Passengers travel in transport networks with diverse interests represented by linked points of interest (POIs) and drive urban regions to group into network communities. Previous studies focused on applying community detection methods (CDM) to discover spatial mobility patterns or using POIs to explain the decision making of human mobility, without comparing the effectiveness of CDMs for detecting network communities. In this paper, we analyze the relationship between POIs and network communities of human mobility over diverse CDMs. Taking the taxi systems of Shanghai and Beijing as case studies, we construct transport networks with urban regions as nodes and the connections between them as links weighted by mobility flows. The spatial communities are identified based on the movement strength among regions. POIs are mapped into nodes in the network and are considered as independent variables for classifying the spatial community categories. Our study suggests that communities detected with two specific CMDs (namely, the Combo algorithm and the Walktrap algorithm) correlate to POIs, and the correlation of the Combo is the best (R2 = 0.3 for Shanghai, and R2 = 0.48 for Beijing). In this regard, this study can provide valuable insight into understanding the formation of spatial communities and assist in selecting reasonable CDMs.

Traffic congestion has gradually become a focal issue in people’s daily life. When the traffic flow on a road segment exceeds its actual capacity, congestion takes place. During rush hours, a congested road segment must carry heavy loads for a long time and is very likely to spread traffic congestion to this road’s adjacent segments via the spatial structure of the road. The new infected road segments continue propagating congestion in the same way. In this paper, we attempt to model the congestion propagation phenomenon with a Space-Temporal Congestion Subgraph (STCS). To this end, we detect each segment regardless of whether it is congested during consecutive time intervals and build the connection of two segments in terms of their spatio-temporal properties. Due to the sparseness of the trajectory data, two strategies of filling missing congestion edges from both temporal and spatial viewpoints are also proposed. Since STCSes are constructed from the same time interval over different days, we design a specific algorithm to discover the frequent congestion subgraphs. Finally, we evaluate the solution on Shanghai taxicab data and the corresponding road network. The experiment shows that the frequent congestion subgraph can reveal an urban congestion propagation pattern.

Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With accurate EV station availability prediction, suitable charging behaviors can be scheduled in advance to relieve range anxiety. Many existing deep learning methods have been proposed to address this issue; however, due to the complex road network structure and complex external factors, such as points of interest (POIs) and weather effects, many commonly used algorithms can only extract the historical usage information and do not consider the comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatiotemporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both the external and internal spatiotemporal dependence of relevant transportation data. The external factors are modeled as dynamic attributes by the attributeaugmented encoder for training. The AST-GIN model was tested on the data collected in Dundee City, and the experimental results showed the effectiveness of our model considering external factors’ influence on various horizon settings compared with other baselines.

Dengue, a mosquito-transmitted viral disease, has posed a public health challenge to Singaporean residents over the years. In 2020, Singapore experienced an unprecedented dengue outbreak. We collected a dataset of geographical dengue clusters reported by the National Environment Agency (NEA) from 15 February to 9 July in 2020, covering the nationwide lockdown associated with Covid-19 during the period from 7 April to 1 June. NEA regularly updates the dengue clusters during which an infected person may be tagged to one cluster based on the most probable infection location (residential apartment or workplace address), which is further matched to fine-grained spatial units with an average coverage of about 1.35 km². Such dengue cluster dataset helps not only reveal the dengue transmission patterns, but also reflect the effects of lockdown on dengue spreading dynamics. The resulting data records are released in simple formats for easy access to facilitate studies on dengue epidemics.

Background/Aims: Islet beta cells (β-cells) are unique cells that play a critical role in glucose homeostasis by secreting insulin in response to increased glucose levels. Voltage-gated ion channels in β-cells, such as K⁺ and Ca²⁺ channels, contribute to insulin secretion. The response of voltage-gated Na⁺ channels (VGSCs) in β-cells to the changes in glucose levels remains unknown. This work aims to determine the role of extracellular glucose on the regulation of VGSC. Methods: The effect of glucose on VGSC currents (I_Na) was investigated in insulin-secreting β-cell line (INS-1) cells of rats using whole-cell patch clamp techniques, and the effects of glucose on insulin content and cell viability were determined using Enzyme-Linked Immunosorbent Assay (ELISA) and Methylthiazolyldiphenyl-tetrazolium Bromide (MTT) assay methods respectively. Results: Our results show that extracellular glucose application can inhibit the peak of I_Na in a concentration-dependent manner. Glucose concentration of 18 mM reduced the amplitude of I_Na, suppressed the I_Na of steady-state activation, shifted the steady-state inactivation curves of I_Na to negative potentials, and prolonged the time course of I_Na recovery from inactivation. Glucose also enhanced the activity-dependent attenuation of I_Na and reduced the fraction of activated channels. Furthermore, 18 mM glucose or low concentration of tetrodotoxin (TTX, a VGSC-specific blocker) partially inhibited the activity of VGSC and also improved insulin synthesis. Conclusion: These results revealed that extracellular glucose application enhances the insulin synthesis in INS-1 cells and the mechanism through the partial inhibition on I_Na channel is involved. Our results innovatively suggest that VGSC plays a vital role in modulating glucose homeostasis.