J. Xu, R. Rahmatizadeh, L. Bölöni, and D. Turgut

A Sequence Learning Model with Recurrent Neural Networks for Taxi Demand Prediction


Cite as:

J. Xu, R. Rahmatizadeh, L. Bölöni, and D. Turgut. A Sequence Learning Model with Recurrent Neural Networks for Taxi Demand Prediction. In to be presented at IEEE Local Computer Networks (LCN 2017), October 2017.

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Abstract:

In this paper, we focus on an application of recurrent neural networks for learning a model that predicts taxi demand based on the requests in the past. A model that can learn time series data is necessary here since taxi requests in the future relate to the requests in the past. For instance, someone who requests a taxi to a movie theater, may also request a taxi to return home after few hours. We use Long Short Term Memory (LSTM), one of the best models for learning time series data. For training the network, we encode the historical taxi requests from the official New York City taxi trip dataset and add date, day of the week and time as impacting factors. Experimental results show that our approach outperforms the prediction heuristics based on feed-forward neural networks and naive statistic average.

BibTeX:

@inproceedings{Xu-2017-LCN,
  author = "J.~Xu and R.~Rahmatizadeh and L.~B{\"o}l{\"o}ni and D.~Turgut",
  title = "A Sequence Learning Model with Recurrent Neural Networks for Taxi Demand Prediction",
  booktitle = "to be presented at IEEE Local Computer Networks (LCN 2017)",
  year = "2017",
  xxxpages = "22-29",
  month = "October",
  location = "Singapore",
  abstract = {
    In this paper, we focus on an application of recurrent neural networks for learning a model that predicts taxi demand based on the requests in the past. A model that can learn time series data is necessary here since taxi requests in the future relate to the requests in the past. For instance, someone who requests a taxi to a movie theater, may also request a taxi to return home after few hours. We use Long Short Term Memory (LSTM), one of the best models for learning time series data. For training the network, we encode the historical taxi requests from the official New York City taxi trip dataset and add date, day of the week and time as impacting factors. Experimental results show that our approach outperforms the prediction heuristics based on feed-forward neural networks and naive statistic average.
  }
}

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