DeepTrip: A Deep Learning Model for the Individual Next Trip Prediction With Arbitrary Prediction Times-Reference-Cited by-同舟云学术

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DeepTrip: A Deep Learning Model for the Individual Next Trip Prediction With Arbitrary Prediction Times

Published:2023-06 Issue:6 Volume:24 Page:5842-5855
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Zhang Pengfei¹^ORCID,Koutsopoulos Haris N.²^ORCID,Ma Zhenliang³^ORCID

Affiliation:

1. Institute of Physics, Henan Academy of Sciences, Zhengzhou, China

2. Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA

3. Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

Funder

Kungliga Tekniska högskolan (KTH) Digital Futures Project on cAIMBER through the Basic Research Fund

Startup Research Fund of Henan Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10139322/10074989.pdf?arnumber=10074989

Reference36 articles.

1. Examining the Limits of Predictability of Human Mobility

2. Get The Point of My Utterance! Learning Towards Effective Responses with Multi-Head Attention Mechanism

3. Approaching the Limit of Predictability in Human Mobility

4. Effective Approaches to Attention-based Neural Machine Translation

5. Next place prediction using mobility Markov chains

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3. COLA: Cross-city Mobility Transformer for Human Trajectory Simulation;Proceedings of the ACM Web Conference 2024;2024-05-13

4. Testing feasibility of using a hidden Markov model on predicting human mobility based on GPS tracking data;Transportmetrica B: Transport Dynamics;2024-04-04

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