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.

翻译：我们提议了一个短期交通流量预测框架,以便运输当局尽早采取行动控制流量和防止拥堵,我们预计今后在基于历史流量数据以及连接车辆技术提供的实时反馈和轨迹数据等创新特征的基础上,目标路段将在未来的时间框架内流动。为了应对现有办法不适应交通差异这一事实,我们展示了这种新颖办法如何通过将CV实际遇到的各种活动对其轨道各部分的影响纳入流量预测而实现先进的建模。我们用深神经网络(DNNN)在多任务学习环境中解决了STP问题。结果显示,我们的方法,即MTL-CV,即平均的根-海洋误差(RMSE)为0.052,它比ARIMA最新时间序列(0.255的RMSE)和基线分类(0.122的RMSE)。与人工神经网络(ANN)的单项任务学习相比,ANNNE在多任务学习环境中的学习中表现较低,而MS-C的成绩为0.113,因为MS-MS-RMS-S-RMS-S-C在历史相似度指标中可能比MMMS-RMS-S-RMS-S-S-S-S-S-S-S-S-S-S-C在历史相似性平比SMMS-RDMS-S-S-S-RDMS-S-S-S-S-S-S-SMS-S-S-S-S-S-S-SDS-S-S-S-S-S-S-SMSDSDSDSDS-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-C-SDMS-C/C/C/C/C-C-C-C-C-C-C-C-C/C/C/C/C/C/C/C-C-C/C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-