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Found 11 papers, 2 papers with code
ST-former for short-term passenger flow prediction during COVID-19 in urban rail transit system
How to dynamically model the complex spatiotemporal dependencies of passenger flow is the main issue in achieving accurate passenger flow prediction during the epidemic.
Meta-learning Based Short-Term Passenger Flow Prediction for Newly-Operated Urban Rail Transit Stations
The Meta-LSTM is to construct a framework that increases the generalization ability of long short-term memory network (LSTM) to various passenger flow characteristics by learning passenger flow characteristics from multiple data-rich stations and then applying the learned parameter to data-scarce stations by parameter initialization.
Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction
To overcome these limitations, we propose an automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction.
Short-term passenger flow prediction for multi-traffic modes: A Transformer and residual network based multi-task learning method
As an important component of MaaS, short-term passenger flow prediction for multi-traffic modes has thus been brought into focus.
Spatial-Temporal Attention Fusion Network for short-term passenger flow prediction on holidays in urban rail transit systems
The short term passenger flow prediction of the urban rail transit system is of great significance for traffic operation and management.
An end-to-end predict-then-optimize clustering method for intelligent assignment problems in express systems
To solve these problems, we put forward an intelligent end-to-end predict-then-optimize clustering method to simultaneously predict the future pick-up requests of AOIs and assign AOIs to couriers by clustering.
STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems
To overcome these limitations, we propose a novel deep learning-based spatiotemporal graph generative adversarial network (STG-GAN) model with higher prediction accuracy, higher efficiency, and lower memory occupancy to predict short-term passenger flows of the URT network.
Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach
This paper focuses on an essential and hard problem to estimate the network-wide link travel time and station waiting time using the automatic fare collection (AFC) data in the URT system, which is beneficial to better understand the system-wide real-time operation state.
Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method
The proposed model contributes to the development of short-term OD flow prediction, and it also lays the foundations of real-time URT operation and management.
Multi-Graph Convolutional Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit
Short-term passenger flow forecasting is a crucial task in the operation of urban rail transit.
Physics and Society
Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit
Finally, ResLSTM is applied to the Beijing subway using three time granularities (10, 15, and 30 min) to conduct short-term passenger flow forecasting.
