A Convolutional Long Short-Term Memory Neural Network Based Prediction Model


  • Yonghong Tian College of Data Science and Application, Inner Mongolia University of Technology
  • Qi Wu College of Data Science and Application, Inner Mongolia University of Technology
  • Yue Zhang College of Data Science and Application, Inner Mongolia University of Technology


online car-hailing, supply and demand prediction, long short-term memory (LSTM), convolutional neural network (CNN), AdaBound


In recent years, the market demand for online car-hailing service has expanded dramatically. To satisfy the daily travel needs, it is important to predict the supply and demand of online car-hailing in an accurate manner, and make active scheduling based on the predicted gap between supply and demand. This paper puts forward a novel supply and demand prediction model for online carhailing, which combines the merits of convolutional neural network (CNN) and long short-term memory (LSTM). The proposed model was named convolutional LSTM (C-LSTM). Next, the original data on online car-hailing were processed, and the key features that affect the supply and demand prediction were extracted. After that, the C-LSTM was optimized by the AdaBound algorithm during the training process. Finally, the superiority of the C-LSTM in predicting online car-hailing supply and demand was proved through contrastive experiments.

Author Biographies

Yonghong Tian, College of Data Science and Application, Inner Mongolia University of Technology

Yong-Hong Tian, male, associate professor, master's supervisor. His research interests include deep learning, big data analysis and processing, machine learning, artificial intelligence.

Qi Wu, College of Data Science and Application, Inner Mongolia University of Technology

Qi Wu , female, master's degree. Her research interests include deep learning, big data analysis and processing.

Yue Zhang, College of Data Science and Application, Inner Mongolia University of Technology

Yue Zhang, female, master's degree. Her research interests include deep learning, big data analysis and processing


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