Real-Time Traffic Incidence dataset

This paper focuses on developing (i) a benchmark dataset for identification of traffic incidences, (ii) a congestion aware navigation application which uses this dataset for real-time detection and classification of traffic incidents, (iii) the System Level Software (or Middleware) required for Distributed Computing in such a system with Rapid Mobility, and (iv) a hardware prototype of the distributed computing and storage infrastructure. The video bandwidth requirement of 10-100 GigaBytes of data per minute per vehicular camera makes it a Big Data problem. With millions of smart vehicles predicted to be deployed within the next 5 years, BigData from a single vehicle, multiplied with the large number of vehicles, presents a Big-Squared-Data computing space which will easily overwhelm any Cloud infrastructure with its Real-Time or near Real-Time demands. Hence the need for a Fog tier between the Edge nodes and the Cloud to bring distributed computation (servers) and storage closer to the Edge nodes. Such a Fog consists of multiple Fog instances, each one of which services cells or Virtual Clusters of Edge nodes. Results show that Fog-Cloud computing framework outperforms a Cloud-only platform by 55.8% reduction in total latency or response time.