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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Modeling Ant Colony Optimization for Multi-Agent based Intelligent Transportation System
Digital Object Identifier (DOI) : 10.14569/IJACSA.2019.0101039
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 10, 2019.
Abstract: This paper focuses on Sumo Urban Mobility Simulation (SUMO) and real-time Traffic Management System (TMS) simulation for evaluation, management, and design of Intelligent Transportation Systems (ITS). Such simulations are expected to offer the prediction and on-the-fly feedback for better decision-making. In these regards, a new Intelligent Traffic Management System (ITMS) was proposed and implemented - where a path from source to destination was selected by Dijkstra algorithm, and the road segment weights were calculated using real-time analyses (Deep-Neuro-Fuzzy framework) of data collected from infrastructure systems, mobile, distributed technologies, and socially-build systems. We aim to simulate the ITMS in pragmatic style with micro traffic, open-source traffic simulation model (SUMO), and discuss the challenges related to modeling and simulation for ITMS. Also, we expose a new model- Ant Colony Optimization (ACO) in SUMO tool to support a multi-agent-based collaborative decision-making environment for ITMS. Beside we evaluate ACO model performance with exiting built-in optimum route-finding SUMO models (Contraction Hierarchies Wrapper) -CHWrapper, A-star (A*), and Dijkstra) for optimum route choice. The results highlight that ACO performs better than other algorithms.
Keywords: Intelligent Traffic Management System (ITMS); Simulation of Urban Mobility (SUMO); traffic simulation; Contraction Hierarchies Wrapper (CHWrapper); Dijkstra; A-star (A*); Deep-Neuro-Fuzzy Classification
Shamim Akhter, Md. Nurul Ahsan and Shah Jafor Sadeek Quaderi, “Modeling Ant Colony Optimization for Multi-Agent based Intelligent Transportation System” International Journal of Advanced Computer Science and Applications(IJACSA), 10(10), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101039
@article{Akhter2019,
title = {Modeling Ant Colony Optimization for Multi-Agent based Intelligent Transportation System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101039},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101039},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {10},
author = {Shamim Akhter and Md. Nurul Ahsan and Shah Jafor Sadeek Quaderi}
}
