Quasi-real time estimation of turning movement spillover events based on partial connected vehicle data

Abstract

Turning movement spillover (TMS) is the result of a turning bay section (TBS) not being able to accommodate all arriving vehicles, so that the turning-vehicle queue spills back and blocks other vehicles turning in different directions. We are not aware of any TMS estimation method that can remedy this situation or support relevant applications in real time. This research proposes a quasi-real time algorithm for estimating TMS, which includes triggering movement as well as duration estimation. The proposed method is based on data for connected vehicles (CVs), including their trajectories and their desired turning directions. In addition, a model that uses partial trajectory data is proposed. For each assumed TMS, a “simplified trajectory” is developed by the construction of a piece-wise linear curve. To minimize any deviation of the simplified trajectory from observation, a TMS estimation can be made. This proposed method is effective and computationally efficient when tested against dynamic demand in two mainstream signal phase settings, with varied sample sizes. Even though data for a higher number of vehicle samples is generally favorable, the proposed model still makes a good estimate when only one trajectory is available.