Summary of Predicting Travel Demand Of a Bike Sharing System Using Graph Convolutional Neural Networks, by Ali Behroozi and Ali Edrisi
Predicting travel demand of a bike sharing system using graph convolutional neural networks
by Ali Behroozi, Ali Edrisi
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to predicting travel demand within bike-sharing systems using a hybrid deep learning model called the gate graph convolutional neural network (GGCNN). The GCGNN integrates trajectory data, weather data, access data, and leverages graph convolution networks to significantly improve the accuracy of travel demand forecasting. The proposed model is compared to base models used in previous literature, demonstrating better performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where bike-sharing systems make it easier for people to get around without polluting the air or causing traffic congestion. To make this happen, transportation planners need to know how many people will use the system at each station. This paper shows how to predict travel demand at the station level using special computer models called deep learning models. By combining different types of data, like where bikes are going and what the weather is like, these models can make more accurate predictions. |
Keywords
» Artificial intelligence » Deep learning » Neural network
