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Bike sharing usage prediction with deep learning: a survey.

¹School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876 China.

Neural Computing & Applications

|June 15, 2022

Summary

This summary is machine-generated.

This survey reviews deep learning models for predicting bike sharing usage, crucial for efficient system management. It covers spatial-temporal modeling and external factors, highlighting advancements in the field.

Keywords:

Bike sharing Bike usage prediction Deep learning Neural networks

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Area of Science:

Urban planning and transportation science.
Computer science, specifically artificial intelligence and machine learning.

Background:

Bike sharing systems are a key component of urban sustainable mobility.
Accurate bike usage prediction is essential for operational efficiency, including inventory management and rebalancing.

Purpose of the Study:

To provide a comprehensive survey of deep learning techniques for bike sharing usage prediction.
To classify prediction problems and models, and discuss their applications.

Main Methods:

Review of recent studies employing deep learning for bike sharing usage prediction.
Analysis of models capturing spatial interactions (CNNs, GNNs) and temporal dependencies (RNNs).
Consideration of environmental and societal factors influencing bike demand.

Main Results:

Deep learning models, including RNNs, CNNs, and GNNs, demonstrate significant advantages in precise bike sharing usage prediction.
The survey categorizes various prediction tasks and deep learning architectures used in the field.
Applications of bike usage prediction extend beyond system management to broader urban contexts.

Conclusions:

Deep learning offers powerful tools for modeling complex factors in bike sharing demand.
This paper is the first comprehensive survey focusing on deep learning for bike sharing usage prediction.
Future research directions are identified to advance the field.