MTLMetro: A Deep Multi-Task Learning Model for Metro Passenger Demands Prediction-Reference-Cited by-同舟云学术

MTLMetro: A Deep Multi-Task Learning Model for Metro Passenger Demands Prediction

Published:2024-09 Issue:9 Volume:25 Page:11805-11820
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Huang Hao¹,Mao Jiannan¹,Liu Ronghui²^ORCID,Lu Weike³^ORCID,Tang Tianli⁴^ORCID,Liu Lan¹^ORCID

Affiliation:

1. School of Transportation and Logistics and the National and Local Joint Engineering Laboratory of Integrated Intelligent Transportation, Southwest Jiaotong University, Chengdu, China

2. Institute for Transport Studies, University of Leeds, Leeds, U.K.

3. School of Rail Transportation, Soochow University, Suzhou, China

4. School of Transportation, Southeast University, Nanjing, China

Funder

National Natural Science Foundation of China

Sichuan Provincial Science and Technology Support Program

China Scholarship Council

Project of Jiangsu Funding Program for Excellent Postdoctoral Talent

Project of ‘Chunhui Jihua’ of Ministry of Education, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10659279/10475134.pdf?arnumber=10475134

Reference59 articles.

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3. Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model

4. Using an ARIMA-GARCH Modeling Approach to Improve Subway Short-Term Ridership Forecasting Accounting for Dynamic Volatility

5. DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending

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