What question did this study set out to answer?

To provide a dataset for modeling human activities within urban areas through an agent-based framework.

April 30, 2026Open Access

Dataset: Modelling human activities in a system of cities

Key Points

To provide a dataset for modeling human activities within urban areas through an agent-based framework.
Utilized the DAVE model for simulations in Vaud and Geneva, Switzerland.
Organized dataset includes categories like Citizens, Environmental Variables, and Transport.
Employed MATSDA for transport simulation using Dijkstra’s Algorithm for route optimization.
Dataset facilitates the understanding of urban form and its impact on micro-climate.
Transport network insights aid in improving commuting efficiencies within urban settings.

Abstract

This repository contains the input data for the paper 'Modelling human activities in a system of cities'. The input data is used to run the DAVE model for Cantons Vaud and Geneva in Switzerland at 500m grid. DAVE (Dynamic Anthropogenic actiVities and feedback to Emissions) is a multi-scale agent-based modelling system. It uses a bottom-up approach to model the interactions and feedback between urban form (e. g. , building morphology, materials), urban function determined by human activities and the urban micro-climate. This dataset is organized following the structure required by DAVE. Further details can be found at: DAVE Documentation and Manual: 10. 5281/zenodo. 11369892 The top folders containing the files for each category: Citizens, EnvironmentalVariables, InputOptions, Spatial, Timeₛettings, Transport, Traveldict. Citizens: each citizen in each grid with household and building information EnvironmentalVariables: weather forcing InputOptions: microenvironments and age type definitions Spatial: Information on the spatial grids such as the grid list, employment rate, spatial attractor, commuting attractor. Information on building such as the initial occupancy of each building (Londonₛtebbsbuildingₜypologies. nml actually contains building in the Swiss domain) and general thermal properties (Londonₛtebbsgeneralₚarams. nml, currently uses London parameters but can be updated in the future) Timeₛettings: day types (weekday, holidays, holidays with school) of each day of year Transport: transport capacity of each mode. The probability of using each transport mode to commute at each grid. Traveldict: the shortest path for each transport mode between each 500m grid in the study domain. The files are produced by MATSDA (Movement And Transport Simulations using Dijkstra’s Algorithm) v1. 0, which uses grid-based transport database and Dijkstra’s Algorithm to solve the shortest paths between each grid. Further details can be found at: Connecting Physical and Socio-Economic Spaces for Multi-Scale Urban Modelling: A Dataset for London. Geoscience Data Journal 12: e289. https: //doi. org/10. 1002/gdj3. 289 The transport network input to MATSDA can be downloaded from SwissTopo: https: //www. swisstopo. admin. ch/en/landscape-model-swisstlm3d Note: a newer version of MATSDA has become available recently (not used in this study): MATSDA-roads (Movement And Transport Simulations using Dijkstra's Algorithm - roads), https: //doi. org/10. 5281/zenodo. 17736682 To run DAVE simulations, a file on population activity schedule/habits is also needed. We processed this file from the TimeUse+ survey conducted by ETHZ but is not able to share it due to privacy. The data from TimeUse+ survey can be requested from its authors. Further details can be found at: TimeUse+ main study Anonymized data, http: //hdl. handle. net/20. 500. 11850/639241 More details on the processes to produce these files can be found at: Connecting Physical and Socio-Economic Spaces for Multi-Scale Urban Modelling: A Dataset for London. Geoscience Data Journal 12: e289. https: //doi. org/10. 1002/gdj3. 289

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