Enhancing Citywide Flow Forecasting through Deep Learning and Spatial Temporal Learning-Reference-Cited by-同舟云学术

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Enhancing Citywide Flow Forecasting through Deep Learning and Spatial Temporal Learning

Published:2024-02-23 Issue: Volume:1 Page:1-6
ISSN:
Container-title:2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)
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Author:

Parvathi R.¹,Sarkar Swagata²,Supraja M.³,Swagatha J P⁴,Kanagajothi D.⁵,S Srimathi⁶

Affiliation:

1. SRM Arts and Science College,Computer Science,Kattankulathur,India

2. Sri Sairam Engineering College,Department of Artificial Intelligence and Data Science,Chennai,India

3. Rajarajeshwari College of Engineering,Electronics and Communication Engineering,Bangalore,India

4. Panimalar Engineering College,Department of IT,Poonamallee,India

5. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,Department of Mathematics

6. SIMATS,Technology Saveetha School of Engineering,Department of Bio,Chennai,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10498131/10498150/10498960.pdf?arnumber=10498960

Reference17 articles.

1. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

2. Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks;Sun;IEEE Annals of the History of Computing,2019

3. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

4. Traffic Flow Prediction With Big Data: A Deep Learning Approach

5. A consecutive hybrid spiking-convolutional (CHSC) neural controller for sequential decision making in robots

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