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

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

doi:10.1109/ASPDAC.2018.8297361

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

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

Department of Computer Science and Engineering

City University of New York
Columbia University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

60 Scopus citations

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 language	English
Title of host publication	ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	428-433
Number of pages	6
ISBN (Electronic)	9781509006021
DOIs	https://doi.org/10.1109/ASPDAC.2018.8297361
State	Published - Feb 20 2018
Event	23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018 - Jeju, Korea, Republic of Duration: Jan 22 2018 → Jan 25 2018

Publication series

Name	Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
Volume	2018-January

Conference

Conference	23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018
Country/Territory	Korea, Republic of
City	Jeju
Period	01/22/18 → 01/25/18

Access to Document

10.1109/ASPDAC.2018.8297361

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

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title = "Large-scale short-term urban taxi demand forecasting using deep learning",

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.",

author = "Siyu Liao and Liutong Zhou and Xuan Di and Bo Yuan and Jinjun Xiong",

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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, 01/22/18. 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 et al.
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 proceeding › Conference contribution › peer-review

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AU - Yuan, Bo

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PY - 2018/2/20

Y1 - 2018/2/20

N2 - 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.

AB - 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.

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M3 - Conference 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). doi: 10.1109/ASPDAC.2018.8297361

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

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