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.

中文翻译：

---

通过联网车辆预测交通流量

我们提出了一个短期交通流量预测 (STP) 框架，以便交通当局采取早期行动来控制流量并防止拥堵。我们根据历史流量数据和创新功能（例如互联车辆 (CV) 技术提供的实时馈送和轨迹数据）预测目标路段未来时间范围内的流量。为了应对现有方法不适应交通变化的事实，我们展示了这种新颖的方法如何通过集成到流量预测中来实现高级建模，CV 实际遇到的各种事件对沿其轨迹的路段的影响。我们在通过 CV 输入增强的多任务学习设置中使用深度神经网络 (DNN) 解决 STP 问题。结果表明，我们的方法，即 MTL-CV，平均均方根误差 (RMSE) 为 0.052，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) 的单任务学习相比，ANN 的性能低于 MTL-CV，RMSE 为 0.113。与在度量中使用直接历史趋势相反，MTL-CV 学习了段之间的历史相似性，因为趋势可能不存在于度量中，但存在于相似性中。