Large-scale short-term urban taxi demand forecasting using deep learning

Siyu Liao, Liutong Zhou, Xuan Di, Bo Yuan, Jinjun Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

The world has seen in recent years great successes in applying deep learning (DL) for many application domains. Though powerful, DL is not easy to be used well. In this invited paper, we study an urban taxi demand forecast problem using DL, and we show a number of key insights in modeling a domain problem as a suitable DL task. We also conduct a systematic comparison of two recent deep neural networks (DNNs) for taxi demand prediction, i.s., the ST-ResNet and FLC-Net, on New York city taxi record dataset. Our experimental results show DNNs indeed outperform most traditional machine learning techniques, but such superior results can only be achieved with proper design of the right DNN architecture, where domain knowledge plays a key role.

Original languageEnglish (US)
Title of host publicationASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages428-433
Number of pages6
ISBN (Electronic)9781509006021
DOIs
StatePublished - Feb 20 2018
Externally publishedYes
Event23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018 - Jeju, Korea, Republic of
Duration: Jan 22 2018Jan 25 2018

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
Volume2018-January

Other

Other23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018
CountryKorea, Republic of
CityJeju
Period1/22/181/25/18

Fingerprint

Network architecture
Learning systems
Deep learning
Deep neural networks

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

Cite this

Liao, S., Zhou, L., Di, X., Yuan, B., & Xiong, J. (2018). Large-scale short-term urban taxi demand forecasting using deep learning. In ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings (pp. 428-433). (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASPDAC.2018.8297361
Liao, Siyu ; Zhou, Liutong ; Di, Xuan ; Yuan, Bo ; Xiong, Jinjun. / Large-scale short-term urban taxi demand forecasting using deep learning. ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 428-433 (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC).
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Liao, S, Zhou, L, Di, X, Yuan, B & Xiong, J 2018, Large-scale short-term urban taxi demand forecasting using deep learning. in ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings. Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 428-433, 23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018, Jeju, Korea, Republic of, 1/22/18. https://doi.org/10.1109/ASPDAC.2018.8297361

Large-scale short-term urban taxi demand forecasting using deep learning. / Liao, Siyu; Zhou, Liutong; Di, Xuan; Yuan, Bo; Xiong, Jinjun.

ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 428-433 (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Liao S, Zhou L, Di X, Yuan B, Xiong J. Large-scale short-term urban taxi demand forecasting using deep learning. In ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 428-433. (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC). https://doi.org/10.1109/ASPDAC.2018.8297361