Traffic flow forecasting is an important part of intelligent transportation systems. Accurate traffic flow forecasting can not only provide travel advice for people, but also improve traffic management efficiency. In this paper, we propose a novel traffic flow prediction model based on sparse regression, which can effectively and conveniently combine spatio-temporal factor. We analyze the traffic flow data, and design a multi-factor dictionary with spatio-temporal factor. Temporal factor and spatial factor are added to the multi-factor dictionary in the form of basis vectors which make the model easy to expand. The multi-factor coefficient solution based on LASSO guarantees the sparsity of the coefficients, which can quantitatively analyze the influence of spatio-temporal factor. Experimental results in urban and suburban scenarios indicate that the prediction accuracy is improved after adding the temporal factor and spatial factor, and traffic flow on urban roads is more susceptible to spatial factors. We compared our method with other models in three different prediction intervals, and the experimental results show that our method achieved the best prediction performance.

Earthquake Early Warning (EEW) system detects earthquakes and sends an early warning to areas likely to be affected, which plays a significant role in reducing earthquake damage. In recent years, as with the widespread distribution of smartphones, as well as their powerful computing ability and advanced built-in sensors, a new interdisciplinary research method of smartphone-based earthquake early warning has emerged. Smartphones-based earthquake early warning system applies signal processing techniques and machine learning algorithms to the sensor data recorded by smartphones for better monitoring earthquakes. But it is challenging to collect abundant phone-recorded seismic data for training related machine learning models and selecting appropriate features for these models. One alternative way to solve this problem is to transform the data recorded by seismic networks into phone-quality data. In this paper, we propose such a transformation method by learning the differences between the data recorded by seismic networks and smartphones, in two scenarios: phone fixed and free located on tables, respectively. By doing this, we can easily generate abundant phone-quality earthquake data to train machine learning models used in EEW systems. We evaluate our transformation method by conducting various experiments, and our method performs much better than existing methods. Furthermore, we set up a case study where we use the transformed records to train machine learning models for earthquake intensity prediction. The results show that the model trained by using our transformed data produces superior performance, suggesting that our transformation method is useful for smartphone-based earthquake early warning.