• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 人工智能与数据挖掘 • 上一篇    

出租车目的地预测的深度学习方法

崔淑敏1,2，张磊1,2，李允1,2，邵长兴1,2，朱少杰1,2   

  1. （1.中国矿业大学计算机学院，江苏 徐州 221116;2.矿山数字化教育部工程研究中心，江苏 徐州 221116）
  • 收稿日期:2019-06-28 修回日期:2019-08-16 出版日期:2020-01-25 发布日期:2020-01-25
  • 基金资助:

    中央高校基本科研业务费专项资金(2014XT04)；教育部博士点基金(20110095110010)；江苏省自然科学基金(BK20130208)

A deep learning method for taxi destination prediction

CUI Shu-min1,2,ZHANG Lei1,2,LI Yun1,2,SHAO Chang-xing1,2,ZHU Shao-jie1,2   

  1. （1.School of Computer Science,China University of Mining and Technology,Xuzhou 221116;
    2.Engineering Research Center of Mine Digitization of MOE,Xuzhou 221116,China）

     
  • Received:2019-06-28 Revised:2019-08-16 Online:2020-01-25 Published:2020-01-25

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关键词: 特征提取, 自编码器, LSTM, 轨迹预测

Abstract:

Taxi destination prediction can grasp the flow direction of taxis and facilitate the taxi scheduling. Most existing prediction methods only use the original features of the trajectory sequence as the input of the prediction model and ignore the spatiotemporal data behind the original features, resulting in the lack of spatiotemporal information. To address the above problems, a Deep Learning method for taxi Destination Prediction (DLDP) is proposed. Firstly, the method uses a sliding window to calculate the high-level features of the trajectory base on speed and turning rate. Secondly, the auto-encoder is used to convert the high-level features into a fixed-length potential spatial representation, so as to obtain the depth features of the trajectory. Finally, the depth features are combined with the original features and used together as an input of the LSTM (Long Short-Term Memory network) for prediction. Experiments show that this method improves the accuracy by 9% and reduces the average distance error by 1km in comparison to the traditional RNN prediction model.
 

Key words: feature extraction, auto-encoder, LSTM, trajectory prediction

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