Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Modelling human activities in a system of cities

Guo-Shiuan Lin, Denise Hertwig, Megan McGrory, Tiancheng Ma, Stefán Thor Smith, Maider Llaguno-Munitxa, Sue Grimmond, Gabriele Manoli

Abstract

Cities host most of the world population with diverse services and activities. One key challenge in urban modelling is the quantification of intra- and inter-city mobility patterns and the associated space-time dynamics of population density and anthropogenic activities. To address this, we apply the novel agent-based urban model DAVE (Dynamic Anthropogenic actiVities and feedback to Emissions) to simulate population behaviour and mobility in the Vaud and Geneva Cantons, a system of small- to medium-size cities in Switzerland. Simulation results provide detailed temporal (10 min) and spatial (500 m) population dynamics for different age groups and day types. DAVE further models the time-varying population distribution in 11 different microenvironments (e.g., home, work, leisure, outdoor) and the travel flows by different modes. Simulation results align with observations, confirming the possibility of driving urban system modelling with statistical information on residents' behaviour. Sustainability and health indicators like daily driving distance and walking time for each neighbourhood are also reflected by the model with urban-rural gradients displayed. This work serves as a foundation for future applications of DAVE to study bottom-up human-built environment interactions, from anthropogenic emissions and building energy to urban climate, exposure, and health in cities around the world.

Modelling human activities in a system of cities

Abstract

Cities host most of the world population with diverse services and activities. One key challenge in urban modelling is the quantification of intra- and inter-city mobility patterns and the associated space-time dynamics of population density and anthropogenic activities. To address this, we apply the novel agent-based urban model DAVE (Dynamic Anthropogenic actiVities and feedback to Emissions) to simulate population behaviour and mobility in the Vaud and Geneva Cantons, a system of small- to medium-size cities in Switzerland. Simulation results provide detailed temporal (10 min) and spatial (500 m) population dynamics for different age groups and day types. DAVE further models the time-varying population distribution in 11 different microenvironments (e.g., home, work, leisure, outdoor) and the travel flows by different modes. Simulation results align with observations, confirming the possibility of driving urban system modelling with statistical information on residents' behaviour. Sustainability and health indicators like daily driving distance and walking time for each neighbourhood are also reflected by the model with urban-rural gradients displayed. This work serves as a foundation for future applications of DAVE to study bottom-up human-built environment interactions, from anthropogenic emissions and building energy to urban climate, exposure, and health in cities around the world.
Paper Structure (21 sections, 1 equation, 14 figures, 1 table)

This paper contains 21 sections, 1 equation, 14 figures, 1 table.

Table of Contents

  1. Abstract
  2. Introduction
  3. Materials and methods
  4. Results
  5. Discussion
  6. Conclusion
  7. Acknowledgments
  8. Supplementary Information (SI)

Figures (14)

  • Figure 1: DAVE modules and the main data used in this study. The Behaviour and Transport modules are the focus of this study. The arrows show the main exchanges between modules. Adapted from hertwig_connecting_2025.
  • Figure 2: Adult population (18-65) distribution at different hours and days of the week. (a) Residential population ($P$) in Canton Vaud and Geneva (inset: VD&GE) (b) Adult population difference ($\Delta P$) between Monday 10 am and residential population (c) Population difference between Sunday 4 pm and residential population. (d)–(f) show the same results as (a)–(c), but are zoomed in to the Lausanne region. Gray polygons in (d)-(f) are buildings.
  • Figure 3: Population changes $\Delta P$ in different microenvironments. Population difference ($\Delta P$) between peak or low hours and 4 am (color bar) for adults on a weekday for six MEs: (a) Home (low: 10 am), (b) Work (peak: 10 am), (c) Shop (peak: 5 pm), (d) Indoor Leisure (peak: 6 pm), (e) Outdoor (peak: 6 pm), and (f) Hospitality (peak: 12 pm).
  • Figure 4: Population flows for accessing different microenvironments at commune resolution. Population flows between residence and destination for accessing (a) Indoor Leisure, (b) Outdoor, (c) Work, and (d) Hospitality MEs on different days and times, with intensity of the travel flows (line thickness, colours) and the number of incoming populations (circle size) shown at the geographical centres of the 350 communes of the study region. The flows are shown as straight lines between the origins and destinations instead of the full travel routes.
  • Figure 5: Diurnal ME occupancy profiles for different age group totals. Population of both Cantons in different MEs at 10-min resolution for (a,c,e) adults (age 18-65) and (b,d,f) seniors ($>$65). (a,b) profiles for 'Home', 'Work' and combined remaining MEs ('Other') for both day types; details of remaining MEs for (c,d) weekday and (e,f) weekend days.
  • ...and 9 more figures