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Predicting Uber Demand in NYC with Wavenet

Authors:
Long Chen
Konstantinos Ampountolas
Piyushimita Thakuriah

Keywords: Uber Demand, Wavenet

Abstract:
Uber demand prediction is at the core of intelligent transportation systems when developing a smart city. However, exploiting uber real time data to facilitate the demand prediction is a thorny problem since user demand usually unevenly distributed over time and space. We develop a Wavenet-based model to predict Uber demand on an hourly basis. In this paper, we present a multi-level Wavenet framework which is a one-dimensional convolutional neural network that includes two sub-networks which encode the source series and decode the predicting series, respectively. The two sub-networks are combined by stacking the decoder on top of the encoder, which in turn, preserves the temporal patterns of the time series. Experiments on large-scale real Uber demand dataset of NYC demonstrate that our model is highly competitive to the existing ones.

Pages: 1 to 5

Copyright: Copyright (c) IARIA, 2019

Publication date: February 24, 2019

Published in: conference

ISSN: 2519-8378

ISBN: 978-1-61208-691-0

Location: Athens, Greece

Dates: from February 24, 2019 to February 28, 2019