Traffic Speed Prediction Under Non-Recurrent Congestion: Based on LSTM Method and BeiDou Navigation Satellite System Data
Traffic Speed Prediction Under Non-Recurrent Congestion: Based on LSTM Method and BeiDou Navigation Satellite System Data
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DOI：
10.1109/mits.2019.2903431
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发表时间：
2019-03
影响因子：
3.6
通讯作者：
Jiandong Zhao;Yuan Gao;Z.M. Bai;Hao Wang;Shuhan Lu
中科院分区：
文献类型：
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作者：
Jiandong Zhao;Yuan Gao;Z.M. Bai;Hao Wang;Shuhan Lu
充分利用基于位置的车辆传感器数据（LB-VSD）可以提高交通控制和管理的效率。目前，LB-VSD被广泛应用于交通速度的预测。与GPS系统一样，北斗卫星导航系统（BDS）可以收集LB-VSD。在我国，高速公路上的关键运营车辆都配备了BDS，对行驶路径进行监控，为准确预测高速公路上的交通速度提供了依据。本文针对BDS采集到的异常数据，制定了筛选和处理规则，进而提取出交通速度序列。针对设备故障或异常数据剔除造成的数据缺失问题和样本量小造成的数据稀疏问题，提出了一种基于趋势历史数据的数据填充方法。交通流演化是一个复杂的过程。突发事故或恶劣天气可能导致交通流量突然变化和非经常性交通拥堵。传统的机器学习方法在非重现性拥塞发生时预测精度较低。为了解决这一问题，本文采用了一种深度学习模型？长短期记忆（LSTM）预测交通速度。此外，在建立预测模型时，使用了三区算法。并与支持向量回归（SVR）方法进行了比较。结果表明，该方法的预测精度高于支持向量回归算法，在非重现性交通拥堵情况下具有较好的鲁棒性。
The full utilization of Location-Based Vehicle Sensor Data (LB-VSD) can improve the efficiency of traffic control and management. Currently, LB-VSD is widely applied to the prediction of traffic speed. Like the GPS system, BeiDou satellite navigation system (BDS) can collect LB-VSD. In China, the key operation vehicles on the expressway are equipped with BDS to monitor the travel path. This provides a basis for predicting the traffic speed on expressway accurately. In this paper, considering the abnormal data collected by BDS, the screening and processing rules are made, and then the traffic speed sequence is extracted. Considering the data-missing problem caused by equipment failure or abnormal data elimination and the data sparse problem caused by small size of sample, a filling method based on trend-historical data is proposed. Traffic flow evolution is a complex process. Sudden accidents or bad weather can cause a sudden change in traffic flow and non-recurrent traffic congestion. The prediction accuracy of traditional machine learning methods is low when non-recurrent congestion occurred. In order to solve this problem, this paper adopts a deep learning model?Long Short-Term Memory (LSTM) to predict the traffic speed. Moreover, three-regime algorithm is used while building the prediction model. The prediction method is compared with Support Vector Regression (SVR) method. The results show that the prediction accuracy of the proposed method is higher than that of SVR algorithm, and the robustness is better in the case of non-recurrent traffic congestion.