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TDP: Personalized Taxi Demand Prediction Based on Heterogeneous Graph Embedding

Published: 18 July 2019 Publication History

Abstract

Predicting users' irregular trips in a short term period is one of the crucial tasks in the intelligent transportation system. With the prediction, the taxi requesting services, such as Didi Chuxing in China, can manage the transportation resources to offer better services. There are several different transportation scenes, such as commuting scene and entertainment scene. The origin and the destination of entertainment scene are more unsure than that of commuting scene, so both origin and destination should be predicted. Moreover, users' trips on Didi platform is only a part of their real life, so these transportation data are only few weak samples. To address these challenges, in this paper, we propose Taxi Demand Prediction (TDP) model in challenging entertainment scene based on heterogeneous graph embedding and deep neural predicting network. TDP aims to predict next possible trip edges that have not appeared in historical data for each user in entertainment scene. Experimental results on the real-world dataset show that TDP achieves significant improvements over the state-of-the-art methods.

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Cited By

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  • (2022)Abnormal User Detection via Multiview Graph Clustering in the Mobile e-Commerce NetworkWireless Communications and Mobile Computing10.1155/2022/37668102022(1-17)Online publication date: 9-Aug-2022
  • (2021)Human Origin-Destination Flow Prediction Based on Large Scale Mobile Signal DataWireless Communications & Mobile Computing10.1155/2021/16042682021Online publication date: 29-Sep-2021
  • (2020)Gemini: A Novel and Universal Heterogeneous Graph Information Fusing Framework for Online RecommendationsProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403388(3356-3365)Online publication date: 23-Aug-2020

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cover image ACM Conferences
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2019
1512 pages
ISBN:9781450361729
DOI:10.1145/3331184
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2019

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Author Tags

  1. deep neural network
  2. heterogeneous graph embedding
  3. taxi demand prediction

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  • Short-paper

Funding Sources

  • National Key Research and Development Program of China under grants
  • National Natural Science Foundation of China under grants

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SIGIR '19
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SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2022)Abnormal User Detection via Multiview Graph Clustering in the Mobile e-Commerce NetworkWireless Communications and Mobile Computing10.1155/2022/37668102022(1-17)Online publication date: 9-Aug-2022
  • (2021)Human Origin-Destination Flow Prediction Based on Large Scale Mobile Signal DataWireless Communications & Mobile Computing10.1155/2021/16042682021Online publication date: 29-Sep-2021
  • (2020)Gemini: A Novel and Universal Heterogeneous Graph Information Fusing Framework for Online RecommendationsProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403388(3356-3365)Online publication date: 23-Aug-2020

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