10.3. All-or-nothing assignment

In this method the trips from any origin zone to any destination zone are loaded onto a single, minimum cost, path between them.

The All-or-nothing assignment (AON) method focuses on assigning all trips from a given starting point (origin) to a specific endpoint (destination) to just one route that has the lowest possible travel cost. This means that if there are multiple paths available, only the path with the least cost will be chosen, without accounting for alternatives that may offer similar cost benefits.

Imagine you're going from home to a friend’s house and you can take either a scenic route or a highway. If you always choose the highway because it's generally faster, that's like the AON method — you ignore the scenic route entirely even if it might take about the same time due to less traffic.

This model is unrealistic as only one path between every O-D pair is utilised even if there is another path with the same or nearly same travel cost.

One major limitation of the All-or-nothing assignment is its unrealistic nature. The model assumes that all trips use only a single path without recognizing the existence of alternative routes that may have similar costs or travel times. This can lead to oversimplification, especially in networks where multiple viable paths exist.

Think of a delivery driver who only uses one route to complete their deliveries, completely ignoring other paths that could save time or fuel. This rigid approach could lead to longer delivery times when alternative routes may be more efficient.

However, this model may be reasonable in sparse and uncongested networks where there are few alternative routes and they have a large difference in travel cost.

Despite its limitations, the AON model can be useful in less congested traffic environments where there are minimal routes. In these scenarios, drivers typically don’t have many options, making it reasonable to assign all traffic to the most cost-effective path, as other routes may be significantly less advantageous.

Imagine driving through a rural area with only a couple of roads. In this case, if one road is much faster than the other, it makes sense for all vehicles to choose that road. The lack of other options justifies the AON approach in this context.

This model may also be used to identify the desired path: the path which the drivers would like to travel in the absence of congestion.

The All-or-nothing assignment can help identify the ideal route drivers would choose if there were no obstacles or traffic jams. This helps urban planners understand the paths drivers prefer, which can guide future transport planning and development.

Consider a busy weekend at a popular amusement park. If there were no lines at all, everyone would likely choose the most direct and fastest path to the rides. Knowing what that ideal path looks like can assist park managers in planning better flow patterns for visitors.

In fact, this model’s most important practical application is that it acts as a building block for other types of assignment techniques.

The All-or-nothing method serves as a foundational concept in traffic assignment modeling. It's often the starting point for more complex models that account for factors like congestion or varying travel costs. Understanding AON is crucial for grasping more intricate traffic assignment methodologies.

Just like learning the rules of basic math is essential before tackling complex equations, understanding the AON assignment lays the groundwork for studying advanced traffic modeling techniques.

To demonstrate how this assignment works, an example network is considered. This network has two nodes having two paths as links.

An example is presented where there are two connections (nodes) with two routes available. The travel times of these routes are constant, illustrating how all traffic moves to the faster link, which in this case is the first link. By assigning all flows to this link, we can see the simplicity of the AON model in action.

Think of a shopping mall with two entrances but only one of them has a direct pathway to the main store. When a crowd arrives, everyone naturally heads to the entrance with the easy access, just like how traffic flows to the more efficient path in our AON example.

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