Details
| 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 |
| 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 |
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
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Sustainable Development Goals
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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 - http://www.scopus.com/inward/record.url?scp=85078417234&partnerID=8YFLogxK
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
A2 - Tserpes, Konstantinos
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 -