Trajectory prediction has been widely pursued in many fields, and many model-based and
model-free methods have been explored. The former include rule-based, geometric or …

To simulate the low-level ('microscopic') behavior of human crowds, a local navigation
algorithm computes how a single person ('agent') should move based on its surroundings …

Most of current crowd simulation methods have considered the impact of interindividual
emotion on the agent's behavior pattern during emergency evacuations. However, the …

Data-driven crowd modeling has now become a popular and effective approach for
generating realistic crowd simulation and has been applied to a range of applications, such …

Federated Learning (FL) presents a novel approach within the domain of Machine Learning
(ML)—enabling the training of ML models in a distributed manner. This paradigmatic shift …

We propose a new semantic-level crowd evaluation metric in this paper. Crowd simulation
has been an active and important area for several decades. However, only recently has …

In the data mining of road networks, trajectory clustering of moving objects is of particular
interest for its practical importance in many applications. Most of the existing approaches to …

Crowd simulation is a central topic in several fields including graphics. To achieve high-
fidelity simulations, data has been increasingly relied upon for analysis and simulation …

In this article, we propose a framework for crowd behavior prediction in complicated
scenarios. The fundamental framework is designed using the standard encoder-decoder …

This paper presents a novel data-driven crowd simulation method that can mimic the
observed traffic of pedestrians in a given environment. Given a set of observed trajectories …