Swarm Intelligence Technique for Capacity Optimization of a Transportation Network

Today artificial intelligence systems support efficient management in different fields of social activities. In particular, congestion control in modern networks seems to be impossible without proper mathematical models of traffic flow assignment. Thus, the network design problem can be referred to as Stackelberg game with independent lower-level drivers acting in a non-cooperative manner to minimize individual costs. In turn, upper-level decision-maker seeks to minimize overall travel time in the network by investing in its capacity. Hence, the decision-maker faces the challenge with a hierarchical structure which solution important due to its influence on the sustainable development of modern large cities. However, well-known that a bilevel programming problem is often strongly NP-hard, while the hierarchical optimization structure of a bilevel problem raises such difficulties as non-convexity and disconnectedness. The present paper is devoted to the swarm intelligence technique for capacity optimization of a transportation network. To this end, we develop the bilevel evolutionary algorithm based on swarm intelligence to cope with the continuous transportation network design problem. The findings of the paper give fresh insights to transport engineers and algorithm developers dealing with network design.