented SM-WRFS is proven to realize precise 3D indoor positioning under different environments, and the meter-level positioning accuracy can be acquired in Wi-Fi ranging supported indoor areas.