Machine Learning for public transportation demand prediction: A Systematic Literature Review
Abstract Within the Intelligent Public Transportation Systems (IPTS) field, the prediction of
public transportation demand is a key point for enhancing the quality of the services. These …
public transportation demand is a key point for enhancing the quality of the services. These …
Combining knowledge graph into metro passenger flow prediction: A split-attention relational graph convolutional network
With the rapid development of intelligent operation and management in metro systems,
accurate network-scale passenger flow prediction has become an essential component in …
accurate network-scale passenger flow prediction has become an essential component in …
Impacts of COVID-19 on urban rail transit ridership using the Synthetic Control Method
M **n, A Shalaby, S Feng, H Zhao - Transport policy, 2021 - Elsevier
The outbreak of COVID-19 in 2020 has had drastic impacts on urban economies and
activities, with transit systems around the world witnessing an unprecedented decline in …
activities, with transit systems around the world witnessing an unprecedented decline in …
Multi-graph convolutional-recurrent neural network (MGC-RNN) for short-term forecasting of transit passenger flow
Short-term forecasting of passenger flow is critical for transit management and crowd
regulation. Spatial dependencies, temporal dependencies, inter-station correlations driven …
regulation. Spatial dependencies, temporal dependencies, inter-station correlations driven …
Analysis of the relationship between metro ridership and built environment: A machine learning method considering combinational features
Limited studies have examined the relationship between combinational features of the built
environment and metro ridership. In this study, we applied the gradient boosting regression …
environment and metro ridership. In this study, we applied the gradient boosting regression …
An overview and general framework for spatiotemporal modeling and applications in transportation and public health
Spatiotemporal modeling and forecasting is an essential task for many real-world problems,
especially in the field of transportation and public health. The complex and dynamic patterns …
especially in the field of transportation and public health. The complex and dynamic patterns …
Forecasting metro rail transit passenger flow with multiple-attention deep neural networks and surrounding vehicle detection devices
JL Wu, M Lu, CY Wang - Applied Intelligence, 2023 - Springer
In the rapid development of public transportation led, the traffic flow prediction has become
one of the most crucial issues, especially estimating the number of passengers using the …
one of the most crucial issues, especially estimating the number of passengers using the …
Station-level short-term demand forecast of carsharing system via station-embedding-based hybrid neural network
F Zhao, W Wang, H Sun, H Yang… - … B: Transport Dynamics, 2022 - Taylor & Francis
Station-based one-way carsharing system brings transformation to public mobility and spurs
the growth of sharing economy. The accurate estimation of rental and return demand of …
the growth of sharing economy. The accurate estimation of rental and return demand of …
Visualization in operations management research
The unprecedented availability of data, along with the growing variety of software packages
to visualize it, presents both opportunities and challenges for operations management (OM) …
to visualize it, presents both opportunities and challenges for operations management (OM) …
How do access and spatial dependency shape metro passenger flows
Spatial imbalances in metro ridership significantly reduce the overall efficiency of metro
system. Understanding the factors that contribute to metro ridership is essential for …
system. Understanding the factors that contribute to metro ridership is essential for …