On a typical workday morning in any major city, thousands of drivers open navigation apps looking for the shortest and fastest route to their destination, following the blue line that promises to save time. But the problem begins when thousands of drivers make the same decision at the same time.
A road that appears empty at one moment may quickly become full, and a street designed for limited local traffic may turn into a lane for cars, while traffic congestion moves from a main axis to a side intersection or residential neighborhood. cause congestion, as recent studies show that their impact varies depending on the structure of the road network, the intensity of demand, the percentage of users who follow their recommendations, and the way the routing algorithm itself is designed.
But these studies reveal a more complex aspect that goes beyond simply finding the fastest route: the actual difference between choosing the optimal route for a single car and choosing the best route for an entire city.
Navigation apps are no longer just maps that determine location and direction, but also attempt to estimate arrival time, read road conditions, predict traffic development over the next few minutes, and choose between a large number of possible routes.
The Google Maps app combines current traffic data, historical patterns, various road conditions, and machine learning models to predict road conditions. It may also utilize official data and information reported by users, in addition to the characteristics of the road itself.
These applications operate according to specific data, algorithms, and routing options. Information sources may overlap between services, but it is not a unified database shared by all applications.
When a group of drivers request a route to the same destination at the same time, the algorithm can find the route that appears to be the least expensive at that moment, but it deals with the request from the perspective of the trip it is directing, not from the perspective of the effects on the rest of the road network.
An individual decision turns into a collective problem
The concept of self-direction describes the situation in which each driver chooses the route that minimizes their personal cost in a busy road network. This concept expresses a mathematical decision-making method, where each driver tries to optimize their journey.
Transportation engineering distinguishes between the optimal choice for the individual and the optimal choice for the city as a whole. The former describes the situation in which each driver selfishly chooses their route to achieve the shortest possible journey time for themselves, while the latter describes the situation in which flows are distributed in a way that minimizes the total journey time for all cars in the city as a whole.
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