Virtual cityscapes: recent advances in crowd modeling and traffic simulation

We survey our recent work on interactive modeling, generation, and control of large-scale crowds and traffic for simulating digital cities. These include multi-agent navigation, simulating large crowds with emerging behaviors as well as interactive simulation of traffic on large road networks. We also highlight their performance on different scenarios.     

Learning behavior patterns from video for agent-based crowd modeling and simulation

This paper proposes a novel data-driven modeling framework to construct agent-based crowd model based on real-world video data. The constructed crowd model can generate crowd behaviors that match those observed in the video and can be used to predict trajectories of pedestrians in the same scenario. In the proposed framework, a dual-layer architecture is proposed to model crowd behaviors. The bottom layer models the microscopic collision avoidance behaviors, while the top layer models the macroscopic crowd behaviors such as the goal selection patterns and the path navigation patterns. An automatic learning algorithm is proposed to learn behavior patterns from video data. The learned behavior patterns are then integrated into the dual-layer architecture to generate realistic crowd behaviors. To validate its effectiveness, the proposed framework is applied to two different real world scenarios. The simulation results demonstrate that the proposed framework can generate crowd behaviors similar to those observed in the videos in terms of crowd density distribution. In addition, the proposed framework can also offer promising performance on predicting the trajectories of pedestrians.     

中观交通流建模与系统仿真研究综述*

在简要描述了宏微观交通流仿真的建模思路和仿真软件开发情况的基础上,重点对近四十年来中观交通流仿真建模的研究成果进行了总结和归类,并对不同中观仿真模型的优缺点进行了比较;最后指出了中观交通流建模与仿真的研究方向。     

基于Agent的空中交通系统建模与仿真研究

从系统特性、内在运行机制和外在行为表现等方面对空中交通系统进行了深入分析,提出基于离散事件和连续时间相结合的混合空中交通仿真模型。该模型采用Agent技术表现实现个体微观行为的仿真,集成个体微观行为构成系统宏观性能表现。构建了典型的空中交通系统的Agent模型：飞机Agent和管制员Agent。最后,通过一个起飞和落地的应用验证了基于Agent的空中交通混合仿真在模拟个体微观行为和系统宏观表现方面均具有较高的逼真度。     

A modeling framework for urban traffic systems microscopic simulation

In this paper a modeling framework for urban traffic systems (UTS) is presented. The model, used for agent based micro-simulation, describes both the traffic network and dynamic entities, namely vehicles, traffic lights, and pedestrians. The framework allows defining systematically the necessary components and their behavior of a model oriented to event driven simulation, which can be executed in a distributed way. In the model, the vehicles are conceived as mobile agents with decision making capabilities that interact with the environment and other entities within the traffic network, performing diverse activities according to numerous situations arisen during the simulation. A multi-level Petri net based formalism, named n-LNS is used for describing the structure of the UTS and the components behavior. The first level describes the traffic network; the second level models the behavior of diverse road network users considered as agents, and the third level specifies detailed procedures performed by the agents, namely travel plans, tasks, etc.     

A review of recent advances in learner and skill modeling in intelligent learning environments

In recent years, learner models have emerged from the research laboratory and research classrooms into the wider world. Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users. Probabilistic models for skill assessment are playing a key role in these advanced learning environments. In this paper, we review the learner models that have played the largest roles in the success of these learning environments, and also the latest advances in the modeling and assessment of learner skills. We conclude by discussing related advancements in modeling other key constructs such as learner motivation, emotional and attentional state, meta-cognition and self-regulated learning, group learning, and the recent movement towards open and shared learner models.     

Towards more behaviours in crowd simulation

While collision avoidance has been the most active topic in pedestrian simulation, the modelling of other kinds of behaviours appears to be essential for better realism. Thus higher cognitive levels of perception and behaviour improve simulation quality. Furthermore, giving an agent the possibility to choose the nature of its interactions with the others can not only improve simulation realism but also bring heterogeneity in the simulated population because each agent individually perceives the situation according to its own characteristics. In this paper, we aim at providing the pedestrian agent the ability to obtain an individual representation of the environment that allows him to adapt its behaviour according to the situation. We base our work on the analysis and interpretation of the environment, which makes the agent decide the behaviour it is going to adopt. We focus on two kinds of behaviours, following and group avoidance behaviours, and on their integration in classical avoidance simulations. We integrate recent works about following behaviour and propose to model interactions directly with groups of people instead of individuals. We aim at providing perception rules totally independent from the collision avoidance model used in the simulation. Because of the improved perception process, we observe emerging speed waves, group behaviours and lane formation in our simulations. Our results demonstrate the interest of modelling such behaviours to obtain more realistic simulations and show that specific patterns and collective behaviours emerge when using several types of behaviours in simulations. Copyright © 2015 John Wiley & Sons, Ltd.     

Cuckoo search: recent advances and applications

Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and the same has been found to be efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally, we propose some important topics for further research.     

Industrial geometry: recent advances and applications in CAD

Industrial Geometry aims at unifying existing and developing new methods and algorithms for a variety of application areas with a strong geometric component. These include CAD, CAM, Geometric Modelling, Robotics, Computer Vision and Image Processing, Computer Graphics and Scientific Visualization. In this paper, Industrial Geometry is illustrated via the fruitful interplay of the areas indicated above in the context of novel solutions of CAD related, geometric optimization problems involving distance functions: approximation with general B-spline curves and surfaces or with subdivision surfaces, approximation with special surfaces for applications in architecture or manufacturing, approximate conversion from implicit to parametric (NURBS) representation, and registration problems for industrial inspection and 3D model generation from measurement data. Moreover, we describe a ‘feature sensitive’ metric on surfaces, whose definition relies on the concept of an image manifold, introduced into Computer Vision and Image Processing by Kimmel, Malladi and Sochen. This metric is sensitive to features such as smoothed edges, which are characterized by a significant deviation of the two principal curvatures. We illustrate its applications at hand of feature sensitive curve design on surfaces and local neighborhood definition and region growing as an aid in the segmentation process for reverse engineering of geometric objects.     

Modeling group structures in pedestrian crowd simulation

Grouping is a common phenomenon in pedestrian crowds and plays important roles in affecting crowd behavior. Group modeling is still an open challenging problem and has not been incorporated by existing crowd simulation models. Motivated by the need of group modeling for crowd behavior simulation, this paper presents a unified and well-defined framework for modeling the structure aspect of different groups in pedestrian crowds. Both intra-group structure and inter-group relationships are considered and their effects on the crowd behavior are modeled. Based on this framework, an agent-based crowd simulation system is developed and crowd behavior simulations using two different group structures are presented. The simulation results show that the developed framework allows different group structures to be easily modeled. Besides, different group sizes, intra-group structures and inter-group relationships can have significant impacts on crowd behaviors.     

Large-scale video compression: recent advances and challenges

The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent compression technologies are highly desired to facilitate the storage and transmission of these tremendous video data for a wide variety of applications. In this paper, a systematic review of the recent advances for large-scale video compression (LSVC) is presented. Specifically, fast video coding algorithms and effective models to improve video compression efficiency are introduced in detail, since coding complexity and compression efficiency are two important factors to evaluate video coding approaches. Finally, the challenges and future research trends for LSVC are discussed.     

Parallel metaheuristics: recent advances and new trends

The field of parallel metaheuristics is continuously evolving as a result of new technologies and needs that researchers have been encountering. In the last decade, new models of algorithms, new hardware for parallel execution/communication, and new challenges in solving complex problems have been making advances in a fast manner. We aim to discuss here on the state of the art, in a summarized manner, to provide a solution to deal with some of the growing topics. These topics include the utilization of classic parallel models in recent platforms (such as grid/cloud architectures and GPU/APU). However, porting existing algorithms to new hardware is not enough as a scientific goal, therefore researchers are looking for new parallel optimization and learning models that are targeted to these new architectures. Also, parallel metaheuristics, such as dynamic optimization and multiobjective problem resolution, have been applied to solve new problem domains in past years. In this article, we review these recent research areas in connection to parallel metaheuristics, as well as we identify future trends and possible open research lines for groups and PhD students.     

An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster

A key activity in emergency management is planning and preparation for disaster. If the right safety measures are implemented beforehand, harmful effects can be significantly mitigated. However, evaluation and selection of effective measures is difficult due to the numerous scenarios that exist in most emergency environments coupled with the high associated cost of testing such scenarios. An agent-based system employs a computational model of autonomous interacting agents in an environment with the purpose of assessing the emergent behavior of the group. This paper presents a prototype of a computer simulation and decision support system that uses agent-based modeling to simulate crowd evacuation in the presence of a fire disaster and provides for testing of multiple disaster scenarios at virtually no cost. The prototype is unique in the current literature as it is specifically designed to simulate a concert venue setting such as a stadium or auditorium and is highly configurable allowing for user definition of concert venues with any arrangement of seats, pathways, stages, exits, and people as well as the definition of multiple fires with fire and smoke dynamics included.     

An integration of enhanced social force and crowd control models for high-density crowd simulation

Neural Computing and Applications - Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to...     

KUMBH MELA: a case study for dense crowd counting and modeling

Dense crowd counting and modeling at different gatherings has ignited a new flame in the visual surveillance research community. There is a high possibility of mishappenings in the form of stampede, mob fighting at these gatherings and the administration is helpless in these scenarios. There is a requirement of analyzing the crowd to prevent these dangerous situations. The proposed work is a case study of Kumbh Mela which models the crowd counting in densely populated images. In the proposed work, the orthographic projection of the crowd is captured using a camera attached to a drone, to reduce the effect of occlusion and scaling which, otherwise, may get introduce during image acquisition process. The captured data is fed to a Convolutional Neural Network for training the model to count head of persons present in the frame. The results obtained from the trained model are validated using geometry and imaging techniques. The proposed model has achieved a mean-absolute-error of 94.3 and a mean-squared-error of 104.6 which seems to outperform the existing state-of-the-art models with respect to the reported performance parameters. The proposed model can be used as a viable solution in applications related to modeling the crowd behavior.

     

Model-driven agent-based simulation development: A modeling language and empirical evaluation in the adaptive traffic signal control domain

Model-driven development (MDD) is an approach for supporting the development of software systems, in which high-level modeling artifacts drive the production of time and effort-consuming low-level artifacts, such as the source code. Previous studies of the MDD effectiveness showed that it significantly increases development productivity, because the development effort is focused on the business domain rather than technical issues. However, MDD was exploited in the context of agent-based development in a limited way, and most of the existing proposals demonstrated the effectiveness of using MDD in this context by argumentation or examples, lacking disciplined empirical analyses. In this paper, we explore the use of MDD for agent-based modeling and simulation in the adaptive traffic signal control (ATSC) domain, in which autonomous agents are in charge of managing traffic light indicators to optimize traffic flow. We propose an MDD approach, composed of a modeling language and model-to-code transformations for producing runnable simulations automatically. In order to analyze the productivity gains of our MDD approach, we compared the amount of design and implementation artifacts produced using our approach and traditional simulation platforms. Results indicate that our approach reduces the workload to develop agent-based simulations in the ATSC domain.     

A simulation framework for modeling urban freight operations impacts on traffic networks

This paper presents a traffic simulation framework to reproduce urban freight movements, particularly concerning double-parked delivery operations. Since freight movements affect traffic and vice versa, we propose a hybrid framework that simulates traffic phenomena macroscopically and, at the same time, allows tracking delivery vehicles along their routes throughout the entire simulation. The traffic simulation framework is based on the Lighthill–Whitham–Richards macroscopic model as well as the theory of bottlenecks. The traffic component of the model can be coupled with a generic parking model. Since a novel faster version of the Lax-Hopf Formula is used in the traffic simulation, the proposed framework can perform efficient simulations. Because of this hybrid nature, the framework is suitable for simulations of large scenarios and for evaluations of City Logistic measures to tackle the last-mile problem. We show this in the second part of the study with two different measures: shifting delivery operations to off-peak hours, and prohibiting deliveries on critical streets. While the benefits deriving from the first strategy are evident, the effects of the second one are less clear because of the complexity of network interactions.     

Cartilage proteomics: Challenges, solutions and recent advances

The mammalian skeleton is largely composed of cartilage and bone. The major functions of cartilage are first to provide a transient template for development of the axial and appendicular skeleton and secondly to provide permanent articulating joint surfaces. The unique cartilage extracellular matrix (ECM) is essential for the load-bearing and viscoelastic properties of cartilage tissues. Maintained by the chondrocytes, the ECM contains a myriad of proteins and proteoglycans organized into precise networks. Many cartilage disorders result from genetic disruption of cartilage ECM components, their interactions and/or degradation. Although technically challenging, the proteomic analysis of cartilage in development and disease is now emerging as a clinically important research area. In this article, we will review progress in the proteomic characterization of cartilage-related samples.     

ACMICS: an agent communication model for interacting crowd simulation

Behavioral plausibility is one of the major aims of crowd simulation research. We present a novel approach that simulates communication between the agents and assess its influence on overall crowd behavior. Our formulation uses a communication model that tends to simulate human-like communication capability. The underlying formulation is based on a message structure that corresponds to a simplified version of Foundation for Intelligent Physical Agents Agent Communication Language Message Structure Specification. Our algorithm distinguishes between low- and high-level communication tasks so that ACMICS can be easily extended and employed in new simulation scenarios. We highlight the performance of our communication model on different crowd simulation scenarios. We also extend our approach to model evacuation behavior in unknown environments. Overall, our communication model has a small runtime overhead and can be used for interactive simulation with tens or hundreds of agents.