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Attaining efficient road traffic through optimizing travel time and energy consumption without compromising safety is a key goal in transport telematics. Lane-changes and merging have a vital role in modelling on-ramp and off-ramp bottlenecks. Near on-ramps and off-ramps, they are often major factors to cause safety hazards and traffic breakdowns. In this paper, we address the problem of improving/optimizing merging time at on-ramps and thus reducing the merging bottlenecks. In order to merge two streams of communicationand sensor-enabled vehicles, we define a merging problem and propose a proactive optimal merging strategy that dissociates the point of decision-making from the actual merging point. Our algorithm computes the optimal merging order for the group of vehicles of the two streams. Our optimal merging algorithm for on-ramp vehicles (single-lane scenario) outperforms an efficient, previously suggested strategy as well as the conventional mainstream priority merging in terms of merging time and rate, waiting time, energy consumption, flow and average velocity (especially at the point after the initial merging point) at the cost of slightly increased average trip time for the mainstream vehicles compared to the conventional merging. We also highlight important directions for further research.

Identification of these boundaries can help to determine the loss of neuroretinal cells or layers and the presence of retinal pathology, which can be used as features for the automatic determination of the stages of retinal diseases.