Passenger flow forecasting on transportation network: sensitivity analysis of the spatiotemporal features-Reference-Cited by-同舟云学术

Passenger flow forecasting on transportation network: sensitivity analysis of the spatiotemporal features

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 International Conference on Data Mining Workshops (ICDMW)
language:
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Author:

Baro Johanna¹,Khouadjia Mostepha¹

Affiliation:

1. IRT SystemX,Saclay,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9679833/9679835/09679883.pdf?arnumber=9679883

Reference25 articles.

1. DeepPF: A deep learning based architecture for metro passenger flow prediction

2. Forecasting Short-Term Passenger Flow: An Empirical Study on Shenzhen Metro

3. Data-Driven Metro Train Crowding Prediction Based on Real-Time Load Data

4. Supervised learning: Predicting passenger load in public transport;heydenrijk-ottens;Conference on Advanced Systems in Public Transport and TransitData (CASPT),2018

5. Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data;zhong;PLoS ONE,2016

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