Comparison of main approaches for extracting behavior features from crowd flow analysis
Z Ebrahimpour, W Wan, O Cervantes, T Luo… - … International Journal of …, 2019 - mdpi.com
Extracting features from crowd flow analysis has become an important research challenge
due to its social cost and the impact of inadequate planning of high-quality services and …
due to its social cost and the impact of inadequate planning of high-quality services and …
[HTML][HTML] SIMCD: SIMulated crowd data for anomaly detection and prediction
A Bamaqa, M Sedky, T Bosakowski, BB Bastaki… - Expert Systems with …, 2022 - Elsevier
Smart Crowd management (SCM) solutions can mitigate overcrowding disasters by
implementing efficient crowd learning models that can anticipate critical crowd conditions …
implementing efficient crowd learning models that can anticipate critical crowd conditions …
Curb-gan: Conditional urban traffic estimation through spatio-temporal generative adversarial networks
Given an urban development plan and the historical traffic observations over the road
network, the Conditional Urban Traffic Estimation problem aims to estimate the resulting …
network, the Conditional Urban Traffic Estimation problem aims to estimate the resulting …
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 …
Urban traffic dynamics prediction—a continuous spatial-temporal meta-learning approach
Urban traffic status (eg, traffic speed and volume) is highly dynamic in nature, namely,
varying across space and evolving over time. Thus, predicting such traffic dynamics is of …
varying across space and evolving over time. Thus, predicting such traffic dynamics is of …
Forecasting pedestrian movements using recurrent neural networks: An application of crowd monitoring data
DC Duives, G Wang, J Kim - Sensors, 2019 - mdpi.com
Currently, effective crowd management based on the information provided by crowd
monitoring systems is difficult as this information comes in at the moment adverse crowd …
monitoring systems is difficult as this information comes in at the moment adverse crowd …
Self-supervised pre-training for robust and generic spatial-temporal representations
Advancements in mobile sensing, data mining, and artificial intelligence have revolutionized
the collection and analysis of Human-generated Spatial-Temporal Data (HSTD), paving the …
the collection and analysis of Human-generated Spatial-Temporal Data (HSTD), paving the …
A spatiotemporal hierarchical attention mechanism-based model for multi-step station-level crowd flow prediction
Y Zhou, J Li, H Chen, Y Wu, J Wu, L Chen - Information Sciences, 2021 - Elsevier
Multi-step station-level crowd flow prediction (Ms-SLCFP) is to predict the count of people
that would depart from or arrive at subway/bus/bike stations in multiple future consecutive …
that would depart from or arrive at subway/bus/bike stations in multiple future consecutive …
Sensing and forecasting crowd distribution in smart cities: Potentials and approaches
The possibility of sensing and predicting the movements of crowds in modern cities is of
fundamental importance for improving urban planning, urban mobility, urban safety, and …
fundamental importance for improving urban planning, urban mobility, urban safety, and …
Strans-gan: Spatially-transferable generative adversarial networks for urban traffic estimation
Conditional traffic estimation is a vital problem in urban plan deployment, which can help
evaluate urban construction plans and improve transportation efficiency. Conventional …
evaluate urban construction plans and improve transportation efficiency. Conventional …