A Sequence Learning Model With Recurrent Neural Networks For Taxi Demand Prediction

Keywords

Internet of things; mixture density networks; recurrent neural networks; taxi demand prediction; time series regression

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.

Publication Date

11-14-2017

Publication Title

Proceedings - Conference on Local Computer Networks, LCN

Volume

2017-October

Number of Pages

261-268

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/LCN.2017.31

Socpus ID

85040541906 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85040541906

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