Agent-Based Analysis for Smart Traffic Management

Traffic management has become a crucial problem in many developing countries, especially in small, growing urban areas like Dehradun, where the traditional traffic signal management system does not effectively work. This work proposes a framework for building smart traffic management solutions using agents, simulators, geospatial and remote sensing tools and methodologies to model and simulate traffic flow in large urban environments. Satellite images and kernel density were employed to examine traffic flow. Consider the vehicle congestion detected on road segments from a satellite; the zones were created by classifying and grouping them based on traffic intensity. Traffic agents were created and described with parameters such as speed, acceleration, lanes, and traffic lights so that they reconstruct actual conditions as closely as possible. Using the agent-based approach, it is possible to adapt traffic flows and provide valuable information about the methods of traffic signalization and client vehicle behavior patterns. Some of the preliminary outcomes demonstrate the predicted and computed congestion patterns to determine how the whole traffic system responds. This combination of methodologies aids the optimization of such systems by making transportation decisions more intelligent and, hence, creating sustainable transportation networks for city planners and traffic authorities. The defined framework can be implemented in other urban settings and can be viewed as a scalable solution for ITS.