Demand Density Forecasting in Mobility-on-Demand Systems Through Recurrent Mixture Density Networks-Reference-Cited by-同舟云学术

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Demand Density Forecasting in Mobility-on-Demand Systems Through Recurrent Mixture Density Networks

Published:2023-09-24 Issue: Volume: Page:250-255
ISSN:
Container-title:2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
language:
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Author:

Li Xiaoming¹,Normandin-Taillon Hubert²,Wang Chun¹,Huang Xiao³

Affiliation:

1. Concordia Institute for Information Systems Engineering (CIISE), Concordia University,Montréal,QC,Canada,H3G 1M8

2. Concordia University,Department of Computer Science and Software Engineering (CSSE),Montréal,QC,Canada,H3G 1M8

3. Concordia John Molson School of Business (JMSB), Concordia University,Montréal,QC,Canada,H3G 1M8

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10420842/10420843/10422357.pdf?arnumber=10422357

Reference31 articles.

1. Ridesourcing systems: A framework and review

2. On the impact of deep learning-based time-series forecasts on multistage stochastic programming policies

3. Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems Under Demand and Supply Uncertainties

4. Forecasting time-dependent conditional densities: a semi non-parametric neural network approach

5. Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models

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