Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network
Authors
Keywords
Passenger demand prediction, Region division, Deep learning, Graph convolutional network, Community detection
Journal
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Volume 124, Issue -, Pages 102951
Publisher
Elsevier BV
Online
2021-01-09
DOI
10.1016/j.trc.2020.102951
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Taxi-Based Mobility Demand Formulation and Prediction Using Conditional Generative Adversarial Network-Driven Learning Approaches
- (2019) Hao Yu et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction
- (2019) Lingbo Liu et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Passenger flow prediction for new line using region dividing and fuzzy boundary processing
- (2018) Haitao Yu et al. IEEE TRANSACTIONS ON FUZZY SYSTEMS
- A hybrid deep learning based traffic flow prediction method and its understanding
- (2018) Yuankai Wu et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach
- (2018) Lei Lin et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Hexagon-Based Convolutional Neural Network for Supply-Demand Forecasting of Ride-Sourcing Services
- (2018) Jintao Ke et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks
- (2017) Jun Xu et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic
- (2017) Jinjun Tang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
- (2017) Xiaolei Ma et al. SENSORS
- Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
- (2017) Haiyang Yu et al. SENSORS
- Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach
- (2017) Jintao Ke et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation
- (2015) Jinjun Tang et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Predicting Taxi–Passenger Demand Using Streaming Data
- (2013) Luis Moreira-Matias et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Fast unfolding of communities in large networks
- (2008) Vincent D Blondel et al. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now