Volunteer activity often undergoes unique transformations with the constant changes in society. The information behind volunteer data was created to enhance public welfare efficiently and boost governmental organization productivity. This research aims to utilize public transit systems for volunteer services, reducing inequality in volunteer service provision across different regions and improving overall service efficiency. We collected and processed large-scale data related to public transit and volunteer services, conducting in-depth analysis using data mining techniques and deep learning methods. Through LDA, we annotated a large amount of volunteer data, and via data analysis, discovered patterns related to population distribution, spatial distribution, and temporal distribution. Combining public transit data and the mined features, we propose a novel spatio-temporal embedding model based on the transformer architecture, which can effectively classify and predict the matching between volunteer service demands and public transit systems. Studying the coupling between volunteer services and transportation systems helps establish a new data-driven mindset, better utilize urban resources, and provide high-quality volunteer services to the public.

In order to reduce the measurement error of low cost sensor in the real-time mobile sensing network, rendezvous calibration mechanism is widely used. To tackle the sparsity of reference data and the lack of calibration opportunities, we propose ST-ICM: a Spatial-Temporal Inference Calibration Model based on Gaussian Process Regression, assisting the calibration task by creating more calibration grids in both spatial and temporal dimensions. By using the GPR, the inferred grids generated by ST-ICM are associated with various confidence levels. Based on this property, we propose to make use of a hyperparameter, i.e., variance threshold, to balance the tradeoff between the quantity and quality of the inferred grids. Specifically, only the grids with variances below the threshold will be employed. We conducted experiments using a real-world dataset collected in Nanjing, China, to evaluate the performance of the proposed ST-ICM. The experimenal results show that our model achieves 24% improvement on error calibration compared to the baseline.