期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：10
页码：277-284
出版社：Science and Information Society (SAI)
摘要：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 decisionmaking
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.
