Traffic Congestion

Eliminating traffic congestions is a top priority for local governments as they aim to reduce idle time spent on city roads. Constructing additional roads or widening existing ones is a common approach by policy makers, which are tasked with choosing the most worthwhile investment. In order to asses the benefits of a road infrastructure project, engineers must take into account traffic flow patterns, drivers habits and a multitude of other factors. We represent traffic networks as graphs and analyse them using techniques from graph theory. This method can be used in order to determine the best infrastructure investment. As an example let us look at a planned underground road that is supposed to reduce congestion in Skopje. We automatically process data from OpenStreetMap to get a graph representation of the traffic network in Skopje (Fig 1).

A screen-shot of a small part of Skopje's road map. Using information from OpenStreetMap we can determine junction positions and size of roads which we translate to nodes and weighted edges respectively.

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A screen-shot of a small part of Skopje's road map. Using information from OpenStreetMap we can determine junction positions and size of roads which we translate to nodes and weighted edges respectively.

Then we added an additional link that represents the planned underground road (Fig. 2).

Figure 2. Planned underground road in the city centre of Skopje.

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Figure 2. Planned underground road in the city centre of Skopje.

We can compare the characteristics of both network (with and without the underground road) by calculating local or global metrics. As a demonstration, we calculated average betweenness for both networks. Network betweenness increases significantly when the underground road is added from 0.20351 to 0.20434. Although this number may seem small, please bear in mind that this is a single road in a city of over 20 000 other roads. The increased betweenness suggest possible reduction in traffic congestion if this project is realised and the road is built. Of course, the betweenness metric is just an example and a real analysis would require additional tools and metrics. Overall with this project we aim to help policy makers and steer the traffic network evolution in the right direction.