Using DSRC Road-Side Unit Data to Derive Braking Behavior

With the increasing deployment of Connected Vehicle Technology (via DSRC/CV2X), public traffic authorities are presented with a potential treasure trove of valuable data for analysis. However, several practical limitations exist that pose unique challenges in this publicly-collected data such as lack of vehicle re-identification due to privacy measures, sparsity of data, limited range of transmission, noise in recorded trajectories etc. In this paper, we analyze trajectories for braking behaviors of Connected Vehicle Road-side Unit (RSU) data. The dataset consists of trajectories collected from a dense urban grid consisting of 25 intersections along 4 high-volume arterials, for a period of 1 year. We begin by providing a brief description of the data collection and processing modalities. We then present a tool to perform exploratory analytics on the data with a focus on anomalous trajectories with hard-braking events. We show the benefits of such a tool for public traffic authorities to gain insights into the performance and safety aspects of urban arterials, and to guide policy decisions.