Emergent intelligence technique-based transport depot resource management in a metropolitan area

Abstract

A metropolitan area experiences transport resource scarcities and dynamic resource allocation problems due to huge number of commuters’ population, dynamic commuters’ arrival rates, etc. The progress of these problems results into economic loss, traffic congestion, inefficient utilization of resources and increase in indefinite waiting time of commuters. Hence, we propose a dynamic transport resource allocation scheme. The scheme efficiently manages number of vehicles over time and space using emergent intelligence (EI) technique. Based on the historical information, commuters’ arrival rates, resource availability, deficit resources, surplus resources of neighbor depots, etc., the EI technique with agents is used for collecting, analyzing, sharing and allocating resources, for optimal utilization of resources in a metropolitan area. Analytical model of EI technique-based transport resource allocation scheme is developed using Markov chain, and derived the closed-form expression of optimal utilization of resources. Performance evaluation is carried out with varying number of commuters’ arrival rates, vehicles, requests and number of neighborhood depots. We observe that results obtained in the simulation and analysis reflect the effectiveness of the proposed scheme in a metropolitan area.