Connected cars and data sharing to improve road safety?

By Saumya Jain

Wide scale advancements in technology and data sharing has brought about a change in the decision-making process of many sectors. The healthcare, finance, and entertainment industries all depend on a steady feed of information in order to make business decisions. But data sharing has been very limited when it comes to the auto industry, especially personal vehicles. According to a recent article by Forbes, connected and autonomous vehicles are ready to break this wall and the “wave of attribution” is finally coming to driving behavior.

A recent collaboration between Ford, Uber, and Lyft will share data over a common platform called SharedStreets and aims to improve roadway safety and curbside management. According to the partnership, Ford will develop a universal data standard for real-time curb demand and availability, and Lyft and Uber will provide a data set of vehicle driving speeds as well as produce a universal framework for sharing curbside pick-up/drop-off counts. This partnership promises to give city leaders and policy makers access to readily available local road traffic data that can help them make more informed planning and investment decisions.

Early this month SSTI wrote about the new NCHRP guidebook that outlines a systemic approach to proactively assess crash risk and pedestrian safety. The NCHRP approach is based on predicting crash risk based on pre-crash conditions, driver behavior, and surrounding land use. The information that will be made available through the Ford, Uber, and Lyft partnership can help to greatly improve the accuracy of this predictive model.

Connected Autonomous Vehicles (CAVs) will result in a continuous flow of real-time driver behavior data and information on roadway conditions. This information could take road safety to a whole new level. According to a Forbes article, the benefit of real-time driver-behavior data sharing would be twofold. First, recognizing risky driving in real-time would significantly improve safety enforcement and emergency preparedness. Second, identifying the otherwise life-threatening driver errors that do not result in a fatality and go unnoticed will highlight the level of human error associated with manual or partial automation and would help in rapid adoption of CAVs.

As discussed above, the benefits of data sharing in the auto-industry will be plentiful, and to take advantage of this opportunity planning agencies and decision makers need to be prepared with the right tools before this large pool of information becomes public.

Saumya Jain is a Senior Associate at SSTI.