With the rapid development of global positioning technologies and the pervasiveness of intelligent mobile terminals, trajectory data have shown a sharp growth trend both in terms of data volume and coverage. In recent years, increasing numbers of LBS (location based service) applications have provided us with trajectory data services such as traffic flow statistics and user behavior pattern analyses. However, the storage and query efficiency of massive trajectory data are increasingly creating a bottleneck for these applications, especially for large-scale spatiotemporal query scenarios. To solve this problem, we propose a new spatiotemporal indexing method to improve the query efficiency of massive trajectory data. First, the method extends the GeoSOT spatial partitioning scheme to the time dimension and forms a global space–time subdivision scheme. Second, a novel multilevel spatiotemporal grid index, called the GeoSOT ST-index, was constructed to organize trajectory data hierarchically. Finally, a spatiotemporal range query processing method is proposed based on the index. We implement and evaluate the index in MongoDB. By comparing the range query efficiency and scalability of our index with those of the other two space–time composite indexes, we found that our approach improves query efficiency levels by approximately 40% and has better scalability under different data volumes.

Forecasting the motion of surrounding vehicles is necessary for an autonomous driving system applied in complex traffic. Trajectory prediction helps vehicles make more sensible decisions, which provides vehicles with foresight. However, traditional models consider the trajectory prediction as a simple sequence prediction task. The ignorance of inter-vehicle interaction and environment influence degrades these models in real-world datasets. To address this issue, we propose a novel Dynamic and Static Context-aware Attention Network named DSCAN in this paper. The DSCAN utilizes an attention mechanism to dynamically decide which surrounding vehicles are more important at the moment. We also equip the DSCAN with a constraint network to consider the static environment information. We conducted a series of experiments on a real-world dataset, and the experimental results demonstrated the effectiveness of our model. Moreover, the present study suggests that the attention mechanism and static constraints enhance the prediction results.

With the increasing use and complexity of urban natural gas pipelines, the occurrence of accidents owing to leakage, fire, explosion, etc., has increased. Based on Quantitative Risk Analysis (QRA) models and Geographic Information System (GIS) technology, we put forward a quantitative risk simulation model for urban natural gas pipeline, combining with a multi-level grid-based pre-warning model. We develop a simulation and pre-warning model named QRA-Grid, conducting fire and explosion risk assessment, presenting the risk by using a grid map. Experiments show that by using the proposed method, we can develop a fire and explosion accident pre-warning model for gas pipelines, and effectively predict areas in which accidents will happen. As a result, we can make a focused and forceful policy in areas which have some potential defects in advance, and even carry out urban planning once more, rebuilding it to prevent the risk.

Address geolocation aims to associate address texts to the geographic locations. In China, due to the increasing demand for LBS applications such as take-out services and express delivery, automatically geolocating the unstructured address information is the key issue that needs to be solved first. Recently, a few approaches have been proposed to automate the address geolocation by directly predicting geographic coordinates. However, such point-based methods ignore the hierarchy information in addresses which may cause poor geolocation performance. In this paper, we propose a hierarchical region-based approach for geolocating Chinese addresses. We model the address geolocation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a textual address, and the output sequence is a GeoSOT grid code which exactly represents multi-level regions covered by the address. A novel coarse-to-fine model, which combines BERT and LSTM, is designed to learn the task. The experimental results demonstrate that our model correctly understands the Chinese addresses and achieves the highest geolocation accuracy among all the baselines.