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.
Highlights
A new intelligence traffic management system (ITMS) [8] [11] [20] [21] [22] [30] was proposed and implemented to find an optimum route from source to destination
Simulation of Urban Mobility (SUMO) is not initially designed for simulating automated vehicles, and we present an interface for exchanging the road weights generated by the DeepNeuro-Fuzzy software model between ITMS to SUMO environment
ITMS is successfully implemented in SUMO with traffic related issues including route choice, traffic light simulation or vehicular communication and etc
Summary
A new intelligence traffic management system (ITMS) [8] [11] [20] [21] [22] [30] was proposed and implemented to find an optimum route from source to destination. We are proposing a traffic simulation environment by integrating Sumo Urban Mobility Simulation (SUMO) tool and a realtime Intelligent Traffic Management System (ITMS) [8] [11] [20] [21] [22] Such simulations are expected to offer the prediction and on-the-fly feedback for better decision-making on complex traffic managerial issues and corporate them to the end-user applications. ACO model is analyzed by changing internal parameters including pheromone density, pheromone trail, visibility, and optimum setting for control parameters (α, β) to route more vehicles from source to destination within a certain period. The results highlight that the ACO model performs better than other existing models
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