A Probabilistic Approach to Driver Assistance for Delay Reduction at Congested Highway Lane Drops
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. The prediction model estimates the probability of reaching a goal state on the road using one or multiple lane changes. This estimate is based on several traffic-related parameters such as the distribution of inter-vehicle headway distances, as well as driver-related parameters like lane change duration. For an upcoming lane drop, the advance warning system uses the model and vehicle conditions at the moment to continuously estimate the probability of successfully changing lanes under those conditions before reaching the lane end, and advises the driver or autonomous vehicle to change lanes when that probability dips below a certain threshold. In a 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 show that the proposed system can reduce average delay up to 50% and maximum delay up to 33%, depending on traffic flow and the ratio of vehicles equipped with the advance warning system.
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