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Taxi Destination Prediction with Deep Spatial-Temporal Features

机译：出租车目的地预测，具有深度空间 - 时间特征

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摘要

Taxi destination prediction can grasp the flow direction of the taxi, facilitate the taxi dispatches. The existing methods mostly use the original features of the trajectory to predict and ignore the spatio-temporal features behind the original features, resulting in lack of spatio-temporal information of the trajectory. To address the above problem, we propose a taxi Destination Prediction method with Deep Spatial-Temporal features (DPDST). Firstly, we take advantage of sliding window to calculate the high-level features based on the speed and the turning rate. Secondly, we pass the auto-encoder to learn deep spatial-temporal features from high-level features of the trajectory. Finally, obtained deep spatial-temporal features and original features are combined as input to LSTM (Long Short-Term Memory Network)-based model. Experiments demonstrate that the accuracy is 9% and 7% higher than traditional RNN and LSTM model, and the average distance error is reduced by 1.3km and 1km respectively.
机译：出租车目的地预测可以掌握出租车的流动方向，促进出租车派遣。现有方法主要使用轨迹的原始特征来预测和忽略原始特征背后的时空特征，从而缺乏轨迹的时空信息。为了解决上述问题，我们提出了一种具有深空间特征（DPDST）的出租车目的地预测方法。首先，我们利用滑动窗口来计算基于速度和转速的高级功能。其次，我们通过自动编码器来了解轨迹的高级功能的深空间特征。最后，获得了深度的空间 - 时间特征和原始特征将作为基于LSTM的输入（长短期存储器网络）的模型组合。实验表明，比传统的RNN和LSTM模型高度为9％，7％，平均距离误差分别减少1.3km和1km。

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