Chen, L., Thakuriah, V. and Ampountolas, K. (2019) Predicting Uber Demand of NYC with Wavenet. In: SMART ACCESSIBILITY 2019, Athens, Greece, 24-28 Feb 2019, ISBN 9781612086910
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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.
Item Type: | Conference Proceedings |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Thakuriah, Professor Piyushimita and Chen, Dr Long and Ampountolas, Dr Konstantinos |
Authors: | Chen, L., Thakuriah, V., and Ampountolas, K. |
Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
College/School: | College of Science and Engineering > School of Engineering > Infrastructure and Environment College of Social Sciences > School of Social and Political Sciences |
ISSN: | 2519-8378 |
ISBN: | 9781612086910 |
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