Multiagent Self-organization for a Taxi Dispatch System

The taxi dispatch problem involves assigning taxis to callers waiting at different locations. A dispatch system currently in use by a major taxi company divides the city (in which the system operates) into regional dispatch areas. Each area has fixed designated adjacent areas hand-coded by human experts. When a local area does not have vacant cabs, the system chooses an adjacent area to search. However, such fixed, hand-coded adjacency of areas is not always a good indicator because it does not take into consideration frequent changes in traffic patterns and road structure. This causes dispatch officials to override the system by manually enforcing movement on taxis. In this dissertation, I apply a multiagent self-organization technique to this problem, dynamically modifying the adjacency of dispatch areas. We compare performance with actual data from, and a simulation of, an operational dispatch system. The proposed technique decreases the total waiting time by up to 25% in comparison with the real system and increases taxi utilization by 20% in comparison with results of the simulation without self-organization. Interestingly, we also discover that human intervention (by either the taxi-dispatch officials or the taxi drivers) to manually overcome the limitations of the existing dispatch system can be counterproductive when used with a self-organizing system.