How to model connected and autonomous vehicles
March 2020: Martin Hartmann explains a quick and convenient way to study assumption-based behavior of autonomous and connected vehicles.
How to generate throughput and demand outputs
Technical Note #62
By Tessa Hayman
September 2021
In traffic modelling, it can be useful to compare the traffic demand and the throughput along a route: how many people would like to make a journey and how many were able to carry out that journey within a set time frame. It is possible to generate this information in Aimsun Next, using the features developed as part of the hybrid macro-meso model. In this technical note, we will describe how to generate both outputs and display them with a view mode so that you can analyse where there are pinch points on your network.
To do this we will compare the outputs of a mesoscopic result and a macro-meso result without a meso area, which will give us the throughput and demand respectively for a particular scenario.
Step 1: Generate throughput outputs
To start with, you need a base simulation to produce the throughput for each section. You can use a calibrated micro, meso or hybrid simulation as these types of network loading approaches have capacity constraint, therefore their output flow represents the throughput of each section. For this example, we will create an incremental mesoscopic DUE result.
We also need to set the outputs to include a path assignment object, so that we can load the same OD matrices on the same paths, but without capacity constraints, to get the demand for each section.
Once this has been set up with your preferred parameters in the experiment. You can run the result.
Step 2: Path assignment plan
You now need to create a path assignment plan, a feature introduced in Aimsun Next 20, so that you can use as input the path assignment that the base simulation just produced. A path assignment plan is similar to the traffic demand or master control plan object in that it can contain a set of path assignments which could change over time. For this case, we will just use one path assignment in the path assignment plan. Make sure the path assignment plan time aligns with your modelled period including any warmup.
Step 3: Generate demand outputs
To generate the demand outputs, we will use the macro network loading part of the hybrid macro-meso model. For the hybrid model, the macroscopic area is modelled using individual vehicles which are generated dynamically with the travel time calculated using delay functions without capacity restraint, flow metering or blocking back. This means that we can see how the demand at a particular junction changes over time, given the assumption that all vehicles can complete their journey.
It is possible to run a hybrid macro-meso without specifying a meso area. In doing this, if the travel time computation doesn’t introduce any delay (instantaneous or free flow travel time), the output flow represents a demand, as there is no capacity restraint anywhere in the network.
Start off by creating a new dynamic scenario with the same demand, control plan and public transport plan as the Throughput Scenario. In addition to this, add the path assignment plan that we created in step 2.
Then for this scenario, create a new hybrid macro-meso experiment which uses stochastic route choice.
We will use stochastic route choice for the assignment so that we can specify that 100% of the routes follow the path assignment generated by the dynamic user equilibrium result from step 1.
For the experiment, specify the same warm up, attribute overrides, arrivals and traffic management that you had in step 1. In the dynamic traffic assignment tab, check that 100% of the vehicles are following the Input Path Assignment.
To get the demand output, we must set the macro travel time to either be instantaneous or free flow travel time. For this example, we will use instantaneous travel time. This is set in the hybrid tab of your experiment.
NB: If you want to use free flow travel time for a more accurate dynamic demand, you must set your VDF and TPF to be free flow travel time for all your sections and turns and keep this box unticked and instead select Total VDF/TPF/JDF cost.
Now you need to create a replication for this experiment. Make sure that your random seed is the same as your result in step 1 so that the arrivals and traffic demand generated are the same.
Run your replication.
Step 4: Compare
You can now compare the demand and throughput on any link by using the time series viewer. You should see in most models that the demand has a more triangular shape than the throughput.
You can also view this using a view mode generated by the data comparison tool.
Go to Data Analysis > Data Comparison and select the throughput and demand results and the section counts.
Click compare. This creates a table of results, a scatter graph and a view mode. You can now see in the view where the throughput or demand is higher. For most models, you should see that the demand is higher in the congested period of the modelled period and then as the congestion dissipates, the throughput will exceed the demand.
NB: Once you create a comparison, you can use the comparison to make other view modes or view styles; for instance, to show the difference between demand and throughput as a label.
More technical notes
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