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

Taxi Dispatch Planning via Demand and Destination Modeling


Cite as:

J. Xu, R. Rahmatizadeh, L. Bölöni, and D. Turgut. Taxi Dispatch Planning via Demand and Destination Modeling. In Proc. of 43nd IEEE Conference on Local Computer Networks (LCN-2018), October 2018.

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Abstract:

In this paper, we focus on a taxi dispatch system with the help of auxiliary models that predict future demand and destination. We build two different neural networks for learning taxi demand and destination distribution patterns based on historical data. The trained models can predict taxi demand and destination for any area in a city at a future time. Our proposed dispatch system relies on the predictions of the previous models and is designed not only to minimize the waiting time of passengers, but also to assign the taxis to passengers in a way to minimize the idle driving distances of taxis. In order to achieve this, we balance future taxi supply-demand over the city by solving a mixed-integer program (MIP). We validate our dispatch system as well as the prediction models using a dataset of taxi trips in the New York City.

BibTeX:

@inproceedings{Xu-2018-LCN,
  author = "J. Xu and R. Rahmatizadeh and L. B{\"o}l{\"o}ni and D. Turgut",
  title = "Taxi Dispatch Planning via Demand and Destination Modeling",
  booktitle = "Proc. of 43nd IEEE Conference on Local Computer Networks (LCN-2018)",
  year = "2018",
  month = "October",
  abstract = {
    In this paper, we focus on a taxi dispatch system with the help of auxiliary models that predict future demand and destination. We build two different neural networks for learning taxi demand and destination distribution patterns based on historical data. The trained models can predict taxi demand and destination for any area in a city at a future time. Our proposed dispatch system relies on the predictions of the previous models and is designed not only to minimize the waiting time of passengers, but also to assign the taxis to passengers in a way to minimize the idle driving distances of taxis. In order to achieve this, we balance future taxi supply-demand over the city by solving a mixed-integer program (MIP). We validate our dispatch system as well as the prediction models using a dataset of taxi trips in the New York City.
  }
}

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