A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting

With the growth of transport modes, high traffic forecasting precision is required in intelligent transportation systems. Most previous works utilize the transformer architecture based on graph neural networks and attention mechanisms to discover spatiotemporal dependencies and dynamic relationships. The correlation information among spatiotemporal sequences, however, has not been thoroughly considered. In this paper, we present two elaborate spatiotemporal representations, spatial correlation information (SCorr) and temporal correlation information (TCorr), among spatiotemporal sequences based on the maximal information coefficient. Using SCorr, we propose a novel correlation information-based spatiotemporal network (CorrSTN), including a dynamic graph neural network component incorporating correlation information into the spatial structure effectively and a multi-head attention component utilizing spatial correlation information to extract dynamic temporal dependencies accurately. Using TCorr, we further explore the correlation pattern among different periodic data and then propose a novel data selection scheme to identify the most relevant data. The experimental results on the highway traffic flow (PEMS07 and PEMS08) and metro crowd flow (HZME inflow and outflow) datasets demonstrate that CorrSTN outperforms the state-of-the-art methods in terms of predictive performance. In particular, on the HZME (outflow) dataset, our model makes significant improvements compared with the latest model ASTGNN by 12.7%, 14.4% and 27.4% in the metrics of MAE, RMSE and MAPE, respectively.

An Effective Dynamic Spatio-temporal Framework with Multi-Source Information for Traffic Prediction

Traffic prediction is necessary not only for management departments to dispatch vehicles but also for drivers to avoid congested roads. Many traffic forecasting methods based on deep learning have been proposed in recent years, and their main aim is to solve the problem of spatial dependencies and temporal dynamics. In this paper, we propose a useful dynamic model to predict the urban traffic volume by combining fully bidirectional LSTM, the more complex attention mechanism, and the external features, including weather conditions and events. First, we adopt the bidirectional LSTM to obtain temporal dependencies of traffic volume dynamically in each layer, which is different from the hybrid methods combining bidirectional and unidirectional ones; second, we use a more elaborate attention mechanism to learn short-term and long-term periodic temporal dependencies; and finally, we collect the weather conditions and events as the external features to further improve the prediction precision. The experimental results show that the proposed model improves the prediction precision by approximately 3-7 percent on the NYC-Taxi and NYC-Bike datasets compared to the most recently developed method, being a useful tool for the urban traffic prediction.

SQLFlow: A Bridge between SQL and Machine Learning

Industrial AI systems are mostly end-to-end machine learning (ML) workflows. A typical recommendation or business intelligence system includes many online micro-services and offline jobs. We describe SQLFlow for developing such workflows efficiently in SQL. SQL enables developers to write short programs focusing on the purpose (what) and ignoring the procedure (how). Previous database systems extended their SQL dialect to support ML. SQLFlow (https://sqlflow.org/sqlflow ) takes another strategy to work as a bridge over various database systems, including MySQL, Apache Hive, and Alibaba MaxCompute, and ML engines like TensorFlow, XGBoost, and scikit-learn. We extended SQL syntax carefully to make the extension working with various SQL dialects. We implement the extension by inventing a collaborative parsing algorithm. SQLFlow is efficient and expressive to a wide variety of ML techniques -- supervised and unsupervised learning; deep networks and tree models; visual model explanation in addition to training and prediction; data processing and feature extraction in addition to ML. SQLFlow compiles a SQL program into a Kubernetes-native workflow for fault-tolerable execution and on-cloud deployment. Current industrial users include Ant Financial, DiDi, and Alibaba Group.