Next Place Prediction-Reference-Cited by-同舟云学术

Next Place Prediction

Published:2018-11-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility
language:
Short-container-title:

Author:

Schreckenberger Christian¹,Beckmann Simon¹,Bartelt Christian¹

Affiliation:

1. Institute for Enterprise Systems, University of Mannheim, Mannheim, Germany

Funder

German Ministry of Economics

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3283590.3283596

Reference49 articles.

1. Hamidu Abdel-Fatao Jiuyong Li and Jixue Liu. 2015. STMM: Semantic and Temporal-Aware Markov Chain Model for Mobility Prediction. In Data Science Chengqi Zhang Wei Huang Yong Shi Philip S. Yu Yangyong Zhu Yingjie Tian Peng Zhang and Jing He (Eds.). Vol. 9208. Springer International Publishing Cham 103--111. 10.1007/978-3-319-24474-7_15 Hamidu Abdel-Fatao Jiuyong Li and Jixue Liu. 2015. STMM: Semantic and Temporal-Aware Markov Chain Model for Mobility Prediction. In Data Science Chengqi Zhang Wei Huang Yong Shi Philip S. Yu Yangyong Zhu Yingjie Tian Peng Zhang and Jing He (Eds.). Vol. 9208. Springer International Publishing Cham 103--111. 10.1007/978-3-319-24474-7_15

2. Yasunori Akagi Takuya Nishimura Takeshi Kurashima and Hiroyuki Toda. 2018. A Fast and Accurate Method for Estimating People Flow from Spatio-temporal Population Data.. In IJCAI. 3293--3300. Yasunori Akagi Takuya Nishimura Takeshi Kurashima and Hiroyuki Toda. 2018. A Fast and Accurate Method for Estimating People Flow from Spatio-temporal Population Data.. In IJCAI. 3293--3300.

3. A. Al-Molegi M. Jabreel and B. Ghaleb. 2016. STF-RNN: Space Time Features-based Recurrent Neural Network for predicting people next location. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). 1--7. A. Al-Molegi M. Jabreel and B. Ghaleb. 2016. STF-RNN: Space Time Features-based Recurrent Neural Network for predicting people next location. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). 1--7.

4. Learning and user adaptation in location forecasting

5. Using GPS to learn significant locations and predict movement across multiple users

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