相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。SRAI-LSTM: A Social Relation Attention-based Interaction-aware LSTM for human trajectory prediction
Yusheng Peng et al.
NEUROCOMPUTING (2022)
Spontaneous synchronization of motion in pedestrian crowds of different densities
Yi Ma et al.
NATURE HUMAN BEHAVIOUR (2021)
A Radar-Nearest-Neighbor based data-driven approach for crowd simulation
Xuedan Zhao et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2021)
Artificial neural network based modeling on unidirectional and bidirectional pedestrian flow at straight corridors
Xuedan Zhao et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2020)
An Intelligence-Based Approach for Prediction of Microscopic Pedestrian Walking Behavior
Yi Ma et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)
A data-driven neural network approach to simulate pedestrian movement
Xiao Song et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2018)
SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction
Hao Xue et al.
2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018) (2018)
Pedestrian trajectory prediction via the Social-Grid LSTM model
Bang Cheng et al.
JOURNAL OF ENGINEERING-JOE (2018)
Emergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models
Francisco Martinez-Gil et al.
SIMULATION MODELLING PRACTICE AND THEORY (2017)
An Artificial Intelligence-Based Approach for Simulating Pedestrian Movement
Yi Ma et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2016)
Pedestrian Behavior Understanding and Prediction with Deep Neural Networks
Shuai Yi et al.
COMPUTER VISION - ECCV 2016, PT I (2016)
A floor field cellular automaton for crowd evacuation considering different walking abilities
Zhijian Fu et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2015)
Strategies for simulating pedestrian navigation with multiple reinforcement learning agents
Francisco Martinez-Gil et al.
AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS (2015)
MARL-Ped: A multi-agent reinforcement learning based framework to simulate pedestrian groups
Francisco Martinez-Gil et al.
SIMULATION MODELLING PRACTICE AND THEORY (2014)
Understanding pedestrian crowd panic: a review on model organisms approach
Nirajan Shiwakoti et al.
JOURNAL OF TRANSPORT GEOGRAPHY (2013)
Transitions in pedestrian fundamental diagrams of straight corridors and T-junctions
J. Zhang et al.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2011)
Methods for measuring pedestrian density, flow, speed and direction with minimal scatter
B. Steffen et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2010)
New Insights into Pedestrian Flow Through Bottlenecks
Armin Seyfried et al.
TRANSPORTATION SCIENCE (2009)
Agent-based human behavior modeling for crowd simulation
Linbo Luo et al.
COMPUTER ANIMATION AND VIRTUAL WORLDS (2008)
Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study
M. Ballerini et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2008)
The fundamental diagram of pedestrian movement revisited
A Seyfried et al.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2005)
Self-organized queuing and scale-free behavior in real escape panic
C Saloma et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2003)
Simulation of pedestrian dynamics using a two-dimensional cellular automaton
C Burstedde et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2001)
Simulating dynamical features of escape panic
D Helbing et al.
NATURE (2000)