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

Real-time Prediction of Taxi Demand Using \\Recurrent Neural Networks


Cite as:

J. Xu, R. Rahmatizadeh, L. Bölöni, and D. Turgut. Real-time Prediction of Taxi Demand Using \\Recurrent Neural Networks. under review in IEEE Transactions on Intelligent Transportation Systems, 2017.

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Abstract:

Predicting taxi demand throughout a city can help to organize the taxi fleet and minimize the wait-time for passengers and drivers. In this paper, we propose a sequence learning model that can predict future taxi requests in each area of a city based on the recent demand and other relevant information. Remembering the information from the past is critical here since taxi requests in the future depend on some information from the past. For example, someone who requests a taxi to a shopping center, may also request a taxi to return home after few hours. We use one of the best sequence learning methods, Long Short Term Memory (LSTM) that has a gating mechanism to store the relevant information for future use. We evaluate our method on a dataset of taxi requests in New York City by dividing the city into small areas and predicting the demand in each area. We show that this approach outperforms other prediction methods such as feed-forward neural networks. In addition, we show how adding other relevant information such as weather, time, and drop-offs affects the results.

BibTeX:

@article{Xu-2017-TransITS,
  author = "J.~Xu and R.~Rahmatizadeh and L.~B{\"o}l{\"o}ni and D.~Turgut",
  title = "Real-time Prediction of Taxi Demand Using \\Recurrent Neural Networks",
  journal = "under review in IEEE Transactions on Intelligent Transportation Systems",
  year = "2017",
  xxxpages = "22-29",
  abstract = {
  Predicting taxi demand throughout a city can help to organize the taxi fleet and minimize the wait-time for passengers and drivers. In this paper, we propose a sequence learning model that can predict future taxi requests in each area of a city based on the recent demand and other relevant information. Remembering the information from the past is critical here since taxi requests in the future depend on some information from the past. For example, someone who requests a taxi to a shopping center, may also request a taxi to return home after few hours. We use one of the best sequence learning methods, Long Short Term Memory (LSTM) that has a gating mechanism to store the relevant information for future use. We evaluate our method on a dataset of taxi requests in New York City by dividing the city into small areas and predicting the demand in each area. We show that this approach outperforms other prediction methods such as feed-forward neural networks. In addition, we show how adding other relevant information such as weather, time, and drop-offs affects the results.
  }
}

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