When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time-Reference-Cited by-同舟云学术

When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time

Published:2023-10 Issue:10 Volume:24 Page:11438-11452
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Zou Guojian¹^ORCID,Lai Ziliang¹,Ma Changxi²^ORCID,Tu Meiting¹,Fan Jing³^ORCID,Li Ye¹

Affiliation:

1. Key Laboratory of Road and Traffic Engineering, Ministry of Education, and the College of Transportation Engineering, Tongji University, Shanghai, China

2. School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, China

3. China Railway First Survey and Design Institute Group Company Ltd, Xi’an, China

Funder

Project of the National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Foundation of China

Key Research and Development Project of Gansu Province

Gansu Provincial Science and Technology Major Special Project—Enterprise Innovation Consortium Project

Lanzhou Jiaotong University Basic Research Top Talents Training Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10271405/10133870.pdf?arnumber=10133870

Reference44 articles.

1. GMAN: A Graph Multi-Attention Network for Traffic Prediction

2. TAML: A Traffic-aware Multi-task Learning Model for Estimating Travel Time

3. Long-term Origin-Destination Demand Prediction with Graph Deep Learning

4. Learning heterogeneous traffic patterns for travel time prediction of bus journeys

5. Short-Term Traffic Flow Prediction for Urban Road Sections Based on Time Series Analysis and LSTM_BILSTM Method

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