Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Tailoring Local Superstructure Units to Mitigate Voltage Decay in Na-Ion Batteries.

Angewandte Chemie (International ed. in English)·2026
Same author

Bipolar system induced surface electronic localization of violet phosphorene for CO<sub>2</sub> photoreduction to ethylene.

Journal of colloid and interface science·2026
Same author

Stepwise Molecular Design of Epoxy Dielectric Films toward High-Temperature, High-Efficiency Energy Storage.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Potential-dependent interfacial specific adsorption accelerates charge transfer in sodium-ion batteries.

Nature communications·2026
Same author

Violet Arsenic Phosphorus: Switching p-Type into High Performance n-Type Semiconductor by Arsenic Substitution.

Nano-micro letters·2026
Same author

Unveiling electric-field-driven deformation dynamics in metal nanostructures.

Nature communications·2025

Related Experiment Video

Updated: Jun 23, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

5.9K

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm.

Ji Ma1,2, Shenggen Zheng3, Shangjing Lin4

  • 1School of Network Security, Jinling Institute of Technology, Nanjing 211169, China.

Biomimetics (Basel, Switzerland)
|June 26, 2024
PubMed
Summary
This summary is machine-generated.

Optimizing shared bike dispatch using a Temporal Graph Convolutional Network (T-GCN) for demand prediction and genetic algorithms for resource scheduling improves efficiency. This approach enhances urban mobility and can be applied to other logistics challenges.

Keywords:
T-GCNbike-sharingscheduling optimizationspatiotemporal demand forecasting

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

533
Rapid Assembly of Multi-Gene Constructs using Modular Golden Gate Cloning
08:31

Rapid Assembly of Multi-Gene Constructs using Modular Golden Gate Cloning

Published on: February 5, 2021

13.4K

Related Experiment Videos

Last Updated: Jun 23, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

5.9K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

533
Rapid Assembly of Multi-Gene Constructs using Modular Golden Gate Cloning
08:31

Rapid Assembly of Multi-Gene Constructs using Modular Golden Gate Cloning

Published on: February 5, 2021

13.4K

Area of Science:

  • Urban planning and transportation logistics
  • Data science and artificial intelligence in logistics

Background:

  • Public transportation scheduling is vital for sustainable urban systems.
  • Bike-sharing systems enhance public transit by solving the 'last mile' problem.
  • Shared bikes face challenges like demand fluctuations and peak usage, impacting efficiency.

Purpose of the Study:

  • To develop a comprehensive approach for spatiotemporal demand prediction and bike dispatch optimization.
  • To improve the efficiency and user satisfaction of shared bike systems.
  • To provide a scalable solution for transportation scheduling problems with uncertain demand.

Main Methods:

  • A Temporal Graph Convolutional Network (T-GCN) model was designed for predicting shared bike demand.
  • An optimization solution using genetic algorithms was developed for bike dispatch, considering capacity, distance, and costs.
  • The approach was validated using real-world shared bike operating data.

Main Results:

  • The T-GCN model effectively predicted short-term shared bike demand.
  • The genetic algorithm-based optimization provided a complete dispatch solution.
  • The combined approach demonstrated effectiveness in optimizing shared bike operations.

Conclusions:

  • The proposed method successfully integrates demand prediction with resource scheduling for shared bike dispatch.
  • This integrated scheme offers a viable solution for optimizing urban mobility and resource allocation.
  • The methodology can be extended to other transportation and inventory management problems with variable demand.