1. Propagating agents with macroscopic dynamic network loading
Challenges and possible solutions
ir. Jeroen van der Gun
dr.ir. Adam Pel
prof.dr.ir. Bart van Arem

2. Dynamic network loading (1)
Simulating traffic propagation over time
Uses graph of links and nodes
Needs to know amount and routing of traffic
Time

3. Dynamic network loading (2)
Microscopic (agent-based)
Vehicles are agents
Traffic flow emerges from their interaction, including complex phenomena like hysteresis
Macroscopic (aggregate)
Parameters typically more easily observable
Shorter computation time (lower complexity)
Getting better at reproducing complex traffic phenomena

4. Agent-based demand Activity-based modelling Sampled choice behavior
Agents can adapt their initial plans to disruptions and emergencies
Sampled choice behavior
Agents can retain consistent attitudes over time
Multimodal transportation systems
Passenger agents can board and alight public transport vehicles, which are also agents

5. ? Four combinations Aggregate demand Agent-based demand
Macroscopic network loading
Aggregate demand is fed directly into the macroscopic network loading model ?
Microscopic network loading
Aggregate demand can be split into discrete vehicle agents
Decision-making agents map one-to-one to vehicle agents
Aggregating agent-based demand precludes agent-level decision-making while en-route

6. Contours of a solution (1)
Macroscopic dynamic network loading models have multi-commodity formulations
Can be used to distinguish individual agents in the macroscopic flow of traffic

7. Contours of a solution (2)
Individual agents also need an unambiguous location
Agent location is defined as the “front” of its vehicle
“Tail” of its vehicle must follow this “front”
I always have an intended next turn too

8. Pick a suitable network loading model

9. Link modelling challenges
Avoid systematic errors
Adherence to LWR varies across models
Limit numerical diffusion
Variational methods perform best due to fewer discretizations
Prevent agents from traversing links faster than the free speed
Problem for models with discrete time unless the time within a time step is explicitly considered
Accept agents to flow into a link
Computational burden or accuracy problems for models with discrete vehicle count if the number of agents is large

10. Turning fraction challenges
Specify the ordering of vehicles on a link
Need a strict weak order of vehicle parts to avoid highly fluctuating discrete turning fractions
Otherwise, e.g. an entire motorway is blocked while one vehicle is moving into an off-ramp
For models with discrete vehicle counts this implies individual lanes need to be considered
Choose a node model of an appropriate form
Incremental formulations have less vehicle diffusion than squeezing formulations
Prevent “agent-based gridlock”
The “tails” of agents must not be able to block each other

11. preserving agent-based en-route choices
Conclusions
Aggregate demand
Agent-based demand
Macroscopic network loading
You can go here
preserving agent-based en-route choices
but be careful
Microscopic network loading
Discrete vehicle count may not make things easier
Link Transmission Model appears reasonably able to deal with the challenges