The increasing number of fuel-based vehicles has several negative impacts on the environment, the economy and citizen’s daily life. In fact, the transportation sector is, by far, the largest contributor to Green-House Gas (GHG) emissions, being traffic congestion one of the biggest concerns in all-size cities worldwide. Actually, more and more cities are deploying ICT-based infrastructures to monitor the traffic and its environmental impact (air pollution, noise, etc.).

In this line, this Master Thesis develops the SmartTLC software framework, aimed at enabling the simulation and comparison of different traffic light adaptive control algorithms based on contextual data (either historical, real-time or both). This framework allows designers to select the best traffic light control strategy for different situations, showing which one achieves better results in terms of reducing traffic congestion. The experimental results obtained so far demonstrate that the adoption of a context-aware adaptive approach significantly improves traffic fluidity, reducing vehicle waiting time, in particular, in roads with a higher traffic demand.

Keywords

• Adaptive Traffic Light Control • Context-Awareness • Traffic Congestion • Smart Cities • Intelligent Transport System (ITS) •