Mehr, GoodarzEskandarian, Azim2022-03-222022-03-222021-12-012046-0430http://hdl.handle.net/10919/109412This paper proposes an onboard advance warning system based on a probabilistic prediction model that advises vehicles on when to change lanes for an upcoming lane drop. Using several traffic- and driver-related parameters such as the distribution of inter-vehicle headway distances, the prediction model calculates the likelihood of utilizing one or multiple lane changes to successfully reach a target position on the road. When approaching a lane drop, the onboard system projects current vehicle conditions into the future and uses the model to continuously estimate the success probability of changing lanes before reaching the lane-end, and advises the driver or autonomous vehicle to start a lane changing maneuver when that probability drops below a certain threshold. In a simulation case study, the proposed system was used on a segment of the I-81 interstate highway with two lane drops – transitioning from four lanes to two lanes – to advise vehicles on avoiding the lane drops. The results indicate that the proposed system can reduce average delay by up to 50% and maximum delay by up to 33%, depending on traffic flow and the ratio of vehicles equipped with the advance warning system.Pages 353-365application/pdfenCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationaleess.SYcs.SYArticle - Refereed2022-03-22https://doi.org/10.1016/j.ijtst.2020.10.002104Eskandarian, Azim [0000-0002-4117-7692]2046-0449