A smart traffic management system has been developed to enhance emergency response by facilitating the movement of emergency vehicles through busy city intersections. This system is demonstrated through a simulation using a map interface that models the movement of vehicles and traffic signals along a predefined route in San Francisco, utilizing the Mapbox API and PubNub's Real-time Network for real-time communication. The system's architecture includes a cloud-based Traffic Management Server (TMS) on IBM Bluemix, which remotely controls traffic signals based on data from emergency vehicles, optimizing their route by calculating distance and bearing to the next approaching signal. The project, implemented as a client web application and server using Python and PubNub services, aims to alleviate traffic congestion for emergency vehicles, although it acknowledges limitations in handling complex real-world scenarios. Future enhancements could involve integrating IBM Watson to analyze historical traffic patterns for increased accuracy in predicting and managing traffic flows. The full source code is available on GitHub, and instructions for hosting the demonstration are provided within the project's documentation.