Agent-Based Optimizing Match Between Passenger Demand and Service Supply for Urban Rail Transit Network With NetLogo

Both passenger demand and service supply are among the most important factors that determine performance of urban rail transit system. It is not easy to find out optimal solution for match between with traditional methods, due complexity combinatorial intelligent - matching problem. In order get comprehensively degree, this paper transforms multi-criteria problem into distributed artificial intelligence optimization by using multi-agent dynamic interaction technique. On side, traffic agents modelled from perspective boundedly rational travel decision. train agent modelled. The headway time designated as main decision variable, key link in different time-of-day intervals. To make more closely matched network system at reasonable cost operational cost, calculation formula degree proposed, along architecture agent-based mechanism, negotiation-based iterative mechanisms balancing. proposed methods validated on simulation platform NetLogo. results emphasize importance representing side jointly/interactively. These findings meaningful policies both development efficient capacity usage strategies provision high level passengers.