@article{ref1,
title="A probabilistic approach to driver assistance for delay reduction at congested highway lane drops",
journal="International journal of transportation science and technology",
year="2021",
author="Mehr, Goodarz and Eskandarian, Azim",
volume="10",
number="4",
pages="353-365",
abstract="This 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.<p /> <p>Language: en</p>",
language="en",
issn="2046-0430",
doi="10.1016/j.ijtst.2020.10.002",
url="http://dx.doi.org/10.1016/j.ijtst.2020.10.002"
}