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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>
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>
Open synthetic data on travel and charging demand of battery electric cars: An agent-based simulation on three charging behavior archetypes
<p><strong>Background</strong></p> <p>Battery electric vehicles (BEVs) are crucial for a sustainable transportation system. As more people adopt BEVs, it becomes increasingly important to accurately assess the demand for charging infrastructure. However, much of the current research on charging infrastructure relies on outdated assumptions, such as the assumption that all BEV owners have access to home chargers and the "Liquid-fuel" mental model. To address this issue, we simulate the travel and charging demand on three charging behavior archetypes. We use a large synthetic population of Sweden, including detailed individual characteristics, such as dwelling types (detached house vs. apartment) and activity plans (for an average weekday). This data repository aims to provide the BEV simulation's input, assumptions, and output so that other studies can use them to study sizing and location design of charging infrastructure, grid impact, etc.</p> <p>A journal paper published in Transportation Research Part D: Transport and Environment details the method to create the data (particularly Section 2.2 BEV simulation).</p> <p><a href="https://doi.org/10.1016/j.trd.2023.103645">https://doi.org/10.1016/j.trd.2023.103645</a></p> <p><strong>Methodology</strong></p> <p>This data product is centered on the 1.7 million inhabitants of the Västra Götaland (VG) region, which includes the second largest city in Sweden, Gothenburg. We specifically simulated 284,000 car agents who live in VG, representing 35% of all car users and 18% of the total population in the region. They spend their simulation day (representing an average weekday) in a variety of locations throughout Sweden.</p> <p>This open data repository contains the core model inputs and outputs. The numbers in parentheses correspond to the data sets. We use individual agents' activity plans (1) and travel trajectories from MATSim simulation for the BEV simulation (2), in which we consider overnight charger access (3), car fleet composition referencing the current private car fleet in Sweden (4), and Swedish road network with slope information (5) with realistic BEV charging & discharging dynamics. For the BEV simulation, we tested ten scenarios of charging behavior archetypes and fast charging powers (6). The output includes the time history of travel trajectories and charging of the simulated BEVs across the different scenarios (7).</p> <p><strong>Data description</strong></p> <p>The current data product covers seven data files.</p> <p><strong>(1) Agents' experienced activity plans</strong></p> <p>File name: 1_activity_plans.csv</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>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</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>deso</p> </td> <td> <p>Zone code of Demographic statistical areas (DeSO)<sup>1</sup></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>meter</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>meter</p> </td> </tr> <tr> <td> <p>act_purpose</p> </td> <td> <p>Activity purpose (work, home, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>mode</p> </td> <td> <p>Transport mode to reach the activity location (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>dep_time</p> </td> <td> <p>Departure time in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</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:second</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>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> <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>act_start</p> </td> <td> <p>Start time of activity in minute (0-1439)</p> </td> <td> <p>Integer</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>act_end</p> </td> <td> <p>End time of activity in decimal hour (0-23.99)</p> </td> <td> <p>Float</p> </td> <td> <p>hour</p> </td> </tr> <tr> <td> <p>score</p> </td> <td> <p>Utility score of the simulation day given by MATSim</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </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> <p> </p> <p><strong>(2) Travel trajectories</strong></p> <p>File name: 2_input_zip</p> <p>Produced by MATSim simulation, the zip folder contains ten files (events_batch_X.csv.gz, X=1, 2, …, 10) of input events for the BEV simulation. They are the moving trajectories of the car agents in their simulation days.</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>time</p> </td> <td> <p>Time in second in a simulation day (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation<sup>2</sup></p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Nearest road link consistent with (5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>vehicle</p> </td> <td> <p>Vehicle ID identical to person</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p><sup>2 </sup>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> <p> </p> <p><strong>(3) Overnight charger access</strong></p> <p>File name: 3_home_charger_access.csv</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>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(4) Car fleet composition</strong></p> <p>File name: 4_car_fleet.csv</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>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>income_class</p> </td> <td> <p>Income group (0=None, 1=below 180K, 2=180K-300K, 3=300K-420K, 4=above 420K)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p>(<strong>5) Road network with slope information</strong></p> <p>File name: 5_road_network_with_slope.shp (5 files in total)</p> <table> <tbody> <tr> <td> <p>Column</p> </td> <td> <p>Description</p> </td> <td> <p>Data type</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>length</p> </td> <td> <p>The length of road link</p> </td> <td> <p>Float</p> </td> <td> <p>meter</p> </td> </tr> <tr> <td> <p>freespeed</p> </td> <td> <p>Free speed</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> <tr> <td> <p>capacity</p> </td> <td> <p>Number of vehicles</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>permlanes</p> </td> <td> <p>Number of lanes</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>oneway</p> </td> <td> <p>Whether the segment is one-way (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>modes</p> </td> <td> <p>Transport mode (car)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link_id</p> </td> <td> <p>Link ID</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>from_node</p> </td> <td> <p>Start node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>to_node</p> </td> <td> <p>End node of the link</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>count</p> </td> <td> <p>Aggregated traffic (number of cars travelled per day)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>slope</p> </td> <td> <p>Slope in percent from -6% to 6%</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>geometry</p> </td> <td> <p>LINESTRING (SWEREF99TM)</p> </td> <td> <p>geometry</p> </td> <td> <p>meter</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(6) Simulation scenarios specifying the parameter sets</strong></p> <p>File name: 6_scenarios.txt</p> <table> <tbody> <tr> <td> <p><strong>Parameter set</strong></p> <p><strong>(paraset)</strong></p> </td> <td> <p><strong>Strategy 1</strong></p> </td> <td> <p><strong>Strategy 2</strong></p> </td> <td> <p><strong>Strategy 3</strong></p> </td> <td> <p><strong>Fast charging power (kW)</strong></p> </td> <td> <p><strong>Minimum parking time for charging (min)</strong></p> </td> <td> <p><strong>Intermediate charging power (kW)</strong></p> </td> </tr> <tr> <td> <p>0</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>0.2</p> </td> <td> <p>0.2</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>150</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.3</p> </td> <td> <p>0.9</p> </td> <td> <p>50</p> </td> <td> <p>5</p> </td> <td> <p>22</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>(7) Time history of travel trajectories and charging of the simulated BEVs</strong></p> <p>File name: 7_output.zip</p> <p>Produced by the BEV simulation, the zip folder contains four files (parasetX.csv.gz, X=1, 2, 3, 4) corresponding to the four parameter sets specified in (6). They are the moving trajectories of the car agents with simulated energy and charging time history in their simulation days.</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>person</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>home_charger</p> </td> <td> <p>Whether an agent has access to a home garage charger/living in a detached house (0=no, 1=yes)</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>car</p> </td> <td> <p>Car model class (B=40 kWh, C=60 kWh, D=100 kWh)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>seq</p> </td> <td> <p>Sequence ID of time history by agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p>Time (0-86399)</p> </td> <td> <p>Integer</p> </td> <td> <p>Second</p> </td> </tr> <tr> <td> <p>purpose</p> </td> <td> <p>Valid for activities (home, work, school, other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>type</p> </td> <td> <p>Event type defined by MATSim simulation</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>link</p> </td> <td> <p>Link ID (link_id in File 5)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>distance_driven</p> </td> <td> <p>Cumulative driven distance in the simulation day</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>energy_1</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_2</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>energy_3</p> </td> <td> <p>Energy consumed while driving (-) or charging (+) (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kWh</p> </td> </tr> <tr> <td> <p>charger_1</p> </td> <td> <p>Power rating of the charger (Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_2</p> </td> <td> <p>Power rating of the charger (Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>charger_3</p> </td> <td> <p>Power rating of the charger (Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>kW</p> </td> </tr> <tr> <td> <p>soc_1</p> </td> <td> <p>State of charge (0-1, Strategy 1)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_2</p> </td> <td> <p>State of charge (0-1, Strategy 2)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>soc_3</p> </td> <td> <p>State of charge (0-1, Strategy 3)</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </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>
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>
Agent-based simulation
<p>This datasaet comprises the results of the agent-based simulation study described in: Mazurkiewicz, Bartosz and Kattenbeck, Markus and Giannopoulos, Ioannis, <strong>Navigating Your Way! Increasing the Freedom of Choice during Wayfinding, </strong>11th International Conference on Geographic Information Science (GIScience 2021) - Part II</p>
Data from: Agent-based versus correlative models of species distributions: Evaluation of predictive performance with real and simulated data
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Simulated results from an agent-based model examining inequality and innovation in social networks
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Simulation results of the agent-based model of urban insurgence with the effect of gathering sites and Koopman mode analysis
<p>The data set contains the simulation results of the agent-based model of urban insurgence with the effect of gathering sites and Koopman mode analysis. Some details on the agent-based model (without gathering sites) can be found in [Maria Fonoberova, Vladimir A. Fonoberov, Igor Mezic, Jadranka Mezic and P. Jeffrey Brantingham, Nonlinear Dynamics of Crime and Violence in Urban Settings, Journal of Artificial Societies and Social Simulation, 15(1), 2, http://jasss.soc.surrey.ac.uk/15/1/2.html, DOI: 10.18564/jasss.1921].</p> <p>Files in folder "0bar" are related to the case with 0 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "1bar" are related to the case with 1 preferential gathering site.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and average distance from the preferential gathering site. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "2bars" are related to the case with 2 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars2.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "3bars" are related to the case with 3 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars3.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "4bars" are related to the case with 4 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars4.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "5bars" are related to the case with 5 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "10bars" are related to the case with 10 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars10.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "20bars" are related to the case with 20 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars20.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "30bars" are related to the case with 30 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars30.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "prob25" are related to the case with 5 preferential gathering sites and 25% probability of agents moving towards them.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5_prob0.25.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "prob50" are related to the case with 5 preferential gathering sites and 50% probability of agents moving towards them.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5_prob0.50.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "prob75" are related to the case with 5 preferential gathering sites and 75% probability of agents moving towards them.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5_prob0.75.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "KMD" have detailed information for the case with 3 preferetial gathering sites and lattice size 200x200.</p> <p>File "2016_Actives_LD200_seed111_bars3.txt" has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens.</p> <p>Each file with name starting with Day for each active / intimidated (jailed) / non-intimidated (notjailed) agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship. Each file with name starting with Day for each LEO (cop) has his/her x-coordinate, y-coordinate, agent's state and -1, -1, -1. Last 3 entrees are not used. These files are provided for each time step of the simulation.</p>
Simulation results of two agent-based models of logistics systems
<p>The data set contains the simulation results of the two different agent-based models of logistics systems: <br> 1. A medical treatment facility (MTF) model, consisting of agents representing wounded soldiers and fixed sites representing medical facilities.<br> 2. A ship fueling (SF) simulation, consisting of agents representing fuel transport ships and fixed sites representing fuel-using bases.</p> <p>Folders in folder "MTF" are related to the MTF model.</p> <p>Files in folder "casualty_rateX" are related to the case with casualty rate of X new casualties per time step, where X = 30, 50, 70, . . . , 330.</p> <p>Each file "RunN_casualtyX_fullness.txt" has the "fullness" (defined as the ratio of the number of patients at a site to the total patient capacity of that site) of each site for each time step, where the run number N = 1, 2, 3, . . . , 100.</p> <p>Each file "RunN_casualtyX_dow.txt" has the total number of Dead Of Wounds that occur in all sites in each time step, where the run number N = 1, 2, 3, . . . , 100.</p> <p>Folders in folder "SF" are related to the SF model.</p> <p>Files in folder "siteMaxX" are related to the case with an initial (and maximum) site fuel value of X units, where X = 25, 50, 75, . . . , 200.</p> <p>Each file "assetTowedFuelHistory_siteMaxX_N.txt" has the number of towed fuel units for each asset for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p> <p>Each file "assetUseFuelHistory_siteMaxX_N.txt" has the number of onboard fuel units for each asset for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p> <p>Each file "siteHistory_siteMaxX_N.txt" has the number of fuel units at each site for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p>
A simple framework for agent-based modeling with extracellular matrix: Simulation results
<h1>Data for "A simple framework for agent-based modeling with extracellular matrix"</h1> <p> </p> <div>Metzcar, J., Duggan, Ben S., Fischer, B., Murphy, M., Heiland, R., Macklin, P. A simple framework for agent-based modeling with extracellular matrix. bioRxiv. doi: 10.1101/2022.11.21.514608</div> <div> </div> <div><a href="https://www.biorxiv.org/content/10.1101/2022.11.21.514608">Link to preprint</a></div> <div> <p>This repository contains the data for each subfigure (and video) in the preprint cited and linked abvoe, as well as the original figures and videos themselves. We have tried to include original model file (<code>PhysiCell_settings.xml</code> or other <code>*.xml</code> file) used to generate each results with each file set as well as the code (see python scripts) to make images and videos that appear in the pre-print - both within the files and base modules and examples in a separate folder.</p> <p>The data can be regenerated using release 2.0, <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.1">2.1</a>, and <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.2.1">2.2.1</a>. Release 2.1 is recommended for reproducing more exactly the results for the fibrosis, invasive carcinoma, and series of leader-follower results as the results archived here were produced using a set of two random number generators (one from PhysiCell and one from BioFVM). 2.2.1, used to produce the invasive cellular front results, consolidates the use of random number generators to just one (the PhysiCell one). As such, in 2.2.1, the random number generator seed may need changed to produce stochastic replicates, even when multithreading.</p> </div> <p> </p> <div>The following file sets are in this download:</div> <ul> <li>Fig2_SM_3_simple_tests.zip <ul> <li>Has results for Figure 2 and SM Figure 3</li> </ul> </li> <li>Fig3_fibrosis.zip <ul> <li>Results from fibrosis simulation (originally Figure 3, now Figure 4)</li> </ul> </li> <li>Fig4_invasive_carcinoma.zip <ul> <li>Results from invasive carcinoma simulation (originally Figure 4, now Figure 5)</li> </ul> </li> <li>Fig5_collective_migration_initial_tests.zip <ul> <li>Results from initial leader follower model development (originally Figure 5, now Figure 6)</li> </ul> </li> <li>Fig6_instant_remodeling.zip <ul> <li>Results from instant remodeling leader follower model scenariods (originally Figure 6, now Figure 7)</li> </ul> </li> <li>Fig7b_leader_follower.zip <ul> <li>Results from leader-follower collective migration scenario (origianlly Figure 7b, now Figure 8b)</li> </ul> </li> <li>images_and_vidoes_for_paper.zip <ul> <li>has all the images, videos, and some figures from the main body of the paper and the supplmental material. Updated in this version.</li> </ul> </li> <li>Invasive_cellular_front.zip <ul> <li>Has results for each ECM scenario: random, parallel, and perpindicular orientations and mixed ECM conditions. New to this data repository.</li> </ul> </li> <li>python_imaging.zip <ul> <li>Base modules and examples for producing images (tested on Python 3.9). Updated in in this verison</li> </ul> </li> <li>SM_Fig_4b_leader_follower_decreased_remodeling.zip</li> <li>stochastic_replicates.zip <ul> <li>Contains stochastic replicates for the collective migration, fibrosis, and invasive carcinoma models and source code for all.</li> </ul> </li> <li>stochastic_replicates_invasive_cellular_front.zip <ul> <li>Contains stochastic replicates for the invasive cellular front scenarios. New to this data repository.</li> </ul> </li> </ul>
Data from: Analysis and prediction of effects of the Manchester Triage System on patient waiting times in an emergency department by means of agent-based simulation
A simulation of complex clinical processes is a challenging task and suitable methods need to be found which can capture the influence of relevant factors and their relationships. The Manchester triage system (MTS) is widely used in German emergency departments (ED), however the impact on patient waiting times remain difficult to predict. The purpose of this work is the assessment of MTS particularly with regard to the waiting times of different degrees of severity. The methodology of agent based simulation was found suitable for the ED domain and the agent based simulation tool SeSAm was chosen due to its intuitive user interface and easy adaption of the simulation models. Altogether four agent classes could be implemented based on the information derived from a process model. The model permits a dynamic simulation of the ED processes and a reliable assessment of patient waiting times. In addition, the implementation of a triage nurse allowed the simulation of the triage process and a direct comparison to the current state without a standardized triage procedure. Essential influencing factors (e.g. number of patients, manning level) were implemented and their effects on the ED processes and patient waiting times assessed. The simulation runs delivered correct results based on the underlying process model and the collected statistical data. The process flow and the waiting times of an ED could be mapped exactly. In all simulation runs the waiting times of high triage levels (MTS-levels 1 and 2) could be reduced. Especially patients of MTS-level 2 in the waiting area of the ED benefit significantly from the implementation of a standardized triage procedure and the associated permanent monitoring.
Data from: Analysis and prediction of effects of the Manchester Triage System on patient waiting times in an emergency department by means of agent-based simulation
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Simulation results of an agent-based model of civil violence with the effect of introducing a small world network
<p>The data set contains the simulation results of an agent-based model of civil violence with the effect of introducing a small world network. Some details on the agent-based model (without small world network) can be found in [Maria Fonoberova, Vladimir A. Fonoberov, Igor Mezic, Jadranka Mezic and P. Jeffrey Brantingham, Nonlinear Dynamics of Crime and Violence in Urban Settings, Journal of Artificial Societies and Social Simulation, 15(1), 2, http://jasss.soc.surrey.ac.uk/15/1/2.html, DOI: 10.18564/jasss.1921].</p> <p>Files in folder "Appropriate_Rate_of_Violence" are related to the case with the appropriate rate of violence.</p> <p>Subfolder NoSWN_CitVis_14 is for the case with no small world network and the citizen vision of 14.</p> <p>Subfolder SWN_CitVis_13.16 is for the case with small world network and the citizen vision of 13.16.</p> <p>Subfolder SWN_CitVis_14 is for the case with small world network and the citizen vision of 14.</p> <p>Each file with name starting with Act has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. These files are provided for lattice sizes from 100x100 to 300x300 and for different random seeds.</p> <p>Files in folder "High_Rate_of_Violence" are related to the case with the high rate of violence.</p> <p>Subfolder Beta0.2 is for the case with small world network and \beta=0.2.</p> <p>Subfolder Beta0.8 is for the case with small world network and \beta=0.8.</p> <p>Subfolder NoSWN is for the case with no small world network.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. These files are provided for lattice sizes from 100x100 to 300x300 and for different random seeds.</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.