A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Graph neural networks for intelligent transportation systems: A survey
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
Improving short-term bike sharing demand forecast through an irregular convolutional neural network
As an important task for the management of bike sharing systems, accurate forecast of travel
demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In …
demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In …
Forecasting bike sharing demand using quantum Bayesian network
R Harikrishnakumar, S Nannapaneni - Expert Systems with Applications, 2023 - Elsevier
In recent years, bike-sharing systems (BSS) are being widely established in urban cities to
provide a sustainable mode of transport, by fulfilling the mobility requirements of public …
provide a sustainable mode of transport, by fulfilling the mobility requirements of public …
One size fits all: A unified traffic predictor for capturing the essential spatial–temporal dependency
Traffic prediction is a keystone for building smart cities in the new era and has found wide
applications in traffic scheduling and management, environment policy making, public …
applications in traffic scheduling and management, environment policy making, public …
Demand prediction and optimal allocation of shared bikes around urban rail transit stations
L Yu, T Feng, T Li, L Cheng - Urban Rail Transit, 2023 - Springer
The imbalance between the supply and demand of shared bikes is prominent in many urban
rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this …
rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this …
Enhancing multistep-ahead bike-sharing demand prediction with a two-stage online learning-based time-series model: insight from Seoul
Bike-sharing is a powerful solution to urban challenges (eg, expanding bike communities,
lowering transportation costs, alleviating traffic congestion, reducing emissions, and …
lowering transportation costs, alleviating traffic congestion, reducing emissions, and …
A short-term hybrid TCN-GRU prediction model of bike-sharing demand based on travel characteristics mining
S Zhou, C Song, T Wang, X Pan, W Chang, L Yang - Entropy, 2022 - mdpi.com
This paper proposes an accurate short-term prediction model of bike-sharing demand with
the hybrid TCN-GRU method. The emergence of shared bicycles has provided people with a …
the hybrid TCN-GRU method. The emergence of shared bicycles has provided people with a …
Applying Machine Learning in Retail Demand Prediction—A Comparison of Tree-Based Ensembles and Long Short-Term Memory-Based Deep Learning
In the realm of retail supply chain management, accurate forecasting is paramount for
informed decision making, as it directly impacts business operations and profitability. This …
informed decision making, as it directly impacts business operations and profitability. This …
Enabling smart curb management with spatiotemporal deep learning
Curb spaces are important assets to cities. They are often used by travelers to switch
transportation means, visitors to access curbside properties, and municipalities to place …
transportation means, visitors to access curbside properties, and municipalities to place …