Real-Time Prediction Of Taxi Demand Using Recurrent Neural Networks

Keywords

mixture density networks; recurrent neural networks; Taxi demand prediction; time series forecasting

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 information from the past is critical here, since taxi requests in the future are correlated with information about actions that happened in 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 that has a gating mechanism to store the relevant information for future use. We evaluate our method on a data set 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.

Publication Date

8-1-2018

Publication Title

IEEE Transactions on Intelligent Transportation Systems

Volume

19

Issue

8

Number of Pages

2572-2581

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TITS.2017.2755684

Socpus ID

85032457601 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85032457601

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