Sumo Based Dynamic Urban Simulation for Intelligence Tra c Management System

Abstract

Increasing world population demands transportation and that re ects huge road tra c in metropolitan cities and consequently needs signi cant research for smooth tra c man- agement system. In this regard, a new Intelligent Tra c Management System (ITMS) was proposed and implemented- where a path from source to destination was selected by Ant Colony algorithm and the roads segment's weight were calculated using Deep- Neuro-Fuzzi cation and C-Means clustering framework on real time weather e ects and road conditions. However, the ITMS is still running on hypothesis and does not have any empirical evidence of working. Therefore, the ITMS needs some comprehensive analysis, other means some simulation or emulation is demanded to proof that the hypothesis is valid. In this thesis, we are going to explore the whole ITMS in realistic fashion with a micro tra c and open source tra c simulation model (SUMO) whics helps to explore tra c related issues including route choice on optimum form. Results highlight that dynamic SUMO model is able to simulate ITMS in a realistic situation, using an origin destination matrix based on road segment weight values and routing algorithms.