Bio-inspired redistribution of urban traffic flow using a social network approach

In present days, the road network in any major city faces the constant pressure of accommodating an ever increasing number of vehicles while conserving a congestion-free status. However, identifying key intersections that will soon become congested is a difficult task, performed by tedious, thorough simulations; even more difficult is to adapt the road network so as to increase its efficiency and avoid congestion. We argue upon the social component of road traffic and propose an alternative way to detect hotspots leading to congestion by using techniques borrowed from complex network analysis. We will use the betweenness centrality and argue upon its power-law distribution, which we set out to redistribute and equalize. The paper introduces a genetic algorithm that redistributes the betweenness values at a community level in a city by changing street directions and number of available lanes in order to reduce (and possibly even eliminate) congestion hotspots. Experimental results in terms of reducing traffic loads from hotspots and transferring to neighboring streets yield an improvement with a factor of up to 90% times without adding significant costs or modifying the existing infrastructure.