Traffic Estimation and Navigation by Pervasive Computing


Vehicular traffic estimation is one of the major issues in intelligent transportation systems. Traffic information can be used by drivers and navigation systems to improve travel efficiency. Current traffic estimation systems rely on infrastructure deployment to monitor traffic state. Therefore, they are costly to implement. In this project, we use new technology of context-aware computing for estimating traffic state. A dynamic traffic estimation and context-aware navigation system is proposed, which is composed of three main modules: local traffic congestion estimation module, global traffic congestion estimation module, and navigation module. In the proposed system, vehicles estimate local traffic congestion level using vehicular contextual information including speed and acceleration by relying on fuzzy logic, and send it to the central server. Central server estimates global traffic congestion level by integrating the local traffic information received from different vehicles. Using this global traffic information, it provides a traffic-aware navigation service to help drivers.


Presentation: PDF (English/Persian) Source code/Executable file/Additional files