We’ll build on the previous post’s complex traffic problem and introduce another representation technique.
Consider road capacity in the traffic example. Road capacity is a state or condition of the system. At any given time there is a certain capacity, which could be measured in units of lane-kilometres. Selection of the right units is very important to capture the underlying behaviour. This capacity is the result of all the roads and expansions that have been built, and all the roads that have been replaced or taken out of service. We can show this as:
In our model, what we are saying is that the road capacity is an accumulation, or sum, of all the roads (or lane-km) made available per year and all those made unavailable. This gives us a starting point to explicitly ask what are all the important effects on Roads Made Available (such as construction) and Roads Made Unavailable (which in Calgary might be bike lanes).
These accumulations are called stocks, and their inputs and outputs are called flows.
The clouds mean that the roads “come from” somewhere outside the area of interest for the model, and “go to” somewhere outside the model. If the model was expanded to include the lifecycle of roads, it might have additional stocks such as planned capacity, approved capacity, under construction capacity, capacity lost to bike lanes, etc.
Another variable we had identified in the causal loop diagram is the Traffic Volume. The unit for Traffic Volume might be average weekly peak vehicles for simplicity. We’d have to ensure our subject matter experts selected the right measurements.
The flows for this stock are visualized a bit differently. Instead of separating the inflow and the outflow, a single element called Net Change in Vehicles per Year has been set up to show the combination of the two effects. This simplification is useful for some stocks where the same factors affect both inflow and outflow, where we don’t plan to drill down on details, or to reduce clutter in the model.
The last main variable we had identified was Attractiveness of Driving, which we will show in a similar manner.
Attractiveness of Driving might be more difficult to measure, requiring for example surveys of discretionary trips made. In this model we could just call the unit attractiveness.
Connecting the Stocks
The last step is to connect these three stocks: Road Capacity, Traffic Volume, Attractiveness of Driving. We’ll add the connections in two steps.
Just as in the CLD:
- more Road Capacity means less Average Travel Time
- more Travel Time compared to Desired Average Travel Time means more Pressure to Add Roads
- more Pressure to Add Roads means more Annual Road Construction
- more Annual Road Construction contributes to Roads Made Available per Year
As before, the reverse needs to be true for all statements.
Travel time seemed to be central to the causal loop diagram. Thinking through the nature of travel time, it is a function of traffic volume and road capacity (assuming the same route, speed limit, no accidents, etc.). Here is the completed model.
Talking through the added connections:
- more Traffic Volume means increased Average Travel Time
- increased Average Travel Time makes a negative contribution toward Net Change in Attractiveness per Year
- increased Attractiveness of Driving makes a positive contribution toward Net Change in Vehicles per Year
The intent of going through this exercise of building and reading a relatively simple model is to develop your ability to read a simple stock and flow diagram in preparation for reading models for your domains, not to be able to create models yourself.
An Introduction to Representing Complex Problems
Chapter 2 Another Representation: Stocks and Flows
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