GeoTraPredict: A machine learning system of web spatio-temporal traffic flow

Abstract

Traffic flow prediction is an important component for self-driving. Traffic flow is closely related to population distribution, and the traffic flow is not only related to the absolute number of human population but also to their concerns and interests. Accurate spatio-temporal web traffic flow prediction is critical in many applications, such as bandwidth allocation, anomaly detection, congestion control and admission control. Most existing traffic flow prediction methods use models based on time-series analysis and remain inadequate for many real-world applications. Web traffic flow is found to be strongly associated with the spatio-temporal distribution of the population. Increasingly, it is critical to understand and make decisions based on the relationship between population patterns and web traffic flow patterns. It has been proven that different people have different responses to web events. Due to the complexity of spatial data structures and the huge volume of web traffic flow log data, it is difficult to routinely find the relationship between web events and population distributions without an appropriate processing framework. In this paper, we propose an innovative framework named GeoTrafficPredict to support the accurate spatio-temporal prediction of web traffic flow. GeoTrafficPredict provides a machine learning platform to learn the spatio-temporal pattern of traffic flow and use the pattern to predict the trend in both spatial and temporal dimension. Also, GeoTrafficPredict provide data aggregation portal and cloud-based computation function. GeoTrafficPredict deploys a series of computational images in a cloud computing environment, and the implementation on China’s CSTNET illustrates the performance of our platform.