A complex challenge
Managing urban areas has become one of the most important development challenges of the 21st century
The Urban Dynamics Educational Simulator (UDES) is a tool designed to help provide skills and tools needed for successful urban planning and design.
It illustrates the complex nature of city interactions, and provides a method for representing them – agent based simulation. It is an excellent example model, accessible to all in the AnyLogic Cloud, with both interactive and customizable elements. Let’s take a closer look.
An agent-based solution
Dr. ir. Gonçalo Homem de Almeida Correia’s recent paper [PDF] introduces the UDES and demonstrates the benefits of agent-based simulation for modeling the dynamic complexity of land use and transportation in an urban environment.
While agent-based models of real metropolitan areas exist, the example model is selectively constrained to enable an understanding of the modeling technique, and also land use and transportation (LUT). The result is a model complex enough to highlight the challenges of real cities but clear enough to be quickly understood.
Agent-based simulation is increasingly the standard approach for modeling dynamic complexity within urban environments. It can capture the components, connections, and interactions necessary for analysis and forecasting.
The principle of agent-based modeling is to observe the emergence of the behavior of the system by characterizing very well the behavior of the agents of the system and the environment in which they interact and move
Related blog post: How AnyLogic can help build superior cities.
The model
The UDES model simulates ten years in one day increments. People and enterprises are agents in the model, with people making choices, such as where they live and how they travel. Enterprises consider other factors, including their location and staffing.
As can be seen on the map, the city is divided into various zones and has a transport network of roads and public transport. The characteristics of a wide variety of elements can be changed, both while the model is running or paused.
Across the city, you can alter a variety of factors, including the cost of transport, the price of fuel, rental costs, and road capacity. Within each zone it is possible to change parameters such as the quality of housing, and the availability of business space.
When you run the model, try changing some of the parameters to see the results of your actions. You can thoroughly explore the statistics while the model is running and after.
How happy are the city’s residents? Can business within a zone be improved, what effect would it have? What about a new road? Try it! Add a new road and see how the city responds.
The future’s bright
After running the model without making any changes, the happiness of the city overall is 75.8%. From my own experimentation, the best result I could achieve was 72.8%. Clearly, I need to sharpen my planning skills.
What’s your best? Post your results below! Find the statistics via the button in the bottom left of the main screen — ‘Click for more aggregated results’.
The model is an excellent demonstration of both the agent-based simulation and AnyLogic Cloud. If you would like to know more about the decision-making and design, you can read the paper [PDF] in full.
⚡ Mobility in the city was the theme of a presentation at the AnyLogic Conference 2018 in Balitmore. Oliver Bandte, PhD, Principal Data Scientest at Boston Consulting Group, detailed the Impact of Ridesharing and Autonomous Vehicles on Mobility in a City. Get the PDF from the conference presentations!
