Data Driven Congestion Trends Prediction of Urban Transportation

Abstract

Smart traffic prediction system provides significant benefits in solving the city traffic congestion. However, existing smart transportation system needs a lot of real-time traffic data and accurate location information to display the traffic condition. We hope that we can use the data which is easy to be obtained, and then predict a reliable congestion time. To address this problem, this paper studied a smart traffic forecasting system based on SWARIMA model. The system includes three steps: 1) use the sliding windows to calculate and process real-time data stream; 2) establish the SWARIMA model and make regression analysis; and 3) from a statistical point of view, calculate the elastic interval and predict the congestion trend. Our system is capable of accepting the real-time traffic data stream for the congestion prediction, in addition, we reduce the actual running parameters to three attributes: 1) speed; 2) time; and 3) location information. When faced with the challenges of real-time traffic congestion, the system can timely and effectively calculate the congestion trends and provide three reliable elastic intervals: 1) warning; 2) congestion; and 3) mitigation, which has significance to improve traffic condition and alleviate urban road congestion.