Enhancing Demand Prediction in Open Systems by Cartogram-aided Deep Learning

Khái niệm cốt lõi

Deep learning with cartogram aids demand prediction in open systems.

Tóm tắt

The article discusses the challenges of predicting temporal patterns in shared transport systems like public bicycles due to their openness and imbalanced usage. It introduces a deep learning framework using cartogram approaches to predict rental and return patterns, showcasing improved accuracy across different time scales.

Introduction

Predicting temporal patterns is challenging due to nuanced trajectories.
Shared transport systems face difficulties in predicting rental and return patterns.

Data Construction and Prediction Method

Spatio-temporal demand patterns of public bicycles are analyzed using deep learning models.
The study focuses on Seoul's open system, utilizing CNN for prediction alongside graph neural networks.

Rental-and-return data in Seoul

Time series data collection for rentals and returns hourly over two years.
City map division into grids for computational efficiency.

Node-feature matrix

Matrix size considerations for efficient data processing.

Thống kê

The total number of rental stations is 1,538 for 2018 and 1,554 for 2019.
Time series data was collected every hour from January 1, 2018, to December 31, 2019.

Trích dẫn

Thông tin chi tiết chính được chắt lọc từ

Enhancing Demand Prediction in Open Systems by Cartogram-aided Deep Learning

by Sangjoon Par... lúc arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16049.pdf

Yêu cầu sâu hơn

How can the proposed deep learning framework be applied to other open systems beyond public bicycles

The proposed deep learning framework, which leverages cartogram approaches for demand prediction in open systems like public bicycles, can be extended to various other domains beyond shared transportation. For instance, it could be applied to predict demand patterns in ride-sharing services, parking spaces utilization, or even crowd management at events or tourist attractions. By adapting the spatial-temporal convolutional graph attention network architecture and incorporating batch attention and modified node feature updates, this framework can effectively capture complex patterns and correlations within different open systems. The ability to predict temporal patterns across diverse domains makes this approach versatile and valuable for optimizing operations and resource allocation.

What are the potential limitations or biases that could arise from using cartogram-aided deep learning in demand prediction

While cartogram-aided deep learning offers significant advantages in predicting demand patterns in open systems, there are potential limitations and biases that need to be considered. One limitation is the reliance on historical data for training the model, which may introduce biases based on past trends or anomalies that do not reflect future behavior accurately. Additionally, the distortion introduced by Voronoi tessellation in creating cartograms could lead to inaccuracies if not carefully calibrated. Biases may arise from uneven station distributions or imbalanced usage patterns across regions if not properly addressed during preprocessing or model training. It's crucial to continuously validate the model against real-time data and adjust parameters accordingly to mitigate these limitations.

How might the use of Voronoi tessellation impact the scalability of the model when applied to larger datasets

The use of Voronoi tessellation as part of the cartogram-aided deep learning framework can impact scalability when applied to larger datasets due to computational complexity issues. As the dataset grows with more stations or regions being monitored for demand prediction, the calculations involved in generating accurate cartograms become more intensive. This could result in longer processing times and increased memory requirements when dealing with extensive spatial information from a broader area or multiple interconnected systems simultaneously.
To address scalability concerns when scaling up this model for larger datasets, optimization techniques such as parallel computing strategies or distributed processing frameworks might be necessary. Implementing efficient algorithms tailored for handling big data sets along with hardware acceleration methods could help improve performance while maintaining accuracy levels required for effective demand prediction across expansive open systems.