Each year, millions of people either die or get injured due to road incidents. Thus, integrating safety optimization techniques into future traffic systems is of utmost importance. Emerging connected vehicle technologies have enabled ways to manage traffic networks with optimization goals such as travel time efficiency, fuel efficiency. However, these existing studies have focused less on maximizing traffic safety. Increasing space between vehicles in the road network with an acceptable travel time increase will help to improve the safety of the system. We propose the Platooning Graph, which is capable of modelling the inter-vehicular spacing optimization problem and we provide a fast and readily deployable algorithm to find a good approximate solution. Using microscopic traffic simulations, we demonstrate how the proposed method can improve safety, with minimal impact on travel time.

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With the increasing connectedness of vehicles, real-time spatio-temporal data can be collected from citywide road networks. Innovative data management solutions can process the collected data for the purpose of reducing travel time. However, a majority of the existing solutions have missed the opportunity to better manage the collected data for improving road safety at the network level. We propose an efficient data management framework that uses network-level data to improve road safety for citywide applications. Our framework uses a graph-based data structure to maintain real-time network-level traffic data. Based on the graph, the framework uses a novel technique to generate driving instructions for individual vehicles. By following the instructions, inter-vehicular spacing can be increased, leading to an improvement of road safety. Experimental results show that our framework improves road safety, measured based on the time to collision between vehicles, from the state-of-the-art traffic data management solutions by a large margin while achieving lower travel times compared with the solutions. The framework is also readily deployable for large-scale real-time applications due to its low computation costs.