论文标题
通过连接车辆的交通流量预测
Prediction of Traffic Flow via Connected Vehicles
论文作者
论文摘要
我们提出了一个短期的交通流量预测（STP）框架，以便运输当局采取早期行动来控制流动并防止充血。我们预计，基于历史流数据和创新功能，例如连接车辆（CV）技术提供的实时供稿和轨迹数据，将在目标路段上的未来时间框架流动。为了应对现有方法不适应流量变化的事实，我们展示了这种新颖的方法如何通过集成到流的预测中允许高级建模，这是CV在其轨迹沿其轨迹上现实遇到的各种事件的影响。我们在多任务学习设置中使用深度神经网络（DNN）解决了STP问题，该设置增强了CV的输入。结果表明，我们的方法，即MTL-CV，平均根平方误差（RMSE）为0.052，表现优于最先进的ARIMA时间序列（RMSE为0.255）和基线分类器（RMSE为0.122）。与使用人工神经网络（ANN）的单个任务学习相比，ANN的性能较低，RMSE的0.113比MTL-CV。 MTL-CV学会了细分之间的历史相似之处，与使用该度量的直接历史趋势相比，因为趋势可能不存在，而是在相似之处中存在。
We propose a Short-term Traffic flow Prediction (STP) framework so that transportation authorities take early actions to control flow and prevent congestion. We anticipate flow at future time frames on a target road segment based on historical flow data and innovative features such as real time feeds and trajectory data provided by Connected Vehicles (CV) technology. To cope with the fact that existing approaches do not adapt to variation in traffic, we show how this novel approach allows advanced modelling by integrating into the forecasting of flow, the impact of the various events that CV realistically encountered on segments along their trajectory. We solve the STP problem with a Deep Neural Networks (DNN) in a multitask learning setting augmented by input from CV. Results show that our approach, namely MTL-CV, with an average Root-Mean-Square Error (RMSE) of 0.052, outperforms state-of-the-art ARIMA time series (RMSE of 0.255) and baseline classifiers (RMSE of 0.122). Compared to single task learning with Artificial Neural Network (ANN), ANN had a lower performance, 0.113 for RMSE, than MTL-CV. MTL-CV learned historical similarities between segments, in contrast to using direct historical trends in the measure, because trends may not exist in the measure but do in the similarities.
