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Integrated Agent-based Modelling and Simulation of Transportation Demand and Mobility Patterns in Sweden
<h2>About</h2> <p><span>The Synthetic Sweden Mobility (SySMo) model provides a simplified yet statistically realistic microscopic representation of the real population of Sweden. The agents in this synthetic population contain socioeconomic attributes, household characteristics, and corresponding activity plans for an average weekday. This agent-based modelling approach derives the transportation demand from the agents’ planned activities using various transport modes (e.g., car, public transport, bike, and walking).</span></p> <div> <p>This open data repository contains four datasets: </p> <p>(1) Synthetic Agents, </p> </div> <div> <p>(2) Activity Plans of the Agents, </p> </div> <div> <p>(3) Travel Trajectories of the Agents, and </p> </div> <div> <p>(4) Road Network (EPSG: 3006)</p> <p><span>(OpenStreetMap data were retrieved on August 28, 2023, from https://download.geofabrik.de/europe.html, and GTFS data were retrieved on September 6, 2023 from https://samtrafiken.se/)</span></p> <p><span>The database can serve as input to assess the potential impacts of new transportation technologies, infrastructure changes, and policy interventions on the mobility patterns of the Swedish population.</span></p> </div> <h2>Methodology</h2> <p>This dataset contains statistically simulated 10.2 million agents representing the population of Sweden, their socio-economic characteristics and the activity plan for an average weekday. For preparing data for the MATSim simulation, we randomly divided all the agents into 10 batches. Each batch's agents are then simulated in MATSim using the multi-modal network combining road networks and public transit data in Sweden using the package pt2matsim (https://github.com/matsim-org/pt2matsim). </p> <p>The agents' daily activity plans along with the road network serve as the primary inputs in the MATSim environment which ensures iterative replanning while aiming for a convergence on optimal activity plans for all the agents. Subsequently, the individual mobility trajectories of the agents from the MATSim simulation are retrieved.</p> <p>The activity plans of the individual agents extracted from the MATSim simulation output data are then further processed. All agents with negative utility score and negative activity time corresponding to at least one activity are filtered out as the ‘infeasible’ agents. The dataset ‘<strong>Synthetic Agents</strong>’ contains all synthetic agents regardless of their <span>‘<em>feasibility</em>’ (0=excluded & 1=included in plans and trajectories). In the other datasets, only agents with feasible activity plans are included. </span></p> <p>The simulation setup adheres to the MATSim 13.0 benchmark scenario, with slight adjustments. The strategy for replanning integrates BestScore (60%), TimeAllocationMutator (30%), and ReRoute (10%)— the percentages denote the proportion of agents utilizing these strategies. In each iteration of the simulation, the agents adopt these strategies to adjust their activity plans. The "BestScore" strategy retains the plan with the highest score from the previous iteration, selecting the most successful strategy an agent has employed up until that point. The "TimeAllocationMutator" modifies the end times of activities by introducing random shifts within a specified range, allowing for the exploration of different schedules. The "ReRoute" strategy enables agents to alter their current routes, potentially optimizing travel based on updated information or preferences. These strategies are detailed further in W. Axhausen et al. (2016) work, which provides comprehensive insights into their implementation and impact within the context of transport simulation modeling. </p> <h2>Data Description</h2> <h3>(1) Synthetic Agents</h3> <p>This dataset contains all agents in Sweden and their socioeconomic characteristics. </p> <p>The attribute ‘<span><em>feasibility</em></span>’ has two categories: <em>feasible</em><em> agents </em>(73%), and <em>infeasible agents</em> (27%). <span>Infeasible agents are agents with negative utility score and negative activity time corresponding to at least one activity.</span> </p> <p>File name: 1_syn_pop_all.parquet</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>PId</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td>Deso</td> <td>Zone code of Demographic statistical areas (DeSO)<sup>1</sup></td> <td>String</td> <td>-</td> </tr> <tr> <td> <pre>kommun</pre> </td> <td>Municipality code</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>marital </pre> </td> <td>Marital Status (single/ couple/ child)</td> <td>String</td> <td>-</td> </tr> <tr> <td> <pre>sex </pre> </td> <td>Gender (0 = Male, 1 = Female)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>age</pre> </td> <td>Age</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>HId</pre> </td> <td>A unique identifier for households</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>HHtype </pre> </td> <td>Type of households (single/ couple/ other)</td> <td>String</td> <td>-</td> </tr> <tr> <td> <pre>HHsize </pre> </td> <td>Number of people living in the households</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>num_babies</pre> </td> <td>Number of children less than six years old in the household</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>employment</td> <td>Employment Status (0 = Not Employed, 1 = Employed)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>studenthood</td> <td>Studenthood Status (0 = Not Student, 1 = Student)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>income_class</td> <td>Income Class (0 = No Income, 1 = Low Income, 2 = Lower-middle Income, 3 = Upper-middle Income, 4 = High Income)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>num_cars</td> <td>Number of cars owned by an individual </td> <td>Integer</td> <td>-</td> </tr> <tr> <td>HHcars</td> <td>Number of cars in the household</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>feasibility</pre> </td> <td>Status of the individual (1=feasible, 0=infeasible)</td> <td>Integer</td> <td>-</td> </tr> </tbody> </table> <p>1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a></p> <h3>(2) Activity Plans of the Agents</h3> <p>The dataset contains the car agents’ (agents that use cars on the simulated day) activity plans for a simulated average weekday. </p> <p>File name: <span>2_plans_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)</span></p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>act_purpose</p> </td> <td> <p>Activity purpose (work/ home/ school/ other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>PId</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>act_end </p> </td> <td> <p>End time of activity (0:00:00 – 23:59:59)</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:seco</p> <p>nd</p> </td> </tr> <tr> <td> <p>act_id</p> </td> <td> <p>Activity index of each agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>mode</p> </td> <td> <p>Transport mode to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>POINT_X </p> </td> <td> <p>Coordinate X of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>metre</p> </td> </tr> <tr> <td> <p>POINT_Y</p> </td> <td> <p>Coordinate Y of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>metre</p> </td> </tr> <tr> <td> <p>dep_time </p> </td> <td> <p>Departure time (0:00:00 – 23:59:59)</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:seco</p> <p>nd</p> </td> </tr> <tr> <td> <p>score</p> </td> <td> <p>Utility score of the simulation day as obtained from MATSim</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>trav_time </p> </td> <td> <p>Travel time to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:seco</p> <p>nd</p> </td> </tr> <tr> <td> <p>trav_time_min </p> </td> <td> <p>Travel time in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_time </p> </td> <td> <p>Activity duration in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>distance</p> </td> <td> <p>Travel distance between the origin and the destination</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>speed</p> </td> <td> <p>Travel speed to reach the activity location</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> </tbody> </table> <h3>(3) Travel Trajectories of the Agents</h3> <p>This dataset contains the driving trajectories of all the agents on the road network, <span>and the public transit vehicles used by these agents, including buses, ferries, trams etc. The files are produced by MATSim simulations and organised into 10 *.parquet’ files (representing different batches of simulation) corresponding to each plan file.</span></p> <p>File name: <span>3_events_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)</span></p> <p> </p> <table> <tbody> <tr> <td> <div> <div> <p><strong>Column </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Description </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Data type </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Unit </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>time </p> </div> </div> </td> <td> <div> <div> <p>Time in second in a simulation day (0-86399) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>second </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type </p> </div> </div> </td> <td> <div> <div> <p>Event type defined by MATSim simulation* </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>person </p> </div> </div> </td> <td> <div> <div> <p>Agent ID </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>link </p> </div> </div> </td> <td> <div> <div> <p>Nearest road link consistent with the road network </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>vehicle </p> </div> </div> </td> <td> <div> <div> <p>Vehicle ID identical to person </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>from_node </p> </div> </div> </td> <td> <div> <div> <p>Start node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>to_node </p> </div> </div> </td> <td> <div> <div> <p>End node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> <p>* One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart) </p> <h3>(4) Road Network</h3> <p>This dataset contains the road network.</p> <p>File name: 4_network.shp</p> <table> <tbody> <tr> <td> <div> <div> <p><strong>Column </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Description </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Data type </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Unit </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>length </p> </div> </div> </td> <td> <div> <div> <p>The length of road link </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>metre </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>freespeed </p> </div> </div> </td> <td> <div> <div> <p>Free speed </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km/h </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>capacity </p> </div> </div> </td> <td> <div> <div> <p>Number of vehicles </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>permlanes </p> </div> </div> </td> <td> <div> <div> <p>Number of lanes </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>oneway </p> </div> </div> </td> <td> <div> <div> <p>Whether the segment is one-way (0=no, 1=yes) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>modes </p> </div> </div> </td> <td> <div> <div> <p>Transport mode </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>from_node </p> </div> </div> </td> <td> <div> <div> <p>Start node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>to_node </p> </div> </div> </td> <td> <div> <div> <p>End node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>geometry </p> </div> </div> </td> <td> <div> <div> <p>LINESTRING (SWEREF99TM) </p> </div> </div> </td> <td> <div> <div> <p>geometry </p> </div> </div> </td> <td> <div> <div> <p>metre </p> </div> </div> </td> </tr> </tbody> </table> <p> </p> <p><strong><span>Additional Notes</span></strong></p> <p><span>This research is funded by the RISE Research Institutes of Sweden, the Swedish Research Council for Sustainable Development (Formas, project number 2018-01768), and Transport Area of Advance, Chalmers.</span></p> <p><strong><span>Contributions</span></strong></p> <p><span>YL designed the simulation, analyzed the simulation data, and, along with CT, executed the simulation. CT, SD, FS, and SY conceptualized the model (SySMo), with CT and SD further developing the model to produce agents and their activity plans. KG wrote the data document. All authors reviewed, edited, and approved the final document.</span></p>
Barchan Swarm Simulations Using the Two-Flank Agent-Based Model v3
<p>This archive contains several animations of barchan swarms simulated using the Two-Flank Agent-Based model which was introduced in following article: </p> <p> </p> <p>Robson, Dominic T., and Andreas CW Baas. "A Simple Agent‐Based Model That Reproduces All Types of Barchan Interactions." Geophysical Research Letters 50.19 (2023): e2023GL105182. </p> <p> </p> <p>which is Open Access and available at https://doi.org/10.1029/2023GL105182</p> <p> </p> <p>The Two-Flank Agent-Based Model has been developed openly on GitHub by Dominic T Robson and Andreas CW Baas. All the necessary source files together with an example run file can be found at:</p> <p> </p> <p>https://github.com/DTRobson/TwoFlankABModel/releases/tag/TFABM</p> <p> </p> <p>The following model parameters and initial conditions were used for these simulations:</p> <p> </p> <p> simwidth = 9000 or 15000 #(m)</p> <p> simlength = 10000 #(m)</p> <p> fieldwidth = 3000 or 5000 #(m)</p> <p> </p> <p> qsatinit = 79 #(m^2 year^{-1})</p> <p> q0 = 0.25 #(q_sat)</p> <p> dt = 1/8 #(years) </p> <p> collson = True</p> <p> inject = True</p> <p> injectdist = Uniform </p> <p> periodic = False</p> <p> </p> <p> lambda1 = 1.</p> <p> lambda2 = 1.8</p> <p> lambda3 = 1/3</p> <p> alpha = 0.05</p> <p> delta = 4.6 #(m)</p> <p> a = 0.45</p> <p> b = 0.1</p> <p> </p> <p> </p> <p> eqw = 0.5 * delta/(q0 - alpha)</p> <p> injectparams = [2*eqw, 2*eqw]</p> <p> lws = [eqw]</p> <p> rws = [eqw]</p> <p> xs = [fieldwidth*1.5] </p> <p> ys = [simlength - 1]</p> <p> </p> <p> c = 45. </p> <p> </p> <p> w0 = 16.6 #(m)</p> <p> </p> <p> outfluxmode = 'Hersen' or 'Duran' #Hersen for unscaled outflux, Duran for scaled outflux</p> <p> plottinghornflux = False</p> <p> </p> <p> keep_coll_rec = True</p> <p> </p> <p> helpplotting = True</p> <p> </p> <p> </p> <p>The remaining model parameters varied across the different runs and took the values shown in the filenames. Where the filename does not list the angular separation of the secondary wind mode (thetab) simulations were unidirectional. The primary mode was normally distributed with mean 270degrees and standard deviation theta_sd. The list of all values used are shown here:</p> <p> </p> <p> initdensity = 12 or 24 or 37#(km^{-2}) this sets the rate at which dunes are injected into the model</p> <p> qshift = 0. or 0.05 or 0.1 or 0.15#(q_sat)</p> <p> thetab = 22.5 or 45 or 67.5 #(degrees)</p> <p> theta_sd = 3 #(degrees) </p> <p> </p> <p>In bimodal simulations the secondary mode angle was normally distributed with mean 270+thetab and standard deviation theta_sd. Each year (12 iterations) the first 9 iterations had wind direction taken from the primary mode and the final 3 iterations were from the secondary mode i.e. the 3:1 seasons of primary:secondary wind direction. Note that, in the simulations the primary mode 270deg means that dunes migrate in the negative y-direction, to produce the plots and revert to the convention of the primary wind being in the x-direction, the dunes were then rotated.</p> <p>The simulations were performed by Dominic T Robson using a 12th Gen Intel(R) Core(TM) i7-1255U 1.70 GHz processor and 16.0GB of RAM.</p> <p> </p> <p> </p>
An agent-based model of the origins of modern linguistic complexity – supplementary information
<p>A central question in the evolution of human language is whether it emerged as a result of one specific event or from a mosaic-like constellation of different phenomena and their interactions. Three potential processes have been identified by recent research as the potential <em>primum mobile</em> for the origins of modern linguistic complexity: Self-domestication, characterized by a reduction in reactive aggression and often associated with a gracilization of the face; changes in early brain development manifested by globularization of the skull; and demographic expansion of H. sapiens during the Middle Pleistocene. We developed an agent-based model to investigate how these three factors influence transmission of information within a population. Our model shows that there is an optimal degree of both hostility and mental capacity at which the amount of transmitted information is the largest. It also shows that linguistic communi- ties formed within the population are strongest under circumstances where individuals have high levels of cognitive capacity available for information processing and there is at least a certain degree of hos- tility present. In contrast, we find no significant effects related to population size.</p>
PhysiCell Studio: a graphical tool to make agent-based modeling more accessible. Supplemental material.
<p>Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification, i.e., a markup language, that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a graphical user interface that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open source software and contributions from the community are encouraged. This dataset provides Supplemental material for the PhysiCell Studio publication.</p>
Simulated data from abmAnimalMovement: An R package for simulating animal movement using an agent-based model
<p>Contained here are the data simulated as part of the manuscript: "abmAnimalMovement: An R package for simulating animal movement using an agent-based model" that can be found at: https://github.com/BenMMarshall/abmAnimalMovement (and archived at: https://doi.org/10.5281/zenodo.6951937).</p> <p>- BADGER_locations.csv: A csv file that contains the realised locations of the example badger simulation, where each row is equal to a timestep. Columns include: timestep, the timestep as an integer; x, the x coordinate of the animal; y, the y coordinate of the animal; sl, the step length between locations used during the simulation; sl_rescale the rescale factor required to return step lengths back to the input scale; ta, turning angle between locations in degrees; behave, the behavioural mode the animal was in at a given timestep; chosen, the location chosen out of the number of options available; destination_x and destination_y the point the animal was attracted to at that time (note exploratory behaviour is not subject attraction).</p> <p>- BADGER_options.csv: A csv file that contains the options available to the example badger simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. Columns include: timestep, the timestep as an integer; oall_x, and oall_y show the x and y coordinates of all the options available to an animal at a timestep; oall_steplengths are the step lengths from the current location compared to all the options.</p> <p>- completelist.RDS: This RDS file contains a list object of length three, where the full simulation outputs from each three examples are stored. Each species slot contains the “locations” dataframe (see description of locations.csv), the "options" dataframe (see description of options.csv), and a nested list containing all the "inputs" used to generate the simulated results (split into subsections: inputs_basic that contains inputs linked to simulation duration and intensity, inputs_destination that contains inputs linked to destination and attraction aspects, inputs_movement that contains inputs linked to movement capacity and behavioural switching, inputs_cycle that contains inputs linked to activity cycling, inputs_layerSeed that contains the environmental matrices and seed). A fourth object is returned called "others" that captures all other outputs, mainly used internally for debugging and checking.</p> <p>- KINGCOBRA_locations.csv: A csv file that contains the realised locations of the example king cobra simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_options.csv file.<br>KINGCOBRA_options.csv: A csv file that contains the options available to the example king cobra simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_options.csv file.</p> <p>- VULTURE_locations.csv: A csv file that contains the realised locations of the example vulture simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_locations.csv file.<br>VULTURE_options.csv: A csv file that contains the options available to the example vulture simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_locations.csv file.</p> <p>- eg_landscapedata_completelist.RDS: This RDS file contains the landscape matrices required for recreating the simulated outputs described in the manuscript named above. It is a list of three objects ("shelter", "forage", "movement"), each a numeric matrix of equal size, with values describing the quality of each landscape characteristic.</p> <p>- argument_table.csv: Descriptive table of the simulation inputs used in the walk-through manuscript.</p>
ITACA agent-based simulation model results
<p>This files downloaded present the raw results and derived indicators per scenario considered obtained with the agent-based model developed for policy assessment in the scope of the ITACA project (<a href="https://www.itaca-h2020.eu/">https://www.itaca-h2020.eu/</a>).</p> <p>The files are structured in three different case studies:</p> <ul> <li>CS01 - Past unsuccessful and delayed solutions</li> <li>CS02 - Past successful solutions</li> <li>CS03 - Future solutions</li> </ul> <p>In each case study there are included a set of scenarios, each of them testing one or several policy measures. Each scenario file includes the short name of the policy under study. For example, "Results_visualisation_CS01_Mandates.xlsm" includes the results when testing mandates as policy measure in the CS01 case study. The corresponding raw outputs of the simulator are included in the folder "yyyymmdd_hhmmss_CS01_Mandates".</p> <p>Each excel file contains the following tabs:</p> <ul> <li>Files - Files loaded to create the file. The loading interactive buttons have been disabled to avoid errors.</li> <li>H0 - Tab containing the visualisation of different raw outputs for the adopters agents. The user can select all agents or one specific agent to look at the results by selecting its ID in the slicers on top of each agent type (enable editing in excel to do so).</li> <li>H0_KPI_tech - Shows the number of adoptions per technology and agent type</li> <li>H0_KPI_eco - Shows the surpluses per agent type and total social welfare</li> <li>H0_KPI_eco_metrics - Same results, disaggregated in a tabular format.</li> <li>H0_KPI_op - Shows the total fuel burnt, throughput and Determined Unitary Cost (DUC) for enroute and terminal ANS.</li> <li>H1 - Configuration file used as input for the simulation.</li> <li>H2 - Raw results for airlines.</li> <li>H3 - Raw results for airports.</li> <li>H4 - Raw results for enroute ANSPs.</li> <li>H5 - Raw results for terminal ANSPs.</li> <li>H6 - Raw results per route.</li> <li>H7 - Raw results for regulators.</li> </ul> <p>The raw results folders include, apart from the data shown in the visualisation excel files, the input data per stakeholder, policy parameters, technology parameters, exogenous variables that affect the agents and the charging zones defined in the European airspace. </p> <p>The complete description of the case studies and scenarios tested is included in ITACA's deliverable <a href="http://www.nommon-files.es/itaca/ITACA-D5.1_Impact_assessment_of_policies_and_regulations_to_boost_ATM_technology_adoption_v01.00.00.pdf">D5.1 Impact Assessment of Policies and Regulations to Boost ATM Technology Adoption</a>.</p> <p>Thank you for downloading the dataset! It would be very helpful if you share your view on the data show with us. We have created the following short <a href="https://forms.gle/EgVAW57siXizsEn8A">questionnaire</a> to facilitate the task. </p>
The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil
<p>This video shows de simulation of scenarios presented in the article "The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil"</p>
Synthetic COVID-19 Case Reporting Data Generated from an Agent-Based Simulation Model
<p>This is a synthetic case reporting data set for the SARS-CoV-2 epidemic in Austria. The data set statistically reproduces and synthetically augments data on reported cases and was generated with an agent-based simulation model. References to descriptions of the model and the parameterization used to generate the data set is included in the attached PDF file. The data format is described in the README file.</p>
An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces: Video Results
<p>A video illustrating the results presented in the paper: <em>"Prédhumeau M., Mancheva L., Dugdale J., and Spalanzani A. 2021. An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces. In the Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). IFAAMAS, Online."</em></p> <p> </p>
Alternative Covid-19 mitigation measures in school classrooms: Analysis using an agent-based model of SARS-CoV-2 transmission
<p>The SARS-CoV-2 epidemic continues to have major impacts on children's education, with schools required to implement infection control measures that have led to long periods of absence and classroom closures. We have developed an agent-based epidemiological model of SARS-CoV-2 transmission that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties; the basis of our approach is a contact model constructed using random networks, informed by structured expert judgment. The effectiveness of mitigation strategies is considered in terms of effectiveness at suppressing infection outbreaks and limiting pupil absence. Covid-19 infections in schools in the UK in Autumn 2020 are re-examined and the model used for forecasting infection levels in autumn 2021, as the more infectious Delta-variant was emerging and school transmission was thought likely to play a major role in an incipient new wave of the epidemic. Our results are in good agreement with available data and indicate that testing-based surveillance of infections in the classroom population with isolation of positive cases is a more effective mitigation measure than bubble quarantine both for reducing transmission in primary schools and for avoiding pupil absence, even accounting for the insensitivity of self-administered tests. Bubble quarantine entails large numbers of pupils being absent from school, with only a modest impact on classroom infection levels. However, maintaining a reduced contact rate within the classroom can have a major beneficial impact on managing Covid-19 in school settings.</p>
Adaptive Behavior of Farmers Under Consecutive Droughts Results In More Vulnerable Farmers: A Large-Scale Agent-Based Modeling Analysis in the Bhima Basin, India
Open the record for dataset details and reuse information.
Alternative Covid-19 mitigation measures in school classrooms: Analysis using an agent-based model of SARS-CoV-2 transmission
Open the record for dataset details and reuse information.
Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma - Location Data
<p>This dataset is meant to be used with "Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma - Results and Data". It provides spatial output data for 4 different setups (spheroid, traditional, 3d gravity, and traditional floating) of an agent-based model of <i>in vitro </i>tuberculosis infection models. </p>
Simulated results from an agent-based model examining inequality and innovation in social networks
<p>Theories of innovation often balance contrasting views that either smart people create smart things or smartly constructed institutions create smart things. While population models have shown factors including population size, connectivity, and agent behavior as crucial for innovation, few have taken the individual-central approach seriously by examining the role individuals play within their groups. To explore how network structures influence not only population-level innovation but also performance among individuals, we studied an agent-based model of the Potions Task, a paradigm developed to test how structure affects a group's ability to solve a difficult exploration task. We explore how size, connectivity, and rates of information sharing in a network influence innovation and how these have an impact on the emergence of inequality in terms of agent contributions. We find, in line with prior work, population size has a positive effect on innovation, but that large and small populations perform similarly per capita; that many small groups outperform fewer large groups; that random changes to structure have few effects on innovation; and that the highest performing agents tend to occupy more central network positions. Moreover, we show that every network factor which facilitates innovation leads to a proportional increase in inequality of performance, creating "genius effects" among otherwise "dumb" agents in both idealized and real-world networks.</p>
Supplemental Movie Files for "Agent-Based Modeling of a Nuclear Chromosome Ensemble Identifies Determinants of Homolog Pairing During Meiosis" by Chriss et al.
<p>This set of Supplemental Information contains two movies made from the simulations from the model developed in the manuscript "<strong>Agent-Based Modeling of a Nuclear Chromosome Ensemble Identifies Determinants of Homolog Pairing During Meiosis</strong>" by A. Chriss, G. V. Börner, and S. D. Ryan. </p> <p> </p> <p>Supplemental Movie S1: <strong>WT Chromosome Trajectories during Prophase I. </strong>The first file "movie_WT..." contains the file for the results of simulations for the wild-type chromosomes and the exact parameter values can be found in Table 1 of the manuscript. The movie shows one realization of the agent-based model. The simulation movie covers the homology search process from <em>t = 3h </em>to <em>t = 9h</em>. Matching colors correspond to homologous pairs. True chromosome lengths are incorporated and scale the relevant interaction radii. The radius represents the attractive and non-homologous repulsive region.</p> <p> </p> <p>Supplemental Movie S2: <strong>WT Chromosome Trajectories during Prophase I with active dumbbell model. </strong> The second file "movie<em>WT</em>_activedumbbell..." contains the file for the results of the simulations for the modeling of chromosomes as active dumbbells (from polymers) to allow for the study of the effects of elongation, orientation, and flexibility. The movie shows one realization of the agent-based active dumbbell model which is closer to modeling a chromosome as a polymer. The simulation movie covers the homology search process from <em>t = 3h </em>to <em>t = 9h</em>. Matching colors correspond to homologous pairs. True chromosome lengths are incorporated and scale the relevant interaction radii, but are allowed to change in time as the two beads expand and contract. The radius represents the attractive and non-homologous repulsive region.</p> <p> </p> <p>Supplemental Movie S3: <strong><em>spo11</em> hypomorph (30% WT DSB levels) Chromosome Trajectories during Prophase I (parameters from Fig 7B)</strong>. The third file "movie_spo11..." contains the file for the results of the simulations for the spo-11 hypomorph and the associated parameter values can be found in Table 1 of the manuscript. The movie depicts one realization of the agent-based model for the {\it spo11} hypomorphic mutant. The simulation movie covers the homology search process from <em>t = 3h</em> to <em>t = 9h</em> where mutant <em>spo11</em> is associated with a weaker attractive and repulsive force (e.g., reduction to 77% of WT values). True chromosome lengths are incorporated and scale the relevant interaction radii. Matching colors correspond to homologous pairs. The radii represent the homologous attractive and the non-homologous repulsive region. Note that the reduction in interaction strength delays homologous pairing consistent with experimental observations in [13]. </p> <p> </p> <p> </p> <p>The codes that generated these movies were written in Matlab and freely available via GitHub: <a href="https://github.com/sdryan/ChromosomeDynamicsProphase1">https://github.com/sdryan/ChromosomeDynamicsProphase1</a></p> <p> </p> <p>For questions please contact the corresponding authors: G. Valentin Börner <a href="mailto:g.boerner@csuohio.edu">g.boerner@csuohio.edu</a> (Biology) or Shawn D. Ryan <a href="mailto:s.d.ryan@csuohio.edu">s.d.ryan@csuohio.edu</a> (Math).</p>
Data repository for study "Understanding agricultural market dynamics in times of crisis: the dynamic agent-based model Agrimate"
<p>Data for the study "Understanding agricultural market dynamics in times of crisis: the dynamic agent-based model Agrimate".</p> <p><strong>hindcasting_analysis</strong></p> <ul> <li>figures of the hindcasting exercise in the main text</li> <li>raw_data <ul> <li> raw model output data for <ul> <li>baseline scenario -- <em>agrimate_baseline=2007-2009_extra_regions=(Egypt=EGY)_regions=AgrimateEU28_start=2000-01-01.nc</em></li> <li>production failure scenario -- <em>agrimate_baseline=2007-2009_extra_regions=(Egypt=EGY)_production_anomalies=FAOsince-2005_regions=AgrimateEU28_start=2000-01-01.nc</em></li> <li>production failure and export restriction scenario -- <em>agrimate_baseline=2007-2009_export_restrictions=2007-2011_extra_regions=(Egypt=EGY)_production_anomalies=FAOsince-2005_regions=AgrimateEU28_start=2000-01-01.nc</em></li> </ul> </li> </ul> </li> </ul> <p><strong>multibreadbasket_analysis</strong></p> <ul> <li>figures of the multibreadbasket analysis in the main text</li> <li>raw_data <ul> <li> raw model output data for <ul> <li>simulations under historical climatic conditions with <number> as an identifier -- <em>agrimate_his-<number>.nc</em></li> <li>simulations under +2°C projection with <number> as an identifier -- <em>agrimate_2p0-<number>.nc</em></li> </ul> </li> </ul> </li> <li>processed_data <ul> <li>processed output data to easier/faster plot</li> </ul> </li> </ul> <p><strong>sensitivity_analysis</strong></p> <ul> <li>raw data and graphics as in <strong>main_output</strong> for different model parameters as given in Table F.1</li> </ul> <p> </p> <p> </p>
Supplementary data - "Learning Reduced Models for Large-Scale Agent-Based Systems"
<p>This repository contains supplementary data on my PhD thesis "Learning Reduced Models for Large-Scale Agent-Based Systems". Chapters 1-3, 7 and A do not have supplementary data.</p> <p><strong>Chapter 4</strong></p> <ul> <li><em>Large_deviation_example.zip</em> contains the trajectory for Figure 4.8.</li> <li><em>mean_exit_time*</em> contains the raw data to compute the mean exit time and standard deviation for the ABM process (JP) and SDE process (CLE). It contains additionally a precomputed mean and standard deviation as well as the corresponding numbers of agents.</li> <li><em>transition_matrix*</em> contain the computed box discretizations as MATLAB and Numpy files as used for Figures 4.2-4.4, 4.6 and Tables 4.1 and 4.2.</li> </ul> <p><strong>Chapter 5</strong></p> <ul> <li><em>CVM_2021-07-09-15-53_training_data.npz</em> contains the training data for Figure 5.7 a and b.</li> <li><em>CVM_2021-09-29-07-13_distribution.npz </em>contains the raw data for Figure 5.7 c.</li> <li>The remaining data for Chapter 5 can be found in the related dataset <a href="https://doi.org/10.5281/zenodo.4522119">doi.org/10.5281/zenodo.4522119</a>.</li> </ul> <p><strong>Chapter 6</strong></p> <ul> <li><em>CVM_pareto_estimate</em> contains trajectory data required for Figure 6.6 b to estimate points in the Pareto Front using the civil violence model. </li> <li><em>CVM_training_data</em> contains the training data to construct the surrogate model. Each data set consists of <em>CVM_*_cops_train.npz</em> as training set, <em>CVM_*_cops_trajectory.npz</em> as sample trajectory and <em>CVM_*_cops.pkl</em> to compute the training data.</li> <li><em>CVM_covering_iterations_8.mat</em> Pareto set covering after 8 iterations for the civil violence model. Required for Figure 6.6 a.</li> <li><em>CVM_pareto_set+front.npz</em> is required for Figure 6.6 b. </li> <li><em>CVM_surrogate_model.mat</em> contains the surrogate model for the civil violence model</li> <li><em>Expl_iterations_*</em> contains Pareto set coverings after 8 and 12 iterations for Example 6.1.4 and Figure 6.1.</li> <li><em>VM_covering_iterations_12.mat</em> contains the Pareto set covering depicted in Figure 6.4 a.</li> <li><em>VM_ODE_covering_iterations_12_subset_front.mat</em> contains the Pareto set covering depicted in Figure 6.5 and 6.5 c.</li> <li><em>VM_ODE_covering_iterations_12_subset.mat</em> contains the Pareto set covering depicted in Figure 6.5 and 6.5 d.</li> <li><em>VM_ODE_covering_iterations_12.mat</em> contains the Pareto set covering depicted in Figure 6.4 b.</li> <li><em>VM_surrogate_model.mat</em> contains the surrogate model for the extended voter model.</li> <li><em>VM_test_points_non_pareto.npz</em> contains Non-Pareto points in Figure 6.5 and 6.5 d.</li> <li><em>VM_test_points_pareto.npz</em> contains Pareto points in Figure 6.5 and 6.5 c.</li> </ul>
Data, models, and outputs for an agent-based hydro-economic modeling study in an intensively irrigated region of the U.S. High Plains
<p>In this dataset, we include all the models developed for the study "An integrated modeling approach to simulate human-crop-groundwater interactions in intensively irrigated regions", which is published in Environmental Modelling & Software (<a href="https://doi.org/10.1016/j.envsoft.2024.106120">https://doi.org/10.1016/j.envsoft.2024.106120</a>). Additionally, we provide all the data used in this study. Below, you will find a description of the contents of each file:</p> <ul> <li>abm_modflow.zip: This file includes the agent-based hydro-economic model (ABM-MODFLOW), including model inputs and outputs, Python post-processing scripts, and the Windows batch script for the integration process.</li> <li>modflow.zip: This file contains the standalone MODFLOW model files. Each folder includes files for individual simulation periods, starting with a steady-state model for the predevelopment period, followed by seven transient models.</li> <li>modflow_rs.zip: This file contains the MODFLOW-RS model files. Given that remote sensing data is provided for years from 1984 onwards, only models for the post-1980 simulation periods are included. For model files corresponding to years prior to 1980, refer to modflow.zip.</li> <li>Figurers.zip: This file includes all data and Python scripts used to produce the figures in the main text.</li> <li>Tables.zip: This file contains all tables and the associated data included in the main text.</li> <li>Supporting_Info_Figures: This file includes all data and Python scripts used to produce the figures in the Supporting Information.</li> <li>Supporting_Info_Tables: This file contains all tables and the associated data included in the Supporting Information.</li> <li>Supporting_Info_Videos: This file stores Videos S1 and S2 of the Supporting Information, displaying the historical development of irrigation wells and groundwater-fed irrigated lands in the study area from 1946 to 2018.</li> </ul>
set of exploration data and parameters for h24/5ad agent based model
<p>Different set of data used for exploration, used for reproductibility, updated with HigherProp parameters</p>
When should bees be flower constant? An agent-based model highlights the importance of social information and foraging conditions
<p>1. Many bee species show flower constancy, i.e. a tendency to visit flowers of one type during a foraging trip. Flower constancy is important for plant reproduction, but the benefits of constancy to bees are unclear. Social bees, which often use communication about food sources, show particularly strong flower constancy.</p> <p>2. We aimed to better understand the benefits of flower constancy in social bees and how these benefits depend on foraging conditions. We hypothesised that sharing social information increases the benefits of flower constancy because social foragers share information selectively about high-quality food sources, thereby reducing the need to sample alternatives.</p> <p>3. We developed an agent-based model that allowed us to simulate bee colonies with and without communication and flower constancy in different foraging environments. By varying key environmental parameters, such as food source numbers and reward size, we explored how the costs and benefits of flower constancy depend on the foraging landscape.</p> <p>4. Flower constancy alone performed poorly in all environments, while indiscriminate flower choice was often the most successful strategy. However, communication improved the performance of flower constant colonies considerably in most environments. This combination was particularly successful when high-quality food sources were abundant and competition was weak.</p> <p>5. Our findings help explain why social bees tend to be more flower constant than solitary bees and suggest that flower constancy can be an adaptive strategy in social bees. Simulations suggest that anthropogenic changes of foraging landscapes will have different effects on the foraging performance of bees that vary in flower constancy.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.