Connected Vehicle-Based Advanced Detection of “Slow-Down” Events on Freeways

From the perspective of an individual vehicle, the prediction of a “slow-down” or shockwave event on a freeway can help the driver reduce potential collision risks, enhance the driving experience, and reduce the cost of energy consumption and vehicle maintenance. From the perspective of traffic management, shockwave prediction may help regulate traffic flow effectively and allow for the response to (non-recurrent) incidents in a timely manner. In this paper, two real-time prediction algorithms are proposed and investigated, which are based on the high-resolution information provided from a set of connected vehicles within the communication range of the host vehicle. Both methods are able to predict the “slow-down” event under high traffic density at 3.51 seconds (on average) earlier than its occurrence. Both algorithm performances degrade with the decrease of the traffic density and penetration rate of the connected vehicles.