Self-adaptive service driven architecture for intelligent transport system

Modern transport systems rely on an increasing number of sensors to control their operation including orientation, speed, arrival and departure times, fuel consumption, passenger count etc. These have become important parts of the echo system with their availability and performance playing a key role in daily commuting. However, they produce data in high volume and frequency that need to be processed fast enough so that it can be shared across the network in real-time. Traditional system architectures have been unable to meet transport systems quality requirements scuh as scalability, adaptability, sustainability, extendibility and high availability. Thus, in this work, we propose a novel architecture to acquire, store, manipulate and integrate information from heterogeneous data sources to produce a reliable prediction. It will help transport managers and bus companies to optimize, in real-time, scheduling, stops usage and passenger‘s time.