A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction

Tai Le Quy; Wolfgang Nejdl; Myra Spiliopoulou; Eirini Ntoutsi

doi:10.1007/978-3-030-38081-6_8

A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction

Tai Le Quy
, Wolfgang Nejdl^*
, Myra Spiliopoulou
, Eirini Ntoutsi

^*Corresponding author for this work

L3S Research Centre

Otto-von-Guericke University Magdeburg

Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

Abstract

Taxi is a convenient means of transportation worldwide. Accurately predicting the taxi-demand is crucial for taxi-companies to effectively allocate their fleet to taxi-stands and reduce the waiting time for passengers thus increasing their overall satisfaction and customer retention. Nowadays precise information about taxi-rides is available and can be used to infer the taxi-passenger demand across different locations and time-points. In this paper, we propose an approach for predicting the pick-demand of a given taxi-stand, that takes into account not only the demand-history of the particular stand but it also considers information from neighboring stands. Our model is an LSTM neural network augmented with information from the spatial neighborhood of the stands. Experiments with two versions of the taxi demand dataset from the city of Porto, Portugal show that our approach can provide better predictions comparing to approaches that do not exploit the neighborhood.

Original language	English
Title of host publication	Multiple-Aspect Analysis of Semantic Trajectories
Subtitle of host publication	First International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings
Editors	Konstantinos Tserpes, Chiara Renso, Stan Matwin
Place of Publication	Cham
Publisher	Springer Nature
Pages	100-116
Number of pages	17
ISBN (Electronic)	9783030380816
ISBN (Print)	9783030380809
DOIs	https://doi.org/10.1007/978-3-030-38081-6_8
Publication status	Published - 4 Jan 2020
Event	1st International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019 held in Conjunction with the 19th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 - Würzburg, Germany Duration: 16 Sept 2019 → 16 Sept 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11889
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019 held in Conjunction with the 19th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
Country/Territory	Germany
City	Würzburg
Period	16 Sept 2019 → 16 Sept 2019

UN Sustainable Development Goals (SDGs)

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Deep learning
k-nearest neighbors
LSTM
Neural networks
Taxi-passenger demand
Time series prediction

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-38081-6_8Licence: CC BY 4.0

Cite this

Quy, T. L., Nejdl, W., Spiliopoulou, M., & Ntoutsi, E. (2020). A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction. In K. Tserpes, C. Renso, & S. Matwin (Eds.), Multiple-Aspect Analysis of Semantic Trajectories: First International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings (pp. 100-116). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11889). Springer Nature. https://doi.org/10.1007/978-3-030-38081-6_8

Quy, Tai Le ; Nejdl, Wolfgang ; Spiliopoulou, Myra et al. / A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction. Multiple-Aspect Analysis of Semantic Trajectories: First International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings. editor / Konstantinos Tserpes ; Chiara Renso ; Stan Matwin. Cham : Springer Nature, 2020. pp. 100-116 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3974db23462d41ebb236e27bf1ffc460,

title = "A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction",

abstract = "Taxi is a convenient means of transportation worldwide. Accurately predicting the taxi-demand is crucial for taxi-companies to effectively allocate their fleet to taxi-stands and reduce the waiting time for passengers thus increasing their overall satisfaction and customer retention. Nowadays precise information about taxi-rides is available and can be used to infer the taxi-passenger demand across different locations and time-points. In this paper, we propose an approach for predicting the pick-demand of a given taxi-stand, that takes into account not only the demand-history of the particular stand but it also considers information from neighboring stands. Our model is an LSTM neural network augmented with information from the spatial neighborhood of the stands. Experiments with two versions of the taxi demand dataset from the city of Porto, Portugal show that our approach can provide better predictions comparing to approaches that do not exploit the neighborhood.",

keywords = "Deep learning, k-nearest neighbors, LSTM, Neural networks, Taxi-passenger demand, Time series prediction",

author = "Quy, \{Tai Le\} and Wolfgang Nejdl and Myra Spiliopoulou and Eirini Ntoutsi",

note = "Funding information: Acknowledgement. The work was inspired by the German Research Foundation (DFG) project OS-CAR (Opinion Stream Classification with Ensembles and Active leaRners) for which the last two authors are Principal Investigators.; 1st International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019 held in Conjunction with the 19th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 ; Conference date: 16-09-2019 Through 16-09-2019",

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Quy, TL, Nejdl, W, Spiliopoulou, M & Ntoutsi, E 2020, A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction. in K Tserpes, C Renso & S Matwin (eds), Multiple-Aspect Analysis of Semantic Trajectories: First International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11889, Springer Nature, Cham, pp. 100-116, 1st International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019 held in Conjunction with the 19th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, Würzburg, Germany, 16 Sept 2019. https://doi.org/10.1007/978-3-030-38081-6_8

A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction. / Quy, Tai Le; Nejdl, Wolfgang; Spiliopoulou, Myra et al.
Multiple-Aspect Analysis of Semantic Trajectories: First International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings. ed. / Konstantinos Tserpes; Chiara Renso; Stan Matwin. Cham: Springer Nature, 2020. p. 100-116 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11889).

Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

TY - GEN

T1 - A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction

AU - Quy, Tai Le

AU - Nejdl, Wolfgang

AU - Spiliopoulou, Myra

AU - Ntoutsi, Eirini

N1 - Funding information: Acknowledgement. The work was inspired by the German Research Foundation (DFG) project OS-CAR (Opinion Stream Classification with Ensembles and Active leaRners) for which the last two authors are Principal Investigators.

PY - 2020/1/4

Y1 - 2020/1/4

N2 - Taxi is a convenient means of transportation worldwide. Accurately predicting the taxi-demand is crucial for taxi-companies to effectively allocate their fleet to taxi-stands and reduce the waiting time for passengers thus increasing their overall satisfaction and customer retention. Nowadays precise information about taxi-rides is available and can be used to infer the taxi-passenger demand across different locations and time-points. In this paper, we propose an approach for predicting the pick-demand of a given taxi-stand, that takes into account not only the demand-history of the particular stand but it also considers information from neighboring stands. Our model is an LSTM neural network augmented with information from the spatial neighborhood of the stands. Experiments with two versions of the taxi demand dataset from the city of Porto, Portugal show that our approach can provide better predictions comparing to approaches that do not exploit the neighborhood.

AB - Taxi is a convenient means of transportation worldwide. Accurately predicting the taxi-demand is crucial for taxi-companies to effectively allocate their fleet to taxi-stands and reduce the waiting time for passengers thus increasing their overall satisfaction and customer retention. Nowadays precise information about taxi-rides is available and can be used to infer the taxi-passenger demand across different locations and time-points. In this paper, we propose an approach for predicting the pick-demand of a given taxi-stand, that takes into account not only the demand-history of the particular stand but it also considers information from neighboring stands. Our model is an LSTM neural network augmented with information from the spatial neighborhood of the stands. Experiments with two versions of the taxi demand dataset from the city of Porto, Portugal show that our approach can provide better predictions comparing to approaches that do not exploit the neighborhood.

KW - Deep learning

KW - k-nearest neighbors

KW - LSTM

KW - Neural networks

KW - Taxi-passenger demand

KW - Time series prediction

UR - https://www.scopus.com/pages/publications/85078417234

U2 - 10.1007/978-3-030-38081-6_8

DO - 10.1007/978-3-030-38081-6_8

M3 - Conference contribution

AN - SCOPUS:85078417234

SN - 9783030380809

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 100

EP - 116

BT - Multiple-Aspect Analysis of Semantic Trajectories

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A2 - Renso, Chiara

A2 - Matwin, Stan

PB - Springer Nature

CY - Cham

T2 - 1st International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019 held in Conjunction with the 19th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019

Y2 - 16 September 2019 through 16 September 2019

ER -

Quy TL, Nejdl W, Spiliopoulou M, Ntoutsi E. A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction. In Tserpes K, Renso C, Matwin S, editors, Multiple-Aspect Analysis of Semantic Trajectories: First International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings. Cham: Springer Nature. 2020. p. 100-116. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-38081-6_8

A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction

Abstract

Publication series

Conference

UN Sustainable Development Goals (SDGs)

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this