Mapping and Describing Geospatial Data to Generalize Complex Models: The Case of LittoSIM-GEN

For some scientific questions, empirical data are essential to develop reliable simulation models. These data usually come from different sources with diverse and heterogeneous formats. The design of complex data-driven models is often shaped by the structure of the data available in research projects. Hence, applying such models to other case studies requires either to get similar data or to transform new data to fit the model inputs. It is the case of agent-based models (ABMs) that use advanced data structures such as Geographic Information Systems data. We faced this problem in the LittoSIM-GEN project when generalizing our participatory flooding model (LittoSIM) to new territories. From this experience, we provide a mapping approach to structure, describe, and automatize the integration of geospatial data into ABMs

Mapping and Describing Geospatial Data to Generalize Complex Models: The Case of LittoSIM-GEN

For some scientific questions, empirical data are essential to develop reliable simulation models. These data usually come from different sources with diverse and heterogeneous formats. The design of complex data-driven models is often shaped by the structure of the data available in research projects. Hence, applying such models to other case studies requires either to get similar data or to transform new data to fit the model inputs. It is the case of agent-based models (ABMs) that use advanced data structures such as Geographic Information Systems data. We faced this problem in the LittoSIM-GEN project when generalizing our participatory flooding model (LittoSIM) to new territories. From this experience, we provide a mapping approach to structure, describe, and automatize the integration of geospatial data into ABMs

LittoSIM-GEN: exploiting GAMA features to simulate a serious game of flooding risk management

International audienceLittoSIM-GEN is a generic participatory simulation model composed of three agent-based models that allow playing a multirole serious game of flooding risk management. The central component of LittoSIM-GEN is the manager model where submersion events are calculated and displayed on a multidistrict study area. The player model offers a set of land use and coastal defense actions to distant playing teams (usually four) to manage their districts and mitigate the unpredictable flooding damage. The last model is the leader, which represents a state risk agency that supervises the game and pushes players towards collaborative and alternative strategies. The model creates a virtual environment for decision makers, urban planners, and risk managers to deal with different scenarios that they may confront in real world situations. Such experiences promote risk culture and raise awareness of the workshop participants who share their feedback and discuss their learning during the debriefing debate. Game animators use automatic and manual data collection, pre- and post-surveys, and a set of graphical indicators to report and assess the results of workshops. To implement such a realistic ludic game with relevant outcomes, LittoSIM-GEN makes use of multiple features of the GAMA platform, a modeling environment for developing agent-based simulations: - Reading and writing text files: besides exporting results as textual data for further analysis, the model reads multiple hierarchical configuration files during the initialization phase. This allows loading diverse territorial archetypes with different parameters, which makes LittoSIM-GEN a generic and dynamic model. - Processing geospatial data: loading and accessing vector and raster files is a simple task in GAMA. LittoSIM-GEN uses this feature to create a realistic environment by using empirical data, such as administrative boundaries, elevation models, and land cover databases. - Using ergonomic interfaces: GAMA allows developing user-friendly graphical interfaces to handle player actions. LittoSIM-GEN players can use tablets to make the game more playful. - Implementing large-scale models: agent-based models developed with GAMA can go up to millions of agents with the possibility of executing parallel processes to speed up calculus. LittoSIM-GEN uses large spatial grids to represent the territories and simulate inundations gradually. - Connecting to the network: participants can play LittoSIM-GEN as a remote game through GAMA primitives that allow serializing data and communicating over the network using shared messaging brokers such as Apache ActiveMQ. - Accessing the system command: GAMA can access the system prompt to execute any command or external program. This feature allows LittoSIM-GEN to use LISFLOOD-FP model to calculate the real extent of a submersion based on data of the study area. - Displaying graphical outputs: during the game, multiple real-time indicators and dynamic graphs show the current state of the territory. GAMA can visualize any relevant information on graphical displays, as well as saving these data to different output files (textual, vector, raster). Improving LittoSIM-GEN depends highly on future enhancements of the GAMA platform, particularly: - Portability of GAMA applications to allow the execution of models out of the platform. - Dynamic graphical components to handle more user interactions, such as built-in buttons and text boxes

Coupling agent-based simulation with optimization to enhance population sheltering

International audiencePopulation sheltering is a recurrent problem in crisis management that requires addressing two aspects: evacuating vulnerable people using emergency vehicles and regulating movements of pedestrians and individual vehicles towards shelters. While these aspects have received considerable attention in modeling and simulation literature, very few approaches consider them simultaneously. In this paper, we argue that Agent-Based Modeling and Simulation (ABMS) and Optimization are two complementary approaches that can address the problem of sheltering globally and efficiently and be the basis of coherent frameworks for decision-and policy-making. Optimization can build efficient sheltering plans, and ABMS can explore what-if scenarios and use geospatial data to display results within a realistic environment. To illustrate the benefits of a framework based on this coupling approach, we simulate actual flash flood scenarios using real-world data from the city of Trèbes in South France. Local authorities may use the developed tools to plan and decide on sheltering strategies, notably, when and how to evacuate depending on available time and resources

The need for optimization tools in agent-based models: a GAMA model for flood crisis management

International audienceThe integration of agent-based modeling and simulation into decision-making tools requires enhancing their realism by supporting intelligent optimized decisions [1]. However, optimization is not a native feature of agent-based models (ABMs), and it is necessary to combine them with existing optimization tools [4]. This complementary approach, combining geospatial simulation with optimization, allows simulators to support realistic decisions that might be necessary to address real-world problems. Such simulators can help test what-if scenarios and assess alternative strategies in different domains where decision-makers need optimized solutions.In this work, we present a simulator that couples an ABM with a Vehicle Routing Problem (VRP) optimizer [3]. This optimizer uses optimization heuristics that compute the best routes for a fleet of vehicles to minimize their total traveled distance [2]. We use this tool to optimize the evacuation of vulnerable people from risk areas during flood events. The ABM, implemented under the GAMA platform, simulates all the spatial and temporal dynamics (the environment, interactions between actors, and the evacuation process) and communicates with the VRP optimizer to obtain evacuation plans.We apply this model to the October 2018 flood scenario in the city of Trèbes (South of France). We use the GAMA platform in graphical mode to visualize and calibrate scenarios, then in headless mode to run multiple parallel simulations with different input parameters. The aggregation of these simulation outputs helps crisis managers decide on the best evacuation strategies according to several parameters: the number of available emergency vehicles, the location of evacuation centers, and the evolution of the flood hazard. This application shows that coupling ABMs with optimization tools is relevant in addressing real-world situations. We believe that integrating optimization tools into GAMA could ease the development of such applications

Coupling agent-based simulation with optimization to enhance population sheltering

International audiencePopulation sheltering is a recurrent problem in crisis management that requires addressing two aspects: evacuating vulnerable people using emergency vehicles and regulating movements of pedestrians and individual vehicles towards shelters. While these aspects have received considerable attention in modeling and simulation literature, very few approaches consider them simultaneously. In this paper, we argue that Agent-Based Modeling and Simulation (ABMS) and Optimization are two complementary approaches that can address the problem of sheltering globally and efficiently and be the basis of coherent frameworks for decision-and policy-making. Optimization can build efficient sheltering plans, and ABMS can explore what-if scenarios and use geospatial data to display results within a realistic environment. To illustrate the benefits of a framework based on this coupling approach, we simulate actual flash flood scenarios using real-world data from the city of Trèbes in South France. Local authorities may use the developed tools to plan and decide on sheltering strategies, notably, when and how to evacuate depending on available time and resources

Coupling agent-based simulation with optimization to enhance population sheltering

International audiencePopulation sheltering is a recurrent problem in crisis management that requires addressing two aspects: evacuating vulnerable people using emergency vehicles and regulating movements of pedestrians and individual vehicles towards shelters. While these aspects have received considerable attention in modeling and simulation literature, very few approaches consider them simultaneously. In this paper, we argue that Agent-Based Modeling and Simulation (ABMS) and Optimization are two complementary approaches that can address the problem of sheltering globally and efficiently and be the basis of coherent frameworks for decision-and policy-making. Optimization can build efficient sheltering plans, and ABMS can explore what-if scenarios and use geospatial data to display results within a realistic environment. To illustrate the benefits of a framework based on this coupling approach, we simulate actual flash flood scenarios using real-world data from the city of Trèbes in South France. Local authorities may use the developed tools to plan and decide on sheltering strategies, notably, when and how to evacuate depending on available time and resources