A Dynamic Traffic Awareness System for Urban Driving

This is the era of Artificial Intelligence (AI) and Internet of Things (IoT). Smart technologies with AI have gained dominance in mobile devices. In recent years, it is paving its way to solve some of the challenging problems in Intelligent Transportation System (ITS). In this paper, we try to answer the following question: How does congestion caused by an unfortunate event on a road segment affect other roads not necessarily close in proximity to the congested road segment? We take advantage of the capabilities of the IoT and propose a dynamic traffic awareness system for urban driving. The system finds all the road points affected by the traffic at some road point at some time, groups them together to predict the effect of traffic on this group of nodes. Grouping the nodes is nothing but clustering since they have similar features, in this case traffic flow. We develop a traffic aware system using IoT technologies and sensors around road points, that dynamically collects and analyzes the traffic flow data to compute the similarity function between road points. We use the concepts from network theory, in particular maximum flow and shortest path algorithms, and a distributed, message passing algorithm to cluster the nodes that is executed continuously to capture up to date information about traffic. We evaluate the system during peak and non-peak hours and against static clustering algorithms and show the performance of our dynamic clustering algorithm.

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