考虑注意力和时空特征深度学习的网约车行程时间预测

行程时间预测做为智能交通领域中重要的组成部分,在道路导航、乘客出行过程中起着重要的作用。现有方法很少考虑到交通拥堵变化所产生的影响。本文提出了一种基于注意力机制的时空特征深度学习模型,模型通过卷积神经网络去学习行程过程中所花费的时间和距离以及交通拥堵状态信息,通过注意力机制从通道和空间上两个角度去捕获影响行程中路段通行时间的异常信息,采用双层的长短时记忆网络去学习行程中的路段序列信息,最后通过多任务的学习机制从路径和路段两个角度出发去预测路径通行时间。本文提出的方法与DEEPTRAVEL模型相比,预测精度在平均绝对误差(MAE)和平均绝对百分比误差(MAPE)分别提升了8.23%和20.79%。

Ride-hailing travel time prediction considering deep learning of attention and spatio-temporal characteristics

Travel time prediction is an important part in the field of intelligent transportation, which plays an important role in road navigation and passenger travel. The existing methods seldom consider the impact by traffic congestion changes. This paper proposes a spatio-temporal characteristic deep learning model with attention mechanism. The model study travel time anomalies caused by traffic congestion changes during trips through convolutional neural networks, and use the attention mechanism to capture the information that affects the travel time and distance in the journey from two perspectives of channel and space. a two-layer long short time memory network that combined with the above information was used to learn the road sequence information in the trip, and finally a multi-task learning mechanism was used to predict the travel time from the perspective of the path and the road segment. The prediction accuracy of the method proposed in this paper increased by 8.23% and 20.79% in average absolute error (MAE) and average absolute percentage error (MAPE) compared with DEEPTRAVEL model respectively.