GAMA Platform Research Articles

Agent-based simulation of non-urgent egress from mass events in open public spaces

Public mass events require thorough planning on allocating resources such as paramedics, police officers, urban cleaning teams, and their equipment (ambulances, patrol cars, garbage collection trucks, and other urban cleaning vehicles). Testing different scenarios of event venue layout and crowd behavior at the end of an event might be useful to plan the event and said resource allocation.Our main objective is to model the non-urgent egress of participants at the end of an event, with possible applications for event management. That is when some resources are released (police and paramedics) and others are requested (urban cleaning teams).Using the agent-based GAMA platform, we implemented a spatially explicit simulation model upon an extension of the Social Force Model that considers group behavior, and a novel implementation of the “social retention” phenomenon, to simulate non-urgent egress from public space mass gathering events. Focus groups with architecture, geography, and urban ergonomics experts were conducted for face validation and improvement of the model.We present the outcome of a series of simulations of a scenario mimicking a real-life music event that took place in a square in downtown Lisbon, Portugal. Cell phone data captured during the event was used to calibrate the model. We analyzed model performance when the number of pedestrian agents increases, to assess the feasibility of using our approach in participatory discussions with stakeholders responsible for resources management.On average, the egress evolution obtained in the simulations fit well with the evolution of cell phone counts captured during the event. The behavior of groups of agents evidenced real-life phenomena, such as the persistence of group cohesion and repulsion interactions (both with architectural obstacles and other agents).Model performance degradation with the increasing number of agents may hamper the usage of this model/platform for participatory meetings, due to the incurred delay in obtaining results. To mitigate this problem, we plan to explore parallelization strategies for agent-based simulation, such as using GPUs.

An agent architecture for expressive spatial knowledge and reasoning in land use modeling and simulations

This paper presents a new cognitive agent design approach integrating spatial knowledge representation and reasoning in agent-based modeling dedicated to land use simulations. A deep motivation for our agent-centric contribution is the ever-increasing development of spatially explicit agent simulation platforms. We build on this technological evolution and topology theory to endow the agent with human’s spatial representation and reasoning following a Belief–Desire–Intention architecture. A pilot implementation of the methodology with simulation experiments on a hunting model was carried out in GAMA platform to assess agent performances. Simulations display a consistent trend of animal population dynamics and also confirm a high model sensitivity to the integration of spatial knowledge and reasoning in agent-based models of human actor. These results demonstrate a successful implementation and the importance of spatial dimension in the expressive power and the validity of agent-based models. Future research efforts should therefore emphasize on designing human knowledge representation and incorporating learning abilities to improve models efficiency.

A simple agent-based model for planning for bicycling: Simulation of bicyclists' movements in urban environments

Bicycling can improve the sustainability and liveability of cities, many of which desperately require better active transport infrastructure. Urban and transport planners need to examine how improvements in infrastructure change bicyclists' behaviour. With this knowledge, investment in bicycling networks can be more efficient and encourage the use of bicycling for transportation. This study developed a simple Agent-Based Model (ABM) to simulate bicyclists' movements in response to the built environment and road network characteristics in the City of Penrith, in the Greater Sydney Area, Australia. In this case study, the GAMA platform was used to build the ABM and Strava and Riderlog data were used to calibrate and validate the model. The model outputs give insights into bicyclist movements through the road network. The incorporated built environment characteristics include the type of bicycling infrastructure, tree canopy, slope, land use mix, and vehicle traffic. These choice factors also allowed the computation of rider levels of comfort and safety on each trip. Potential refinements of the model include additional bicycling behaviour factors (such as aesthetic preferences), and bicyclists' interactions with each other and other modes of transport.

Dealing with mixed and non-normative traffic. An agent-based simulation with the GAMA platform.

Continuous improvement in computing power allowed for an increase of the scales micro-traffic models can be used at. Among them, agent-based frameworks are now appropriate for studying ordinary traffic conditions at city-scale, but remain difficult to adapt, especially for non-computer scientists, to more specific application contexts (e.g., car accidents, evacuation following a natural disaster), that require integrating particular behaviors for the agents. In this paper, we present a built-in model integrated into the GAMA open-source modeling and simulation platform, allowing the modeler to easily define traffic simulations with a detailed representation of the driver's operational behaviors. In particular, it allows modelling road infrastructures and traffic signals, change of lanes by driver agents and less normative traffic mixing car and motorbike as in some South East Asian countries. Moreover, the model allows to carry out city-level simulations with tens of thousands of driver agents. An experiment carried out shows that the model can accurately reproduce the traffic in Hanoi, Vietnam.

MCO Plan: Efficient Coverage Mission for Multiple Micro Aerial Vehicles Modeled as Agents

Micro aerial vehicle (MAV) fleets have gained essential recognition in the decision schemes for precision agriculture, disaster management, and other coverage missions. However, they have some challenges in becoming massively deployed. One of them is resource management in restricted workspaces. This paper proposes a plan to balance resources when considering the practical use of MAVs and workspace in daily chores. The coverage mission plan is based on five stages: world abstraction, area partitioning, role allocation, task generation, and task allocation. The tasks are allocated according to agent roles, Master, Coordinator, or Operator (MCO), which describe their flight autonomy, connectivity, and decision skill. These roles are engaged with the partitioning based on the Voronoi-tessellation but extended to heterogeneous polygons. The advantages of the MCO Plan were evident compared with conventional Boustrophedon decomposition and clustering by K-means. The MCO plan achieved a balanced magnitude and trend of heterogeneity between both methods, involving MAVs with few or intermediate resources. The resulting efficiency was tested in the GAMA platform, with gained energy between 2% and 10% in the mission end. In addition, the MCO plan improved mission times while the connectivity was effectively held, even more, if the Firefly algorithm generated coverage paths.

Combining Telecom Data with Heterogeneous Data Sources for Traffic and Emission Assessments—An Agent-Based Approach

To create quality decision-making tools that would contribute to transport sustainability, we need to build models relying on accurate, timely, and sufficiently disaggregated data. In spite of today’s ubiquity of big data, practical applications are still limited and have not reached technology readiness. Among them, passively generated telecom data are promising for studying travel-pattern generation. The objective of this study is twofold. First, to demonstrate how telecom data can be fused with other data sources and used to feed up a traffic model. Second, to simulate traffic using an agent-based approach and assess the emission produced by the model’s scenario. Taking Novi Sad as a case study, we simulated the traffic composition at 1-s resolution using the GAMA platform and calculated its emission at 1-h resolution. We used telecom data together with population and GIS data to calculate spatial-temporal movement and imported it to the ABM. Traffic flow was calibrated and validated with data from automatic vehicle counters, while air quality data was used to validate emissions. The results demonstrate the value of using diverse data sets for the creation of decision-making tools. We believe that this study is a positive endeavor toward combining big data and ABM in urban studies.

Human Dynamic Behavior: Reconstruction Trajectories Using CDRs

Investigating the human mobility patterns is a highly interesting field in the 21th century, and it takes vast attention from multi-disciplinary scientists in physics, economic, social, computer, engineering…etc. depending on the concept that relates between human mobility patterns and their communications. Hence, the necessity for a rich repository of data has emerged. Therefore, the most powerful solution is the usage of GSM network data, which gives millions of Call Details Records gained from urban regions. However, the available data still have shortcomings, because it gives only the indication of spatio-temporal data at only the moment of mobile communication activities. In this chapter, Gama platform development environment of constructing spatially multi agent simulation is used for modeling and simulating the individual reconstructed trajectories. As a result, the individual trajectories revealed the human daily life patterns.

Water Resources Analysis: An Approach based on Agent-Based Modeling

The paper aims to present a river basin modeling using GAMA platform for water resources analysis. Currently, several models based on multi-agent systems are used for natural resources management and they present satisfactory results for this type of scenario. GAMA is agents based and widely used in this context with several studies already published. In this study, the Sa ̃o Gonc ̧alo and Lagoa Mirim basins were considered from georeferenced data. In the modeling, regions, and rivers are agents on the system where rivers water can flow among neighbors regions.

ENHANCED SAFE CONDUCT AND PREPARATION FOR EFFECTIVE EVACUATION (ESCAPEE) USING GAMA

Hospitals are essential structures with the purpose of providing healthcare and assistance especially during disasters. These facilities are also prone to fire due to the presence of electrical equipment, oxygen tanks, chemicals, and other flammable materials. In an event of a fire, the layout of the hospital must be able to provide an effective evacuation. In this paper, the evacuation of ten emergency rooms (ER) was simulated. The simulation for the rooms utilized an agent-based model (ABM) by using GAMA Platform. Varying densities were simulated per ER and a fire simulation was done. The simulations resulted to total evacuation time for the different ERs and the mortality rate considering a fire inside the room. The results show that the number of occupants affected the evacuation time by 10 to 20 seconds. In addition, there is also a weak correlation between the area of the ER and the evacuation time. Based on observation of the simulations, the location of the exits, the number of exits and the layout of the emergency rooms play a significant role in safe evacuation.

The application of role-playing games and agent-based modelling to the collaborative water management in peri-urban communities

ABSTRACT The environmental systems associated with water resources management have always been represented through physical factors and processes. In order to consider the social system coupled with the environmental system, a new modeling approach emerges, the agent-based modeling. Two steps are essential to build agent-based models-ABMs. The first step requires the agent behavior understanding and considers the influence of external factors in the agent decision-making. The second one is the system representation through an ABM. In this perspective, the WaDiGa water management role-playing game-RPG was applied to map the rural producers' behavior of a peri-urban region. In addition, an ABM was developed in the GAMA platform intended to represent the agents’ behavior and its consequences in the environment configuration. After applying the proposed methodology, it was concluded that the WaDiGa was able to identify factors that influence the local agents' decision-making, and that the game fulfills the function of a dialogue platform on the water resource theme, and could be used as a tool for environmental education and socio-hydrological modeling. The ABM developed was able to represent the relationship between the agricultural production of the community and the water use, presenting results similar to the results obtained in the WaDiGa.

Participatory Modeling and Simulation with the GAMA Platform

In recent years, agent-based simulation has become an important tool to study complex systems. However, the models produced are rarely used for decision-making support because stakeholders are often not involved in the modeling and simulation processes. Indeed, while several tools dedicated to participatory modeling and simulation exist, these are limited to the design of simple KISS models, thus reducing their po-tential impact. In this article, we present the new participatory tools integrated within the GAMA modeling and simulation platform. These tools, which take advantage of the GAMA platform with respect to the definition of rich KIDS models, allow the modelers to build models graphically and develop distributed serious games easily. Several application examples illustrate their use and potential.

Building, composing and experimenting complex spatial models with the GAMA platform

The agent-based modeling approach is now used in many domains such as geography, ecology, or economy, and more generally to study (spatially explicit) socio-environmental systems where the heterogeneity of the actors and the numerous feedback loops between them requires a modular and incremental approach to modeling. One major reason of this success, besides this conceptual facility, can be found in the support provided by the development of increasingly powerful software platforms, which now allow modelers without a strong background in computer science to easily and quickly develop their own models. Another trend observed in the latest years is the development of much more descriptive and detailed models able not only to better represent complex systems, but also answer more intricate questions. In that respect, if all agent-based modeling platforms support the design of small to mid-size models, i.e. models with little heterogeneity between agents, simple representation of the environment, simple agent decision-making processes, etc., very few are adapted to the design of large-scale models. GAMA is one of the latter. It has been designed with the aim of supporting the writing (and composing) of fairly complex models, with a strong support of the spatial dimension, while guaranteeing non-computer scientists an easy access to high-level, otherwise complex, operations. This paper presents GAMA 1.8, the latest revision to date of the platform, with a focus on its modeling language and its capabilities to manage the spatial dimension of models. The capabilities of GAMA are illustrated by the presentation of applications that take advantage of its new features.

A multi-agent framework for cloud-based management of collaborative robots

This article presents a cloud-based multi-agent architecture for the intelligent management of aerial robots in a disaster response situation. In a disaster scenario, a team of highly maneuverable quadcopters is deployed to carry out surveillance and decision support in disaster-affected areas. In Pakistan, such events usually result from sudden unpredictable calamities such as earthquakes. The aim of this work is to develop a robust mechanism to autonomously manage and react to sensory inputs received in soft real time from an unstructured environment. The immediate goal is to locate the maximum number of trapped, injured people within a large area, and help first responders plan rescue activities accordingly. To evaluate the proposed framework, a number of simulations are carried out using GAMA platform to emulate a disaster environment. Subsequently, algorithms are developed to survey an affected geographical area through the use of small flight drones. The key challenges in this work are related to the combination of the domains of multi-agent technology, robotics, and cloud computing for effectively bridging the cyber world with the physical world. Therefore, the proposed work demonstrates the effective use of a limited number of drones to capture inputs from a disaster situation in the physical world, and such inputs are used for timely planning of rescue efforts. The results of fixed resource assignment are compared with the proposed reactive assignment strategy, and it clearly shows a significant improvement in terms of resource usage compared to traditional approach.

Multiagent Hybrid Architecture for Collaborative Exchanges between Communicating Vehicles in an Urban Context

Nowadays, we are more and more surrounded by powerful and intelligent communicating objects. Many of these objects, as smart- phones, watches, detectors and soon cars, are moving in increasingly interconnected environments, have abilities to communicate with each other and to exchange information. A collaborative approach allows these entities to exchange information and objectives and to implement rules in a structured manner in order to optimize the execution of their own mission and, therefore, the operation of the system in general. For example, collaborative behaviours and informations exchanges could improve the movement of vehicles in an urban center and avoid traffic jams. Our contribution puts a stress on a definition of collaboration in the context of mobile communicating entities. For the sake of agent-based modeling, we also list challenges raised like technical architecture and data organisation. Then we propose an hybrid architecture for collaborative exchanges with an example based on communicating vehicles in an urban context and implemented on the GAMA platform.

3DGIS-Based Multi-Agent Geosimulation and Visualization of Building Evacuation Using GAMA Platform

Abstract. Recent GIS applications have already extended analyses from the traditional 2-2.5D environment (x,y,attributes) to 3D space (x,y,z,attributes). Coupled with agent-based modeling (ABM), available 3DGIS data can be used to develop simulation models for improved analysis of spatial data and spatial processes. One such application is on building evacuation for which ABM is integrated with 3D indoor spatial data to model human behavior during evacuation events and simulate evacuation scenarios visualized in 3D. The research presented in this paper develops a multi-agent geosimulation model for building evacuation, integrating 3DGIS dataset of the case study building as input in ABM using the GAMA simulation platform. This model is intended to complement and improve traditional approaches in building evacuation planning and management such as earthquake and fire drills. The initial model developed includes PEOPLE agents to model the building occupants, and FLOORS, ROOMS, INDOOR_PATHS and EXIT_POINTS agents, which are modeled from the 3DGIS layers. The INDOOR_PATHS and EXIT_POINTS agents influence the movement of PEOPLE agents. Test simulations were performed involving PEOPLE agents placed in rooms of the building based on potential number of occupants computed based from the floor area of each room. The PEOPLE agents are programmed to find the shortest path along the INDOOR_PATHS towards the EXIT_POINTS instance designated for each room of the building. The simulation computes for the total time it takes for all PEOPLE agents to exit the building.

Multi-agent Architecture with Space-time Components for the Simulation of Urban Transportation Systems

Transportation systems are increasingly complex and must evolve to incorporate components of sustainable development. It has become appropriate to develop high-level simulation tools for policy makers so that they can analyze the potential consequences of their choices. In this paper, we propose a decision-maker simulator intended to define and tune urban transportation policy (travel, parking and transportation strategies). In terms of system architecture, we adopted a “system of systems” approach, mainly structured in layers, in order to model the main elements of the system. We represent explicitly, for example, a layer of roads, lights, parking, means of transport, etc. Our system uses an agent-based simulation incorporating spatial and temporal information. It must support the regulatory scenarios to simulate the effect of regulatory strategies on transportation systems. The paradigm of multi-agent systems is well suited for simulating the behavior of the components of an urban transportation system. Existing applications address problems of individual behavior management, management of traffic flows, management of temporal and geographical aspects, multi-modality transport, etc. However, they don’t address organizational aspects of transportation systems that incorporate the adaptability of users’ behavior. In addition, we implemented the mechanism of “traces”; the trace files contain the result of simulation. Travel surveys, census and traffic measurements were used. Analysis of available data and traces were used to evaluate the suitability of our simulations according to different regulatory strategies. Finally, we implemented a prototype for the movement of people in the city of La Rochelle from statistical data of INSEE (the French National Institute of Statistics and Economic Studies) and the BD TOPO 2 of the IGN (the French National Geographical Institute) using the GAMA platform.