Data Driven Congestion Trends Prediction of Urban Transportation
Data Driven Congestion Trends Prediction of Urban Transportation
复制标题
数据驱动的城市交通拥堵趋势预测
DOI：
10.1109/jiot.2017.2716114
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发表时间：
2018-04-01
影响因子：
10.6
通讯作者：
Shi, Yuliang
中科院分区：
文献类型：
--
作者：
Jia, Rui;Jiang, Pengcheng;Shi, Yuliang
智能交通预测系统在解决城市交通拥堵方面具有显著的效益。然而，现有的智能交通系统需要大量的实时交通数据和准确的位置信息来显示交通状况。我们希望能够利用这些容易获得的数据，预测出一个可靠的拥塞时间。针对这一问题，本文研究了一种基于SWARIMA模型的智能交通预测系统。该系统包括三个步骤：1）利用滑动窗口对实时数据流进行计算和处理; 2）建立SWARIMA模型并进行回归分析; 3）从统计学角度计算弹性区间并预测拥堵趋势。我们的系统是能够接受的实时交通数据流的拥塞预测，此外，我们减少了实际运行参数的三个属性：1）速度; 2）时间;和3）位置信息。当面临实时交通拥堵的挑战时，该系统能够及时有效地计算出拥堵趋势，并提供3个可靠的弹性区间：1）预警; 2）拥堵; 3）缓解，对改善交通状况、缓解城市道路拥堵具有重要意义。
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.